The Complete Deep Learning & Computer Vision Course in 2021

  • Course provided by Udemy
  • Study type: Online
  • Starts: Anytime
  • Price: See latest price on Udemy
Udemy

Course Description

This Brand New and Modern Deep Learning & Computer Vision Course will teach you everything you will need to know to become a Computer Vision Expert.


Deep Learning & Computer Vision is currently one of the most increasing fields of Artificial Intelligence and Companies like Google, Apple,

Facebook, Amazon are highly investing in this field. Deep Learning & Computer Vision jobs are increasing day by day & provide some of the highest paying jobs all over the world.



If We Want Machines to Think, We Need to Teach Them to See.-Fei Fei Li, Director of Stanford AI Lab and Stanford Vision Lab



Computer Vision allows us to see the world & process digital images & videos to extract useful information to do a certain task from classification, object detection, and much more. Python is one of the most popular used programming language in Deep Learning and Computer Vision.



All the tools, techniques & technologies used in this course -

  1. Learning Computer Vision & Deep Learning Fundamentals

  2. Setting up Anaconda, Installing Libraries & Jupyter Notebook

  3. Learning fundamentals of OpenCV & Numpy - Reading images, Colorspaces, Drawing & Callbacks

  4. Advanced OpenCV - Image Preprocessing, Geometrical transformations, Perspective transformations & affine transformations, image blending & pyramids, image gradients & thresholding, Canny Edge Detector and contours

  5. Working with videos in OpenCV - Using webcam, Haar Cascades & Object Detection, Lane Detection

  6. Deep Learning & How Neural Network Works? - Artificial neural networks, Convolution Neural Networks & Transfer Learning



Image Classification - Plant leaf Classification

  • Working on very recent Kaggle Competitions

  • Using Google Colab & Kaggle Kernels

  • Using the latest Tensorflow 2.0 & Keras

  • Using Keras Data Generators & Data Argumentation

  • Using Transfer Learning & Ensemble learning

  • Using State of The Art Deep Learning Models

  • Using GPU & TPU for Model Training

  • Hyperparameter Tuning

  • Using Weights & Biases for recording Deep Learning experimentations

  • Saving & Loading Models

  • Creating a Weights & Biases Report & Showcasing the Project!


Object Detection - Wheat heads Detection

  • Working on Kaggle Competitions, again!

  • Using Facebook's Detectron2 for Object Detection

  • Creating COCO Dataset from scratch

  • Training Faster RCNN Model and Custom Weights & Biases callback

  • Using Retinanet

  • Saving & Loading Detectron2 models


Generative Adversarial Networks - Creating Fake Leaf Images

  • Learning How Generative Adversarial Networks works

  • Using FastAI

  • Creating & Training Generative Adversarial Networks

  • Making Fake Images using GAN


Making ML Web Application

  • Getting started with Streamlit

  • Creating an ML Web Application from scratch using Streamlit

  • making a React Web Application


Deploying ML Applications

  • Learning how to use Cloud Services to Deploy Models & Applications

  • Using Heroku

  • Learning how to Open Source Projects on GitHub

  • How to showcase your projects to impress boss & employees & Get Hired!


A lot of bonus lectures!


This is what included in the package

  • All lecture codes are available for downloadable for free

  • 110+ HD video lectures ( over 50 more to come very soon! )

  • Free support in course Q/A

  • All videos with English captions available


This course is for you if..

  • ... you want to learn the Latest Tools & Techniques used in Deep Learning & Computer Vision

  • ... you want to get more experience to Win Kaggle Competitions

  • ... you want to get started with Computer Vision to become a Computer Vision Engineer

  • .. you are interested in learning Image Classification, Object Detection, Generative Adversarial Networks, Making & Deploying Machine Learning Applications

Who this course is for:

  • You want to become a Computer Vision Engineer & Get Hired
  • Anyone who want to learn latest tools & techniques used in Computer Vision
  • You are already a Programmer and what to extend your skills by learning Computer Vision
  • Who want to learn new Tools & Techniques used in Computer Vision
  • You want to get more experience for winning Kaggle Competitions

Instructor

Machine Learning Engineer
  • 4.6 Instructor Rating
  • 32 Reviews
  • 46,482 Students
  • 1 Course

Hi there, Me Shubhamai

Machine Learning Engineer • Instructor • Teaching Assistant & Mentor ZTM• ML Researcher @ AIcrowd • Love Space & Rocketry



I am a Machine Learning Engineer & Instructor with having a lot of experience with Deep Learning, Computer Vision.


Currently, I am been working as a Machine learning Teaching Assistant @ Zero to Mastery and Machine Learning Research Assistant @ AIcrowd .


I started my journey as an ML Engineer when I discovered Arduino, one of the most popular microcontrollers that can be used anywhere like in hobby projects or in robotics. With programming c++ in Arduino. I started to question myself about how can I increase my software development skills. and that's where I discovered and started learning python & ML, self-taught. I completed many many courses.

I then started building many projects and Then I started freelancing on Fiverr. I got so much experience working with companies and their projects, and go many reviews too like —


- Amazing developer to work with. Great experience, he went above and beyond to create great code that is clean and efficient. Will work with this developer again! - scifundinc


- Shubham is very keen to work on all the aspects mentioned. He takes his time to make the project appealing and accurate. He was very committed to the time limit given and delivered the project before the deadline. His work is commendable and I look forward to take up more projects from him. - swantikalabh


and many many more...


After that I started learning many more skills, like object detection & segmentation, using Detectron2, FastAI, and many advanced techniques in deep learning including working on many kaggle competitions.


But after that, working for endless months, I finally published my first ever online course, The Complete Deep Learning & Computer Vision Course and started my carer as an Instructor. I made this course keeping in mind that this course is what i wanted when i started to learn Deep Learning & Computer Vision.


And then, here we are...

Expected Outcomes

  1. Using Latest Tools & Techniques in Deep Learning & Computer Vision Learning how to used the latest Tensorflow 2.0 How to apply Transfer Learning, Ensemble Learning, using GPUs & TPUs How to work & win Kaggle Competitions Learning to use FastAI How to use Generative Adversarial Networks How to use Weights & Biases for recording Experiments Learning to use Detectron2 for Object Detection Making Machine Learning Web Application from Scratch Learn how to use OpenCV for Computer Vision How to make Real World Applications & Deploy into Cloud Learning Techniques like Object Detection, Classification & Generation Learning how to use Heroku for deploying ML models Working on Kaggle Competitions & Kaggle Kernels Exploring & Visualizing Datasets using popular libraries like Matplotlib & Plotly. Learinng how to use libraries like Pandas, Sklearn, Numpy Creating Advance Data Pipelines using Tensorflow for training Deep Learning Models Setting up Environment & Project for Deep Learning & Computer Vision Show more Show less Course content 14 sections • 127 lectures • 16h 43m total length Expand all sections Introduction 2 lectures • 4min Course Outline Preview 04:10 Online Community 00:10 Setting up Environment 4 lectures • 14min What is Anaconda ? Preview 00:57 Installing Anaconda & Setting up Libraries 06:54 Setting up Anaconda in Mac & Linux 00:25 Jupyter Notebook Walkthrough 05:21 Computer Vision 2 lectures • 4min What is Computer Vision & Deep Learning ? - Part 1 Preview 01:34 What is Computer Vision & Deep Learning ? - Part 2 02:23 OpenCV 8 lectures • 40min OpenCV Introduction 01:37 How Image are being Stored & Numpy Introduction 02:05 Reading & Writing Images 05:33 Understanding Color Spaces 02:24 Using Different Color Spaces 03:07 Notes for Lecture 15: Drawing the CV2 00:33 Drawing in CV2 07:52 Callbacks & Trackbar in CV2 17:11 Image Manipulation & Processing 15 lectures • 1hr 46min Basic Operations 13:10 Brightening, Darkening 04:27 Geometrical Transformations - Scaling, Translation, Rotation 09:05 Prespective Transformation vs Affine Transformation 07:40 Smoothing & Sharping Images and Convolutions 04:21 Image Pyramids & Blending Images 07:30 Bitwise Operators & Image Masking - Showing Only Dog or Background ? 10:52 Image Thresholding 07:18 Image Gradients 07:46 Morphological Operators - Dialation, Erosion, Opening, Closing 04:12 Templet Matching 06:01 Canny Edge Detector 00:22 Canny Edge Detector & Hough Transform 02:58 Contours - Part 1 10:23 Contours - Part 2 09:43 Working with Videos 3 lectures • 17min Reading Video & Camera Feed 05:32 Using Webcam 00:14 Playing with Video 10:44 OpenCV Projects 5 lectures • 47min Haar Cascade 02:12 Detecting Cars 05:06 Lane Detection - Part 1 09:03 Lane Detection - Part 2 14:24 Lane Detection - Final Part Preview 16:19 Deep Learning - How Neural Networks Works ? 5 lectures • 39min The Neuron & Activation Function | How NN Works 09:41 Optional : Resources to Follow 00:28 Gradient Descent & BackPropagation | How NN Learns 11:53 Convolution & Max Polling and Flattening 14:20 Transfer Learning 02:29 Classifying Plant Leaf Images 37 lectures • 6hr 9min Kaggle Introduction 05:20 Setting up Project 04:20 Why not Google Colab from Start ? 00:21 Problem Statement & Kaggle API for Downloading Data 17:39 Our Goal! 00:19 Getting our Workspace Ready & Data Exploration 12:56 Tensorflow 00:18 Data Visualizations 09:39 Image Visualizations 09:58 Keras Data Generator & Splitting Data 09:54 Making Training and Validation Set 11:23 Creating the Keras Model & Compiling It 12:56 Setting up Tensorboard and Weights & Biases Callback 11:53 Note: Lecture 58. Sample Testing & Training the Model 00:41 Sample Testing & Training the Model 10:58 Analysing The Results & Saving the History 05:35 Xception Model Transfer Learning ( Using Only Architecture ) 11:32 Xception Model Transfer Learning ( Freezing the Layers ) 10:13 Ensemble Learning Introduction 01:39 Implementing Ensemble Learning 15:46 TPU 04:28 Setting up Kaggle Notebok 07:36 Setting up TPU 10:06 tf.data Introduction 04:00 Using tf.data 19:56 Training the Model 19:12 Ensemble Learning 13:58 Hyperparameter Tuning 23:22 Big Hyperparameter Tuning 18:34 Saving & Loading Our Model 06:34 Saving Predictions - Part 1 10:40 Saving Predictions - Part 2 08:26 Submitting to Kaggle 07:03 Winners Solutions 12:17 All Winners Solutions Links 00:12 Creating Weights & Biases Report Preview 39:17 How we can Improve ? 00:15 Detecting Wheat Heads 20 lectures • 3hr 51min What is Object Detection 06:01 Understanding Problem & Data 04:45 Setting up Notebook 11:20 Downloading & Importing all Libraries 14:55 Reading & Visualising Data and Images 20:15 Creating our Training & Validation Dataset 41:43 Creating the Configuration 20:07 Creating Custom Wandb Callback - Part 1 14:04 Creating Custom Wandb Callback - Part 2 15:19 Setting up Custom Wandb callback & Training the Model 08:43 Analysing The Results 08:33 Training Model for 1 Epoch 06:44 Analysing The Results Preview 05:20 Using Retinanet 06:10 Analysing The Results 04:53 Loading Model & Making Predictions on Single Image 13:59 Making Predictions on Test Dataset 20:24 Making Weights & Biases Report 00:10 Winners Solutions 07:03 How we can Improve ? 00:14 4 more sections Requirements Basic Python programming knowledge A Computer with Internet Connection All tools used in this course are free to use Description This Brand New and Modern Deep Learning & Computer Vision Course will teach you everything you will need to know to become a Computer Vision Expert. Deep Learning & Computer Vision is currently one of the most increasing fields of Artificial Intelligence and Companies like Google, Apple, Facebook, Amazon are highly investing in this field. Deep Learning & Computer Vision jobs are increasing day by day & provide some of the highest paying jobs all over the world. If We Want Machines to Think, We Need to Teach Them to See. -Fei Fei Li, Director of Stanford AI Lab and Stanford Vision Lab Computer Vision allows us to see the world & process digital images & videos to extract useful information to do a certain task from classification, object detection, and much more. Python is one of the most popular used programming language in Deep Learning and Computer Vision. All the tools, techniques & technologies used in this course - Learning Computer Vision & Deep Learning Fundamentals Setting up Anaconda , Installing Libraries & Jupyter Notebook Learning fundamentals of OpenCV & Numpy - Reading images, Colorspaces, Drawing & Callbacks Advanced OpenCV - Image Preprocessing, Geometrical transformations, Perspective transformations & affine transformations, image blending & pyramids, image gradients & thresholding, Canny Edge Detector and contours Working with videos in OpenCV - Using webcam, Haar Cascades & Object Detection , Lane Detection Deep Learning & How Neural Network Works? - Artificial neural networks, Convolution Neural Networks & Transfer Learning Image Classification - Plant leaf Classification Working on very recent Kaggle Competitions Using Google Colab & Kaggle Kernels Using the latest Tensorflow 2.0 & Keras Using Keras Data Generators & Data Argumentation Using Transfer Learning & Ensemble learning Using State of The Art Deep Learning Models Using GPU & TPU for Model Training Hyperparameter Tuning Using Weights & Biases for recording Deep Learning experimentations Saving & Loading Models Creating a Weights & Biases Report & Showcasing the Project! Object Detection - Wheat heads Detection Working on Kaggle Competitions, again! Using Facebook's Detectron2 for Object Detection Creating COCO Dataset from scratch Training Faster RCNN Model and Custom Weights & Biases callback Using Retinanet Saving & Loading Detectron2 models Generative Adversarial Networks - Creating Fake Leaf Images Learning How Generative Adversarial Networks works Using FastAI Creating & Training Generative Adversarial Networks Making Fake Images using GAN Making ML Web Application Getting started with Streamlit Creating an ML Web Application from scratch using Streamlit making a React Web Application Deploying ML Applications Learning how to use Cloud Services to Deploy Models & Applications Using Heroku Learning how to Open Source Projects on GitHub How to showcase your projects to impress boss & employees & Get Hired! A lot of bonus lectures! This is what included in the package All lecture codes are available for downloadable for free 110+ HD video lectures ( over 50 more to come very soon! ) Free support in course Q/A All videos with English captions available This course is for you if.. ... you want to learn the Latest Tools & Techniques used in Deep Learning & Computer Vision ... you want to get more experience to Win Kaggle Competitions ... you want to get started with Computer Vision to become a Computer Vision Engineer .. you are interested in learning Image Classification , Object Detection , Generative Adversarial Networks , Making & Deploying Machine Learning Applications Who this course is for: You want to become a Computer Vision Engineer & Get Hired Anyone who want to learn latest tools & techniques used in Computer Vision You are already a Programmer and what to extend your skills by learning Computer Vision Who want to learn new Tools & Techniques used in Computer Vision You want to get more experience for winning Kaggle Competitions Show more Show less Instructor Shubham Gupta Machine Learning Engineer 4.6 Instructor Rating 32 Reviews 46,482 Students 1 Course Hi there, Me Shubhamai Machine Learning Engineer • Instructor • Teaching Assistant & Mentor ZTM• ML Researcher @ AIcrowd • Love Space & Rocketry I am a Machine Learning Engineer & Instructor with having a lot of experience with Deep Learning, Computer Vision. Currently, I am been working as a Machine learning Teaching Assistant @ Zero to Mastery and Machine Learning Research Assistant @ AIcrowd . I started my journey as an ML Engineer when I discovered Arduino, one of the most popular microcontrollers that can be used anywhere like in hobby projects or in robotics. With programming c++ in Arduino. I started to question myself about how can I increase my software development skills. and that's where I discovered and started learning python & ML, self-taught. I completed many many courses. I then started building many projects and Then I started freelancing on Fiverr. I got so much experience working with companies and their projects, and go many reviews too like — - Amazing developer to work with. Great experience, he went above and beyond to create great code that is clean and efficient. Will work with this developer again! - scifundinc - Shubham is very keen to work on all the aspects mentioned. He takes his time to make the project appealing and accurate. He was very committed to the time limit given and delivered the project before the deadline. His work is commendable and I look forward to take up more projects from him. - swantikalabh and many many more... After that I started learning many more skills, like object detection & segmentation, using Detectron2, FastAI, and many advanced techniques in deep learning including working on many kaggle competitions. But after that, working for endless months, I finally published my first ever online course, The Complete Deep Learning & Computer Vision Course and started my carer as an Instructor. I made this course keeping in mind that this course is what i wanted when i started to learn Deep Learning & Computer Vision. And then, here we are... Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'677932a08bf02c5e',m:'16e26019dc7e1e1070c4db63141b53ece076dae5-1627759371-1800-AZWB1tRpa/O8NDbRUCQaIuPtqCFXD1cT1iMFKs4CZpCTyAS5VMNOvTs9VBqy3e25xxWVb6VVFM5Gte2V6ppX5IC21Mvobkg0sg0qOkF+R6Cr9K8uwC/t66PWM9uYc8IOmCEL7SQIEVxJMBvr1uboQAU=',s:[0x654470ff40,0x47e26628a1],}})();
  2. Learning how to used the latest Tensorflow 2.0 How to apply Transfer Learning, Ensemble Learning, using GPUs & TPUs How to work & win Kaggle Competitions Learning to use FastAI How to use Generative Adversarial Networks How to use Weights & Biases for recording Experiments Learning to use Detectron2 for Object Detection Making Machine Learning Web Application from Scratch Learn how to use OpenCV for Computer Vision How to make Real World Applications & Deploy into Cloud Learning Techniques like Object Detection, Classification & Generation Learning how to use Heroku for deploying ML models Working on Kaggle Competitions & Kaggle Kernels Exploring & Visualizing Datasets using popular libraries like Matplotlib & Plotly. Learinng how to use libraries like Pandas, Sklearn, Numpy Creating Advance Data Pipelines using Tensorflow for training Deep Learning Models Setting up Environment & Project for Deep Learning & Computer Vision Show more Show less Course content 14 sections • 127 lectures • 16h 43m total length Expand all sections Introduction 2 lectures • 4min Course Outline Preview 04:10 Online Community 00:10 Setting up Environment 4 lectures • 14min What is Anaconda ? Preview 00:57 Installing Anaconda & Setting up Libraries 06:54 Setting up Anaconda in Mac & Linux 00:25 Jupyter Notebook Walkthrough 05:21 Computer Vision 2 lectures • 4min What is Computer Vision & Deep Learning ? - Part 1 Preview 01:34 What is Computer Vision & Deep Learning ? - Part 2 02:23 OpenCV 8 lectures • 40min OpenCV Introduction 01:37 How Image are being Stored & Numpy Introduction 02:05 Reading & Writing Images 05:33 Understanding Color Spaces 02:24 Using Different Color Spaces 03:07 Notes for Lecture 15: Drawing the CV2 00:33 Drawing in CV2 07:52 Callbacks & Trackbar in CV2 17:11 Image Manipulation & Processing 15 lectures • 1hr 46min Basic Operations 13:10 Brightening, Darkening 04:27 Geometrical Transformations - Scaling, Translation, Rotation 09:05 Prespective Transformation vs Affine Transformation 07:40 Smoothing & Sharping Images and Convolutions 04:21 Image Pyramids & Blending Images 07:30 Bitwise Operators & Image Masking - Showing Only Dog or Background ? 10:52 Image Thresholding 07:18 Image Gradients 07:46 Morphological Operators - Dialation, Erosion, Opening, Closing 04:12 Templet Matching 06:01 Canny Edge Detector 00:22 Canny Edge Detector & Hough Transform 02:58 Contours - Part 1 10:23 Contours - Part 2 09:43 Working with Videos 3 lectures • 17min Reading Video & Camera Feed 05:32 Using Webcam 00:14 Playing with Video 10:44 OpenCV Projects 5 lectures • 47min Haar Cascade 02:12 Detecting Cars 05:06 Lane Detection - Part 1 09:03 Lane Detection - Part 2 14:24 Lane Detection - Final Part Preview 16:19 Deep Learning - How Neural Networks Works ? 5 lectures • 39min The Neuron & Activation Function | How NN Works 09:41 Optional : Resources to Follow 00:28 Gradient Descent & BackPropagation | How NN Learns 11:53 Convolution & Max Polling and Flattening 14:20 Transfer Learning 02:29 Classifying Plant Leaf Images 37 lectures • 6hr 9min Kaggle Introduction 05:20 Setting up Project 04:20 Why not Google Colab from Start ? 00:21 Problem Statement & Kaggle API for Downloading Data 17:39 Our Goal! 00:19 Getting our Workspace Ready & Data Exploration 12:56 Tensorflow 00:18 Data Visualizations 09:39 Image Visualizations 09:58 Keras Data Generator & Splitting Data 09:54 Making Training and Validation Set 11:23 Creating the Keras Model & Compiling It 12:56 Setting up Tensorboard and Weights & Biases Callback 11:53 Note: Lecture 58. Sample Testing & Training the Model 00:41 Sample Testing & Training the Model 10:58 Analysing The Results & Saving the History 05:35 Xception Model Transfer Learning ( Using Only Architecture ) 11:32 Xception Model Transfer Learning ( Freezing the Layers ) 10:13 Ensemble Learning Introduction 01:39 Implementing Ensemble Learning 15:46 TPU 04:28 Setting up Kaggle Notebok 07:36 Setting up TPU 10:06 tf.data Introduction 04:00 Using tf.data 19:56 Training the Model 19:12 Ensemble Learning 13:58 Hyperparameter Tuning 23:22 Big Hyperparameter Tuning 18:34 Saving & Loading Our Model 06:34 Saving Predictions - Part 1 10:40 Saving Predictions - Part 2 08:26 Submitting to Kaggle 07:03 Winners Solutions 12:17 All Winners Solutions Links 00:12 Creating Weights & Biases Report Preview 39:17 How we can Improve ? 00:15 Detecting Wheat Heads 20 lectures • 3hr 51min What is Object Detection 06:01 Understanding Problem & Data 04:45 Setting up Notebook 11:20 Downloading & Importing all Libraries 14:55 Reading & Visualising Data and Images 20:15 Creating our Training & Validation Dataset 41:43 Creating the Configuration 20:07 Creating Custom Wandb Callback - Part 1 14:04 Creating Custom Wandb Callback - Part 2 15:19 Setting up Custom Wandb callback & Training the Model 08:43 Analysing The Results 08:33 Training Model for 1 Epoch 06:44 Analysing The Results Preview 05:20 Using Retinanet 06:10 Analysing The Results 04:53 Loading Model & Making Predictions on Single Image 13:59 Making Predictions on Test Dataset 20:24 Making Weights & Biases Report 00:10 Winners Solutions 07:03 How we can Improve ? 00:14 4 more sections Requirements Basic Python programming knowledge A Computer with Internet Connection All tools used in this course are free to use Description This Brand New and Modern Deep Learning & Computer Vision Course will teach you everything you will need to know to become a Computer Vision Expert. Deep Learning & Computer Vision is currently one of the most increasing fields of Artificial Intelligence and Companies like Google, Apple, Facebook, Amazon are highly investing in this field. Deep Learning & Computer Vision jobs are increasing day by day & provide some of the highest paying jobs all over the world. If We Want Machines to Think, We Need to Teach Them to See. -Fei Fei Li, Director of Stanford AI Lab and Stanford Vision Lab Computer Vision allows us to see the world & process digital images & videos to extract useful information to do a certain task from classification, object detection, and much more. Python is one of the most popular used programming language in Deep Learning and Computer Vision. All the tools, techniques & technologies used in this course - Learning Computer Vision & Deep Learning Fundamentals Setting up Anaconda , Installing Libraries & Jupyter Notebook Learning fundamentals of OpenCV & Numpy - Reading images, Colorspaces, Drawing & Callbacks Advanced OpenCV - Image Preprocessing, Geometrical transformations, Perspective transformations & affine transformations, image blending & pyramids, image gradients & thresholding, Canny Edge Detector and contours Working with videos in OpenCV - Using webcam, Haar Cascades & Object Detection , Lane Detection Deep Learning & How Neural Network Works? - Artificial neural networks, Convolution Neural Networks & Transfer Learning Image Classification - Plant leaf Classification Working on very recent Kaggle Competitions Using Google Colab & Kaggle Kernels Using the latest Tensorflow 2.0 & Keras Using Keras Data Generators & Data Argumentation Using Transfer Learning & Ensemble learning Using State of The Art Deep Learning Models Using GPU & TPU for Model Training Hyperparameter Tuning Using Weights & Biases for recording Deep Learning experimentations Saving & Loading Models Creating a Weights & Biases Report & Showcasing the Project! Object Detection - Wheat heads Detection Working on Kaggle Competitions, again! Using Facebook's Detectron2 for Object Detection Creating COCO Dataset from scratch Training Faster RCNN Model and Custom Weights & Biases callback Using Retinanet Saving & Loading Detectron2 models Generative Adversarial Networks - Creating Fake Leaf Images Learning How Generative Adversarial Networks works Using FastAI Creating & Training Generative Adversarial Networks Making Fake Images using GAN Making ML Web Application Getting started with Streamlit Creating an ML Web Application from scratch using Streamlit making a React Web Application Deploying ML Applications Learning how to use Cloud Services to Deploy Models & Applications Using Heroku Learning how to Open Source Projects on GitHub How to showcase your projects to impress boss & employees & Get Hired! A lot of bonus lectures! This is what included in the package All lecture codes are available for downloadable for free 110+ HD video lectures ( over 50 more to come very soon! ) Free support in course Q/A All videos with English captions available This course is for you if.. ... you want to learn the Latest Tools & Techniques used in Deep Learning & Computer Vision ... you want to get more experience to Win Kaggle Competitions ... you want to get started with Computer Vision to become a Computer Vision Engineer .. you are interested in learning Image Classification , Object Detection , Generative Adversarial Networks , Making & Deploying Machine Learning Applications Who this course is for: You want to become a Computer Vision Engineer & Get Hired Anyone who want to learn latest tools & techniques used in Computer Vision You are already a Programmer and what to extend your skills by learning Computer Vision Who want to learn new Tools & Techniques used in Computer Vision You want to get more experience for winning Kaggle Competitions Show more Show less Instructor Shubham Gupta Machine Learning Engineer 4.6 Instructor Rating 32 Reviews 46,482 Students 1 Course Hi there, Me Shubhamai Machine Learning Engineer • Instructor • Teaching Assistant & Mentor ZTM• ML Researcher @ AIcrowd • Love Space & Rocketry I am a Machine Learning Engineer & Instructor with having a lot of experience with Deep Learning, Computer Vision. Currently, I am been working as a Machine learning Teaching Assistant @ Zero to Mastery and Machine Learning Research Assistant @ AIcrowd . I started my journey as an ML Engineer when I discovered Arduino, one of the most popular microcontrollers that can be used anywhere like in hobby projects or in robotics. With programming c++ in Arduino. I started to question myself about how can I increase my software development skills. and that's where I discovered and started learning python & ML, self-taught. I completed many many courses. I then started building many projects and Then I started freelancing on Fiverr. I got so much experience working with companies and their projects, and go many reviews too like — - Amazing developer to work with. Great experience, he went above and beyond to create great code that is clean and efficient. Will work with this developer again! - scifundinc - Shubham is very keen to work on all the aspects mentioned. He takes his time to make the project appealing and accurate. He was very committed to the time limit given and delivered the project before the deadline. His work is commendable and I look forward to take up more projects from him. - swantikalabh and many many more... After that I started learning many more skills, like object detection & segmentation, using Detectron2, FastAI, and many advanced techniques in deep learning including working on many kaggle competitions. But after that, working for endless months, I finally published my first ever online course, The Complete Deep Learning & Computer Vision Course and started my carer as an Instructor. I made this course keeping in mind that this course is what i wanted when i started to learn Deep Learning & Computer Vision. And then, here we are... Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'677932a08bf02c5e',m:'16e26019dc7e1e1070c4db63141b53ece076dae5-1627759371-1800-AZWB1tRpa/O8NDbRUCQaIuPtqCFXD1cT1iMFKs4CZpCTyAS5VMNOvTs9VBqy3e25xxWVb6VVFM5Gte2V6ppX5IC21Mvobkg0sg0qOkF+R6Cr9K8uwC/t66PWM9uYc8IOmCEL7SQIEVxJMBvr1uboQAU=',s:[0x654470ff40,0x47e26628a1],}})();
  3. How to apply Transfer Learning, Ensemble Learning, using GPUs & TPUs How to work & win Kaggle Competitions Learning to use FastAI How to use Generative Adversarial Networks How to use Weights & Biases for recording Experiments Learning to use Detectron2 for Object Detection Making Machine Learning Web Application from Scratch Learn how to use OpenCV for Computer Vision How to make Real World Applications & Deploy into Cloud Learning Techniques like Object Detection, Classification & Generation Learning how to use Heroku for deploying ML models Working on Kaggle Competitions & Kaggle Kernels Exploring & Visualizing Datasets using popular libraries like Matplotlib & Plotly. Learinng how to use libraries like Pandas, Sklearn, Numpy Creating Advance Data Pipelines using Tensorflow for training Deep Learning Models Setting up Environment & Project for Deep Learning & Computer Vision Show more Show less Course content 14 sections • 127 lectures • 16h 43m total length Expand all sections Introduction 2 lectures • 4min Course Outline Preview 04:10 Online Community 00:10 Setting up Environment 4 lectures • 14min What is Anaconda ? Preview 00:57 Installing Anaconda & Setting up Libraries 06:54 Setting up Anaconda in Mac & Linux 00:25 Jupyter Notebook Walkthrough 05:21 Computer Vision 2 lectures • 4min What is Computer Vision & Deep Learning ? - Part 1 Preview 01:34 What is Computer Vision & Deep Learning ? - Part 2 02:23 OpenCV 8 lectures • 40min OpenCV Introduction 01:37 How Image are being Stored & Numpy Introduction 02:05 Reading & Writing Images 05:33 Understanding Color Spaces 02:24 Using Different Color Spaces 03:07 Notes for Lecture 15: Drawing the CV2 00:33 Drawing in CV2 07:52 Callbacks & Trackbar in CV2 17:11 Image Manipulation & Processing 15 lectures • 1hr 46min Basic Operations 13:10 Brightening, Darkening 04:27 Geometrical Transformations - Scaling, Translation, Rotation 09:05 Prespective Transformation vs Affine Transformation 07:40 Smoothing & Sharping Images and Convolutions 04:21 Image Pyramids & Blending Images 07:30 Bitwise Operators & Image Masking - Showing Only Dog or Background ? 10:52 Image Thresholding 07:18 Image Gradients 07:46 Morphological Operators - Dialation, Erosion, Opening, Closing 04:12 Templet Matching 06:01 Canny Edge Detector 00:22 Canny Edge Detector & Hough Transform 02:58 Contours - Part 1 10:23 Contours - Part 2 09:43 Working with Videos 3 lectures • 17min Reading Video & Camera Feed 05:32 Using Webcam 00:14 Playing with Video 10:44 OpenCV Projects 5 lectures • 47min Haar Cascade 02:12 Detecting Cars 05:06 Lane Detection - Part 1 09:03 Lane Detection - Part 2 14:24 Lane Detection - Final Part Preview 16:19 Deep Learning - How Neural Networks Works ? 5 lectures • 39min The Neuron & Activation Function | How NN Works 09:41 Optional : Resources to Follow 00:28 Gradient Descent & BackPropagation | How NN Learns 11:53 Convolution & Max Polling and Flattening 14:20 Transfer Learning 02:29 Classifying Plant Leaf Images 37 lectures • 6hr 9min Kaggle Introduction 05:20 Setting up Project 04:20 Why not Google Colab from Start ? 00:21 Problem Statement & Kaggle API for Downloading Data 17:39 Our Goal! 00:19 Getting our Workspace Ready & Data Exploration 12:56 Tensorflow 00:18 Data Visualizations 09:39 Image Visualizations 09:58 Keras Data Generator & Splitting Data 09:54 Making Training and Validation Set 11:23 Creating the Keras Model & Compiling It 12:56 Setting up Tensorboard and Weights & Biases Callback 11:53 Note: Lecture 58. Sample Testing & Training the Model 00:41 Sample Testing & Training the Model 10:58 Analysing The Results & Saving the History 05:35 Xception Model Transfer Learning ( Using Only Architecture ) 11:32 Xception Model Transfer Learning ( Freezing the Layers ) 10:13 Ensemble Learning Introduction 01:39 Implementing Ensemble Learning 15:46 TPU 04:28 Setting up Kaggle Notebok 07:36 Setting up TPU 10:06 tf.data Introduction 04:00 Using tf.data 19:56 Training the Model 19:12 Ensemble Learning 13:58 Hyperparameter Tuning 23:22 Big Hyperparameter Tuning 18:34 Saving & Loading Our Model 06:34 Saving Predictions - Part 1 10:40 Saving Predictions - Part 2 08:26 Submitting to Kaggle 07:03 Winners Solutions 12:17 All Winners Solutions Links 00:12 Creating Weights & Biases Report Preview 39:17 How we can Improve ? 00:15 Detecting Wheat Heads 20 lectures • 3hr 51min What is Object Detection 06:01 Understanding Problem & Data 04:45 Setting up Notebook 11:20 Downloading & Importing all Libraries 14:55 Reading & Visualising Data and Images 20:15 Creating our Training & Validation Dataset 41:43 Creating the Configuration 20:07 Creating Custom Wandb Callback - Part 1 14:04 Creating Custom Wandb Callback - Part 2 15:19 Setting up Custom Wandb callback & Training the Model 08:43 Analysing The Results 08:33 Training Model for 1 Epoch 06:44 Analysing The Results Preview 05:20 Using Retinanet 06:10 Analysing The Results 04:53 Loading Model & Making Predictions on Single Image 13:59 Making Predictions on Test Dataset 20:24 Making Weights & Biases Report 00:10 Winners Solutions 07:03 How we can Improve ? 00:14 4 more sections Requirements Basic Python programming knowledge A Computer with Internet Connection All tools used in this course are free to use Description This Brand New and Modern Deep Learning & Computer Vision Course will teach you everything you will need to know to become a Computer Vision Expert. Deep Learning & Computer Vision is currently one of the most increasing fields of Artificial Intelligence and Companies like Google, Apple, Facebook, Amazon are highly investing in this field. Deep Learning & Computer Vision jobs are increasing day by day & provide some of the highest paying jobs all over the world. If We Want Machines to Think, We Need to Teach Them to See. -Fei Fei Li, Director of Stanford AI Lab and Stanford Vision Lab Computer Vision allows us to see the world & process digital images & videos to extract useful information to do a certain task from classification, object detection, and much more. Python is one of the most popular used programming language in Deep Learning and Computer Vision. All the tools, techniques & technologies used in this course - Learning Computer Vision & Deep Learning Fundamentals Setting up Anaconda , Installing Libraries & Jupyter Notebook Learning fundamentals of OpenCV & Numpy - Reading images, Colorspaces, Drawing & Callbacks Advanced OpenCV - Image Preprocessing, Geometrical transformations, Perspective transformations & affine transformations, image blending & pyramids, image gradients & thresholding, Canny Edge Detector and contours Working with videos in OpenCV - Using webcam, Haar Cascades & Object Detection , Lane Detection Deep Learning & How Neural Network Works? - Artificial neural networks, Convolution Neural Networks & Transfer Learning Image Classification - Plant leaf Classification Working on very recent Kaggle Competitions Using Google Colab & Kaggle Kernels Using the latest Tensorflow 2.0 & Keras Using Keras Data Generators & Data Argumentation Using Transfer Learning & Ensemble learning Using State of The Art Deep Learning Models Using GPU & TPU for Model Training Hyperparameter Tuning Using Weights & Biases for recording Deep Learning experimentations Saving & Loading Models Creating a Weights & Biases Report & Showcasing the Project! Object Detection - Wheat heads Detection Working on Kaggle Competitions, again! Using Facebook's Detectron2 for Object Detection Creating COCO Dataset from scratch Training Faster RCNN Model and Custom Weights & Biases callback Using Retinanet Saving & Loading Detectron2 models Generative Adversarial Networks - Creating Fake Leaf Images Learning How Generative Adversarial Networks works Using FastAI Creating & Training Generative Adversarial Networks Making Fake Images using GAN Making ML Web Application Getting started with Streamlit Creating an ML Web Application from scratch using Streamlit making a React Web Application Deploying ML Applications Learning how to use Cloud Services to Deploy Models & Applications Using Heroku Learning how to Open Source Projects on GitHub How to showcase your projects to impress boss & employees & Get Hired! A lot of bonus lectures! This is what included in the package All lecture codes are available for downloadable for free 110+ HD video lectures ( over 50 more to come very soon! ) Free support in course Q/A All videos with English captions available This course is for you if.. ... you want to learn the Latest Tools & Techniques used in Deep Learning & Computer Vision ... you want to get more experience to Win Kaggle Competitions ... you want to get started with Computer Vision to become a Computer Vision Engineer .. you are interested in learning Image Classification , Object Detection , Generative Adversarial Networks , Making & Deploying Machine Learning Applications Who this course is for: You want to become a Computer Vision Engineer & Get Hired Anyone who want to learn latest tools & techniques used in Computer Vision You are already a Programmer and what to extend your skills by learning Computer Vision Who want to learn new Tools & Techniques used in Computer Vision You want to get more experience for winning Kaggle Competitions Show more Show less Instructor Shubham Gupta Machine Learning Engineer 4.6 Instructor Rating 32 Reviews 46,482 Students 1 Course Hi there, Me Shubhamai Machine Learning Engineer • Instructor • Teaching Assistant & Mentor ZTM• ML Researcher @ AIcrowd • Love Space & Rocketry I am a Machine Learning Engineer & Instructor with having a lot of experience with Deep Learning, Computer Vision. Currently, I am been working as a Machine learning Teaching Assistant @ Zero to Mastery and Machine Learning Research Assistant @ AIcrowd . I started my journey as an ML Engineer when I discovered Arduino, one of the most popular microcontrollers that can be used anywhere like in hobby projects or in robotics. With programming c++ in Arduino. I started to question myself about how can I increase my software development skills. and that's where I discovered and started learning python & ML, self-taught. I completed many many courses. I then started building many projects and Then I started freelancing on Fiverr. I got so much experience working with companies and their projects, and go many reviews too like — - Amazing developer to work with. Great experience, he went above and beyond to create great code that is clean and efficient. Will work with this developer again! - scifundinc - Shubham is very keen to work on all the aspects mentioned. He takes his time to make the project appealing and accurate. He was very committed to the time limit given and delivered the project before the deadline. His work is commendable and I look forward to take up more projects from him. - swantikalabh and many many more... After that I started learning many more skills, like object detection & segmentation, using Detectron2, FastAI, and many advanced techniques in deep learning including working on many kaggle competitions. But after that, working for endless months, I finally published my first ever online course, The Complete Deep Learning & Computer Vision Course and started my carer as an Instructor. I made this course keeping in mind that this course is what i wanted when i started to learn Deep Learning & Computer Vision. And then, here we are... Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? '' : 'checked'; var radios = document.querySelectorAll('[name="' + input.dataset.name + '"]'); for (var i = 0; i (function(){window['__CF$cv$params']={r:'677932a08bf02c5e',m:'16e26019dc7e1e1070c4db63141b53ece076dae5-1627759371-1800-AZWB1tRpa/O8NDbRUCQaIuPtqCFXD1cT1iMFKs4CZpCTyAS5VMNOvTs9VBqy3e25xxWVb6VVFM5Gte2V6ppX5IC21Mvobkg0sg0qOkF+R6Cr9K8uwC/t66PWM9uYc8IOmCEL7SQIEVxJMBvr1uboQAU=',s:[0x654470ff40,0x47e26628a1],}})();
  4. How to work & win Kaggle Competitions Learning to use FastAI How to use Generative Adversarial Networks How to use Weights & Biases for recording Experiments Learning to use Detectron2 for Object Detection Making Machine Learning Web Application from Scratch Learn how to use OpenCV for Computer Vision How to make Real World Applications & Deploy into Cloud Learning Techniques like Object Detection, Classification & Generation Learning how to use Heroku for deploying ML models Working on Kaggle Competitions & Kaggle Kernels Exploring & Visualizing Datasets using popular libraries like Matplotlib & Plotly. Learinng how to use libraries like Pandas, Sklearn, Numpy Creating Advance Data Pipelines using Tensorflow for training Deep Learning Models Setting up Environment & Project for Deep Learning & Computer Vision Show more Show less Course content 14 sections • 127 lectures • 16h 43m total length Expand all sections Introduction 2 lectures • 4min Course Outline Preview 04:10 Online Community 00:10 Setting up Environment 4 lectures • 14min What is Anaconda ? Preview 00:57 Installing Anaconda & Setting up Libraries 06:54 Setting up Anaconda in Mac & Linux 00:25 Jupyter Notebook Walkthrough 05:21 Computer Vision 2 lectures • 4min What is Computer Vision & Deep Learning ? - Part 1 Preview 01:34 What is Computer Vision & Deep Learning ? - Part 2 02:23 OpenCV 8 lectures • 40min OpenCV Introduction 01:37 How Image are being Stored & Numpy Introduction 02:05 Reading & Writing Images 05:33 Understanding Color Spaces 02:24 Using Different Color Spaces 03:07 Notes for Lecture 15: Drawing the CV2 00:33 Drawing in CV2 07:52 Callbacks & Trackbar in CV2 17:11 Image Manipulation & Processing 15 lectures • 1hr 46min Basic Operations 13:10 Brightening, Darkening 04:27 Geometrical Transformations - Scaling, Translation, Rotation 09:05 Prespective Transformation vs Affine Transformation 07:40 Smoothing & Sharping Images and Convolutions 04:21 Image Pyramids & Blending Images 07:30 Bitwise Operators & Image Masking - Showing Only Dog or Background ? 10:52 Image Thresholding 07:18 Image Gradients 07:46 Morphological Operators - Dialation, Erosion, Opening, Closing 04:12 Templet Matching 06:01 Canny Edge Detector 00:22 Canny Edge Detector & Hough Transform 02:58 Contours - Part 1 10:23 Contours - Part 2 09:43 Working with Videos 3 lectures • 17min Reading Video & Camera Feed 05:32 Using Webcam 00:14 Playing with Video 10:44 OpenCV Projects 5 lectures • 47min Haar Cascade 02:12 Detecting Cars 05:06 Lane Detection - Part 1 09:03 Lane Detection - Part 2 14:24 Lane Detection - Final Part Preview 16:19 Deep Learning - How Neural Networks Works ? 5 lectures • 39min The Neuron & Activation Function | How NN Works 09:41 Optional : Resources to Follow 00:28 Gradient Descent & BackPropagation | How NN Learns 11:53 Convolution & Max Polling and Flattening 14:20 Transfer Learning 02:29 Classifying Plant Leaf Images 37 lectures • 6hr 9min Kaggle Introduction 05:20 Setting up Project 04:20 Why not Google Colab from Start ? 00:21 Problem Statement & Kaggle API for Downloading Data 17:39 Our Goal! 00:19 Getting our Workspace Ready & Data Exploration 12:56 Tensorflow 00:18 Data Visualizations 09:39 Image Visualizations 09:58 Keras Data Generator & Splitting Data 09:54 Making Training and Validation Set 11:23 Creating the Keras Model & Compiling It 12:56 Setting up Tensorboard and Weights & Biases Callback 11:53 Note: Lecture 58. Sample Testing & Training the Model 00:41 Sample Testing & Training the Model 10:58 Analysing The Results & Saving the History 05:35 Xception Model Transfer Learning ( Using Only Architecture ) 11:32 Xception Model Transfer Learning ( Freezing the Layers ) 10:13 Ensemble Learning Introduction 01:39 Implementing Ensemble Learning 15:46 TPU 04:28 Setting up Kaggle Notebok 07:36 Setting up TPU 10:06 tf.data Introduction 04:00 Using tf.data 19:56 Training the Model 19:12 Ensemble Learning 13:58 Hyperparameter Tuning 23:22 Big Hyperparameter Tuning 18:34 Saving & Loading Our Model 06:34 Saving Predictions - Part 1 10:40 Saving Predictions - Part 2 08:26 Submitting to Kaggle 07:03 Winners Solutions 12:17 All Winners Solutions Links 00:12 Creating Weights & Biases Report Preview 39:17 How we can Improve ? 00:15 Detecting Wheat Heads 20 lectures • 3hr 51min What is Object Detection 06:01 Understanding Problem & Data 04:45 Setting up Notebook 11:20 Downloading & Importing all Libraries 14:55 Reading & Visualising Data and Images 20:15 Creating our Training & Validation Dataset 41:43 Creating the Configuration 20:07 Creating Custom Wandb Callback - Part 1 14:04 Creating Custom Wandb Callback - Part 2 15:19 Setting up Custom Wandb callback & Training the Model 08:43 Analysing The Results 08:33 Training Model for 1 Epoch 06:44 Analysing The Results Preview 05:20 Using Retinanet 06:10 Analysing The Results 04:53 Loading Model & Making Predictions on Single Image 13:59 Making Predictions on Test Dataset 20:24 Making Weights & Biases Report 00:10 Winners Solutions 07:03 How we can Improve ? 00:14 4 more sections Requirements Basic Python programming knowledge A Computer with Internet Connection All tools used in this course are free to use Description This Brand New and Modern Deep Learning & Computer Vision Course will teach you everything you will need to know to become a Computer Vision Expert. Deep Learning & Computer Vision is currently one of the most increasing fields of Artificial Intelligence and Companies like Google, Apple, Facebook, Amazon are highly investing in this field. Deep Learning & Computer Vision jobs are increasing day by day & provide some of the highest paying jobs all over the world. If We Want Machines to Think, We Need to Teach Them to See. -Fei Fei Li, Director of Stanford AI Lab and Stanford Vision Lab Computer Vision allows us to see the world & process digital images & videos to extract useful information to do a certain task from classification, object detection, and much more. Python is one of the most popular used programming language in Deep Learning and Computer Vision. All the tools, techniques & technologies used in this course - Learning Computer Vision & Deep Learning Fundamentals Setting up Anaconda , Installing Libraries & Jupyter Notebook Learning fundamentals of OpenCV & Numpy - Reading images, Colorspaces, Drawing & Callbacks Advanced OpenCV - Image Preprocessing, Geometrical transformations, Perspective transformations & affine transformations, image blending & pyramids, image gradients & thresholding, Canny Edge Detector and contours Working with videos in OpenCV - Using webcam, Haar Cascades & Object Detection , Lane Detection Deep Learning & How Neural Network Works? - Artificial neural networks, Convolution Neural Networks & Transfer Learning Image Classification - Plant leaf Classification Working on very recent Kaggle Competitions Using Google Colab & Kaggle Kernels Using the latest Tensorflow 2.0 & Keras Using Keras Data Generators & Data Argumentation Using Transfer Learning & Ensemble learning Using State of The Art Deep Learning Models Using GPU & TPU for Model Training Hyperparameter Tuning Using Weights & Biases for recording Deep Learning experimentations Saving & Loading Models Creating a Weights & Biases Report & Showcasing the Project! Object Detection - Wheat heads Detection Working on Kaggle Competitions, again! Using Facebook's Detectron2 for Object Detection Creating COCO Dataset from scratch Training Faster RCNN Model and Custom Weights & Biases callback Using Retinanet Saving & Loading Detectron2 models Generative Adversarial Networks - Creating Fake Leaf Images Learning How Generative Adversarial Networks works Using FastAI Creating & Training Generative Adversarial Networks Making Fake Images using GAN Making ML Web Application Getting started with Streamlit Creating an ML Web Application from scratch using Streamlit making a React Web Application Deploying ML Applications Learning how to use Cloud Services to Deploy Models & Applications Using Heroku Learning how to Open Source Projects on GitHub How to showcase your projects to impress boss & employees & Get Hired! A lot of bonus lectures! This is what included in the package All lecture codes are available for downloadable for free 110+ HD video lectures ( over 50 more to come very soon! ) Free support in course Q/A All videos with English captions available This course is for you if.. ... you want to learn the Latest Tools & Techniques used in Deep Learning & Computer Vision ... you want to get more experience to Win Kaggle Competitions ... you want to get started with Computer Vision to become a Computer Vision Engineer .. you are interested in learning Image Classification , Object Detection , Generative Adversarial Networks , Making & Deploying Machine Learning Applications Who this course is for: You want to become a Computer Vision Engineer & Get Hired Anyone who want to learn latest tools & techniques used in Computer Vision You are already a Programmer and what to extend your skills by learning Computer Vision Who want to learn new Tools & Techniques used in Computer Vision You want to get more experience for winning Kaggle Competitions Show more Show less Instructor Shubham Gupta Machine Learning Engineer 4.6 Instructor Rating 32 Reviews 46,482 Students 1 Course Hi there, Me Shubhamai Machine Learning Engineer • Instructor • Teaching Assistant & Mentor ZTM• ML Researcher @ AIcrowd • Love Space & Rocketry I am a Machine Learning Engineer & Instructor with having a lot of experience with Deep Learning, Computer Vision. Currently, I am been working as a Machine learning Teaching Assistant @ Zero to Mastery and Machine Learning Research Assistant @ AIcrowd . I started my journey as an ML Engineer when I discovered Arduino, one of the most popular microcontrollers that can be used anywhere like in hobby projects or in robotics. With programming c++ in Arduino. I started to question myself about how can I increase my software development skills. and that's where I discovered and started learning python & ML, self-taught. I completed many many courses. I then started building many projects and Then I started freelancing on Fiverr. I got so much experience working with companies and their projects, and go many reviews too like — - Amazing developer to work with. Great experience, he went above and beyond to create great code that is clean and efficient. Will work with this developer again! - scifundinc - Shubham is very keen to work on all the aspects mentioned. He takes his time to make the project appealing and accurate. He was very committed to the time limit given and delivered the project before the deadline. His work is commendable and I look forward to take up more projects from him. - swantikalabh and many many more... After that I started learning many more skills, like object detection & segmentation, using Detectron2, FastAI, and many advanced techniques in deep learning including working on many kaggle competitions. But after that, working for endless months, I finally published my first ever online course, The Complete Deep Learning & Computer Vision Course and started my carer as an Instructor. I made this course keeping in mind that this course is what i wanted when i started to learn Deep Learning & Computer Vision. And then, here we are... Show more Show less Udemy Business Teach on Udemy Get the app About us Contact us Careers Blog Help and Support Affiliate Impressum Kontakt Terms Privacy policy Cookie settings Sitemap © 2021 Udemy, Inc. window.handleCSSToggleButtonClick = function (event) { var target = event.currentTarget; var cssToggleId = target && target.dataset && target.dataset.cssToggleId; var input = cssToggleId && document.getElementById(cssToggleId); if (input) { if (input.dataset.type === 'checkbox') { input.dataset.checked = input.dataset.checked ? '' : 'checked'; } else { input.dataset.checked = input.dataset.allowToggle && input.dataset.checked ? 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  5. Learning to use FastAI How to use Generative Adversarial Networks How to use Weights & Biases for recording Experiments Learning to use Detectron2 for Object Detection Making Machine Learning Web Application from Scratch Learn how to use OpenCV for Computer Vision How to make Real World Applications & Deploy into Cloud Learning Techniques like Object Detection, Classification & Generation Learning how to use Heroku for deploying ML models Working on Kaggle Competitions & Kaggle Kernels Exploring & Visualizing Datasets using popular libraries like Matplotlib & Plotly. Learinng how to use libraries like Pandas, Sklearn, Numpy Creating Advance Data Pipelines using Tensorflow for training Deep Learning Models Setting up Environment & Project for Deep Learning & Computer Vision Show more Show less Course content 14 sections • 127 lectures • 16h 43m total length Expand all sections Introduction 2 lectures • 4min Course Outline Preview 04:10 Online Community 00:10 Setting up Environment 4 lectures • 14min What is Anaconda ? Preview 00:57 Installing Anaconda & Setting up Libraries 06:54 Setting up Anaconda in Mac & Linux 00:25 Jupyter Notebook Walkthrough 05:21 Computer Vision 2 lectures • 4min What is Computer Vision & Deep Learning ? - Part 1 Preview 01:34 What is Computer Vision & Deep Learning ? - Part 2 02:23 OpenCV 8 lectures • 40min OpenCV Introduction 01:37 How Image are being Stored & Numpy Introduction 02:05 Reading & Writing Images 05:33 Understanding Color Spaces 02:24 Using Different Color Spaces 03:07 Notes for Lecture 15: Drawing the CV2 00:33 Drawing in CV2 07:52 Callbacks & Trackbar in CV2 17:11 Image Manipulation & Processing 15 lectures • 1hr 46min Basic Operations 13:10 Brightening, Darkening 04:27 Geometrical Transformations - Scaling, Translation, Rotation 09:05 Prespective Transformation vs Affine Transformation 07:40 Smoothing & Sharping Images and Convolutions 04:21 Image Pyramids & Blending Images 07:30 Bitwise Operators & Image Masking - Showing Only Dog or Background ? 10:52 Image Thresholding 07:18 Image Gradients 07:46 Morphological Operators - Dialation, Erosion, Opening, Closing 04:12 Templet Matching 06:01 Canny Edge Detector 00:22 Canny Edge Detector & Hough Transform 02:58 Contours - Part 1 10:23 Contours - Part 2 09:43 Working with Videos 3 lectures • 17min Reading Video & Camera Feed 05:32 Using Webcam 00:14 Playing with Video 10:44 OpenCV Projects 5 lectures • 47min Haar Cascade 02:12 Detecting Cars 05:06 Lane Detection - Part 1 09:03 Lane Detection - Part 2 14:24 Lane Detection - Final Part Preview 16:19 Deep Learning - How Neural Networks Works ? 5 lectures • 39min The Neuron & Activation Function | How NN Works 09:41 Optional : Resources to Follow 00:28 Gradient Descent & BackPropagation | How NN Learns 11:53 Convolution & Max Polling and Flattening 14:20 Transfer Learning 02:29 Classifying Plant Leaf Images 37 lectures • 6hr 9min Kaggle Introduction 05:20 Setting up Project 04:20 Why not Google Colab from Start ? 00:21 Problem Statement & Kaggle API for Downloading Data 17:39 Our Goal! 00:19 Getting our Workspace Ready & Data Exploration 12:56 Tensorflow 00:18 Data Visualizations 09:39 Image Visualizations 09:58 Keras Data Generator & Splitting Data 09:54 Making Training and Validation Set 11:23 Creating the Keras Model & Compiling It 12:56 Setting up Tensorboard and Weights & Biases Callback 11:53 Note: Lecture 58. Sample Testing & Training the Model 00:41 Sample Testing & Training the Model 10:58 Analysing The Results & Saving the History 05:35 Xception Model Transfer Learning ( Using Only Architecture ) 11:32 Xception Model Transfer Learning ( Freezing the Layers ) 10:13 Ensemble Learning Introduction 01:39 Implementing Ensemble Learning 15:46 TPU 04:28 Setting up Kaggle Notebok 07:36 Setting up TPU 10:06 tf.data Introduction 04:00 Using tf.data 19:56 Training the Model 19:12 Ensemble Learning 13:58 Hyperparameter Tuning 23:22 Big Hyperparameter Tuning 18:34 Saving & Loading Our Model 06:34 Saving Predictions - Part 1 10:40 Saving Predictions - Part 2 08:26 Submitting to Kaggle 07:03 Winners Solutions 12:17 All Winners Solutions Links 00:12 Creating Weights & Biases Report Preview 39:17 How we can Improve ? 00:15 Detecting Wheat Heads 20 lectures • 3hr 51min What is Object Detection 06:01 Understanding Problem & Data 04:45 Setting up Notebook 11:20 Downloading & Importing all Libraries 14:55 Reading & Visualising Data and Images 20:15 Creating our Training & Validation Dataset 41:43 Creating the Configuration 20:07 Creating Custom Wandb Callback - Part 1 14:04 Creating Custom Wandb Callback - Part 2 15:19 Setting up Custom Wandb callback & Training the Model 08:43 Analysing The Results 08:33 Training Model for 1 Epoch 06:44 Analysing The Results Preview 05:20 Using Retinanet 06:10 Analysing The Results 04:53 Loading Model & Making Predictions on Single Image 13:59 Making Predictions on Test Dataset 20:24 Making Weights & Biases Report 00:10 Winners Solutions 07:03 How we can Improve ? 00:14 4 more sections Requirements Basic Python programming knowledge A Computer with Internet Connection All tools used in this course are free to use Description This Brand New and Modern Deep Learning & Computer Vision Course will teach you everything you will need to know to become a Computer Vision Expert. Deep Learning & Computer Vision is currently one of the most increasing fields of Artificial Intelligence and Companies like Google, Apple, Facebook, Amazon are highly investing in this field. Deep Learning & Computer Vision jobs are increasing day by day & provide some of the highest paying jobs all over the world. If We Want Machines to Think, We Need to Teach Them to See. -Fei Fei Li, Director of Stanford AI Lab and Stanford Vision Lab Computer Vision allows us to see the world & process digital images & videos to extract useful information to do a certain task from classification, object detection, and much more. Python is one of the most popular used programming language in Deep Learning and Computer Vision. All the tools, techniques & technologies used in this course - Learning Computer Vision & Deep Learning Fundamentals Setting up Anaconda , Installing Libraries & Jupyter Notebook Learning fundamentals of OpenCV & Numpy - Reading images, Colorspaces, Drawing & Callbacks Advanced OpenCV - Image Preprocessing, Geometrical transformations, Perspective transformations & affine transformations, image blending & pyramids, image gradients & thresholding, Canny Edge Detector and contours Working with videos in OpenCV - Using webcam, Haar Cascades & Object Detection , Lane Detection Deep Learning & How Neural Network Works? - Artificial neural networks, Convolution Neural Networks & Transfer Learning Image Classification - Plant leaf Classification Working on very recent Kaggle Competitions Using Google Colab & Kaggle Kernels Using the latest Tensorflow 2.0 & Keras Using Keras Data Generators & Data Argumentation Using Transfer Learning & Ensemble learning Using State of The Art Deep Learning Models Using GPU & TPU for Model Training Hyperparameter Tuning Using Weights & Biases for recording Deep Learning experimentations Saving & Loading Models Creating a Weights & Biases Report & Showcasing the Project! Object Detection - Wheat heads Detection Working on Kaggle Competitions, again! Using Facebook's Detectron2 for Object Detection Creating COCO Dataset from scratch Training Faster RCNN Model and Custom Weights & Biases callback Using Retinanet Saving & Loading Detectron2 models Generative Adversarial Networks - Creating Fake Leaf Images Learning How Generative Adversarial Networks works Using FastAI Creating & Training Generative Adversarial Networks Making Fake Images using GAN Making ML Web Application Getting started with Streamlit Creating an ML Web Application from scratch using Streamlit making a React Web Application Deploying ML Applications Learning how to use Cloud Services to Deploy Models & Applications Using Heroku Learning how to Open Source Projects on GitHub How to showcase your projects to impress boss & employees & Get Hired! A lot of bonus lectures! This is what included in the package All lecture codes are available for downloadable for free 110+ HD video lectures ( over 50 more to come very soon! ) Free support in course Q/A All videos with English captions available This course is for you if.. ... you want to learn the Latest Tools & Techniques used in Deep Learning & Computer Vision ... you want to get more experience to Win Kaggle Competitions ... you want to get started with Computer Vision to become a Computer Vision Engineer .. you are interested in learning Image Classification , Object Detection , Generative Adversarial Networks , Making & Deploying Machine Learning Applications Who this course is for: You want to become a Computer Vision Engineer & Get Hired Anyone who want to learn latest tools & techniques used in Computer Vision You are already a Programmer and what to extend your skills by learning Computer Vision Who want to learn new Tools & Techniques used in Computer Vision You want to get more experience for winning Kaggle Competitions Show more Show less Instructor Shubham Gupta Machine Learning Engineer 4.6 Instructor Rating 32 Reviews 46,482 Students 1 Course Hi there, Me Shubhamai Machine Learning Engineer • Instructor • Teaching Assistant & Mentor ZTM• ML Researcher @ AIcrowd • Love Space & Rocketry I am a Machine Learning Engineer & Instructor with having a lot of experience with Deep Learning, Computer Vision. Currently, I am been working as a Machine learning Teaching Assistant @ Zero to Mastery and Machine Learning Research Assistant @ AIcrowd . I started my journey as an ML Engineer when I discovered Arduino, one of the most popular microcontrollers that can be used anywhere like in hobby projects or in robotics. With programming c++ in Arduino. I started to question myself about how can I increase my software development skills. and that's where I discovered and started learning python & ML, self-taught. I completed many many courses. I then started building many projects and Then I started freelancing on Fiverr. I got so much experience working with companies and their projects, and go many reviews too like — - Amazing developer to work with. Great experience, he went above and beyond to create great code that is clean and efficient. Will work with this developer again! - scifundinc - Shubham is very keen to work on all the aspects mentioned. He takes his time to make the project appealing and accurate. He was very committed to the time limit given and delivered the project before the deadline. His work is commendable and I look forward to take up more projects from him. - swantikalabh and many many more... After that I started learning many more skills, like object detection & segmentation, using Detectron2, FastAI, and many advanced techniques in deep learning including working on many kaggle competitions. But after that, working for endless months, I finally published my first ever online course, The Complete Deep Learning & Computer Vision Course and started my carer as an Instructor. I made this course keeping in mind that this course is what i wanted when i started to learn Deep Learning & Computer Vision. And then, here we are... 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