# Data Science: Deep Learning and Neural Networks in Python

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

## Course Description

This course will get you started in building your FIRST **artificial neural network** using **deep learning** techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE.

We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called "**backpropagation**" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.

Next, we implement a neural network using Google's new TensorFlow library.

You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in **machine learning** and **data science** in general. We go beyond basic models like logistic regression and linear regression and I show you something that **automatically learns features**.

This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited.

Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture!

After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for.

NOTE:

If you *already* know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, **Data Science: Practical Deep Learning Concepts in Theano and TensorFlow**.

I have other courses that cover more advanced topics, such as **Convolutional Neural Networks**, **Restricted Boltzmann Machines**, **Autoencoders**, and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects.

This course focuses on "**how to build and understand**", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about **"seeing for yourself" via experimentation**. It will teach you how to visualize what's happening in the model internally. If you want **more** than just a superficial look at machine learning models, this course is for you.

"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

calculus (taking derivatives)

matrix arithmetic

probability

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations, loading a CSV file

Be familiar with basic linear models such as linear regression and logistic regression

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

## Who this course is for:

- Students interested in machine learning - you'll get all the tidbits you need to do well in a neural networks course
- Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks.

## Featured review

## Instructor

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer.

I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition.

Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark.

I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text.

My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing.

I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School.

Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

## Expected Outcomes

- Learn how Deep Learning REALLY works (not just some diagrams and magical black box code) Learn how a neural network is built from basic building blocks (the neuron) Code a neural network from scratch in Python and numpy Code a neural network using Google's TensorFlow Describe different types of neural networks and the different types of problems they are used for Derive the backpropagation rule from first principles Create a neural network with an output that has K > 2 classes using softmax Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward" Install TensorFlow Course content 14 sections • 89 lectures • 11h 9m total length Expand all sections Welcome 3 lectures • 17min Introduction and Outline Preview 06:32 Where to get the code 05:01 How to Succeed in this Course 05:51 Review 6 lectures • 31min Review Section Introduction 01:58 What does machine learning do? 05:28 Neuron Predictions 05:00 Neuron Training 08:47 Deep Learning Readiness Test 05:33 Review Section Summary 03:52 Preliminaries: From Neurons to Neural Networks 2 lectures • 13min Neural Networks with No Math 04:20 Introduction to the E-Commerce Course Project 08:52 Classifying more than 2 things at a time 15 lectures • 1hr 23min Prediction: Section Introduction and Outline 05:39 From Logistic Regression to Neural Networks 05:12 Interpreting the Weights of a Neural Network 08:05 Softmax 02:54 Sigmoid vs. Softmax 01:30 Feedforward in Slow-Mo (part 1) 19:42 Feedforward in Slow-Mo (part 2) 10:55 Where to get the code for this course 01:30 Softmax in Code 03:39 Building an entire feedforward neural network in Python 06:23 E-Commerce Course Project: Pre-Processing the Data 05:24 E-Commerce Course Project: Making Predictions 03:55 Prediction Quizzes 03:25 Prediction: Section Summary 01:45 Suggestion Box 03:03 Training a neural network 16 lectures • 2hr 14min Training: Section Introduction and Outline 02:49 What do all these symbols and letters mean? 09:45 What does it mean to "train" a neural network? 06:45 How to Brace Yourself to Learn Backpropagation 07:38 Categorical Cross-Entropy Loss Function 11:01 Training Logistic Regression with Softmax (part 1) 14:41 Training Logistic Regression with Softmax (part 2) 05:41 Backpropagation (part 1) 05:13 Backpropagation (part 2) 10:50 Backpropagation in code 17:07 Backpropagation (part 3) 16:12 The WRONG Way to Learn Backpropagation 03:52 E-Commerce Course Project: Training Logistic Regression with Softmax 08:11 E-Commerce Course Project: Training a Neural Network 06:19 Training Quiz 05:30 Training: Section Summary 02:41 Practical Machine Learning 10 lectures • 49min Practical Issues: Section Introduction and Outline 01:43 Donut and XOR Review 01:06 Donut and XOR Revisited 04:21 Neural Networks for Regression 11:38 Common nonlinearities and their derivatives 01:26 Practical Considerations for Choosing Activation Functions 07:45 Hyperparameters and Cross-Validation 04:10 Manually Choosing Learning Rate and Regularization Penalty 04:08 Why Divide by Square Root of D? 06:32 Practical Issues: Section Summary 06:10 TensorFlow, exercises, practice, and what to learn next 6 lectures • 53min TensorFlow plug-and-play example 19:18 Visualizing what a neural network has learned using TensorFlow Playground 11:35 Where to go from here 03:41 You know more than you think you know 04:52 How to get good at deep learning + exercises 05:07 Deep neural networks in just 3 lines of code with Sci-Kit Learn 08:49 Project: Facial Expression Recognition 8 lectures • 1hr 2min Facial Expression Recognition Project Introduction 04:51 Facial Expression Recognition Problem Description 12:21 The class imbalance problem 06:01 Utilities walkthrough 05:45 Facial Expression Recognition in Code (Binary / Sigmoid) 12:13 Facial Expression Recognition in Code (Logistic Regression Softmax) 08:57 Facial Expression Recognition in Code (ANN Softmax) 10:44 Facial Expression Recognition Project Summary 01:20 Backpropagation Supplementary Lectures 5 lectures • 31min Backpropagation Supplementary Lectures Introduction 01:03 Why Learn the Ins and Outs of Backpropagation? 08:53 Gradient Descent Tutorial 04:30 Help with Softmax Derivative 04:09 Backpropagation with Softmax Troubleshooting 11:55 Higher-Level Discussion 4 lectures • 39min What's the difference between "neural networks" and "deep learning"? 07:58 Who should take this course in 2020 and beyond? 08:48 Who should learn backpropagation in 2020 and beyond? Preview 11:18 Where does this course fit into your deep learning studies? 10:43 4 more sections Requirements Basic math (calculus derivatives, matrix arithmetic, probability) Install Numpy and Python Don't worry about installing TensorFlow, we will do that in the lectures. Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course Description This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called " backpropagation " using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features. Next, we implement a neural network using Google's new TensorFlow library. You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features . This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited. Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture! After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for. NOTE: If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow . I have other courses that cover more advanced topics, such as Convolutional Neural Networks , Restricted Boltzmann Machines , Autoencoders , and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects. This course focuses on " how to build and understand ", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation . It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: calculus (taking derivatives) matrix arithmetic probability Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file Be familiar with basic linear models such as linear regression and logistic regression WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) Who this course is for: Students interested in machine learning - you'll get all the tidbits you need to do well in a neural networks course Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks. Show more Show less Featured review Louis Chiu 90 courses 49 reviews Rating: 5.0 out of 5 a year ago Very good course for deep learning. Not just teaching the intuition and teach you how to use the api. But spend most of the time teaching the concept and derivation of the algorithm. Now I can really understand how I construct a neural network and without the api. However, as the tensorflow used in this course is really old, it may be better to take the tensorflow 2.0 course first. Show more Show less Instructor Lazy Programmer Inc. Artificial intelligence and machine learning engineer 4.6 Instructor Rating 115,455 Reviews 439,048 Students 29 Courses Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. 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:'6776fb74ede0dbeb',m:'d75df06d05c08d02a61f1a2f15d42b3ddac3e96c-1627736139-1800-AfyXMbJ3ORHocmmp8DmGF8rssWmrH9023JOiXdGFokeG/zTmAqdznVr9czNb3tojmrRTrtcdOaGrTplaJj6dmo0WmSEJIV+IUxle3KoOyix2tYrPN5wu2kBSwiJ0JKG3Gz73Qt18FasHNq8tUwvCeJ7SQQG5JnkxQUKonFZExOXR',s:[0xccbef4b9c9,0xeda111d492],}})();
- Learn how a neural network is built from basic building blocks (the neuron) Code a neural network from scratch in Python and numpy Code a neural network using Google's TensorFlow Describe different types of neural networks and the different types of problems they are used for Derive the backpropagation rule from first principles Create a neural network with an output that has K > 2 classes using softmax Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward" Install TensorFlow Course content 14 sections • 89 lectures • 11h 9m total length Expand all sections Welcome 3 lectures • 17min Introduction and Outline Preview 06:32 Where to get the code 05:01 How to Succeed in this Course 05:51 Review 6 lectures • 31min Review Section Introduction 01:58 What does machine learning do? 05:28 Neuron Predictions 05:00 Neuron Training 08:47 Deep Learning Readiness Test 05:33 Review Section Summary 03:52 Preliminaries: From Neurons to Neural Networks 2 lectures • 13min Neural Networks with No Math 04:20 Introduction to the E-Commerce Course Project 08:52 Classifying more than 2 things at a time 15 lectures • 1hr 23min Prediction: Section Introduction and Outline 05:39 From Logistic Regression to Neural Networks 05:12 Interpreting the Weights of a Neural Network 08:05 Softmax 02:54 Sigmoid vs. Softmax 01:30 Feedforward in Slow-Mo (part 1) 19:42 Feedforward in Slow-Mo (part 2) 10:55 Where to get the code for this course 01:30 Softmax in Code 03:39 Building an entire feedforward neural network in Python 06:23 E-Commerce Course Project: Pre-Processing the Data 05:24 E-Commerce Course Project: Making Predictions 03:55 Prediction Quizzes 03:25 Prediction: Section Summary 01:45 Suggestion Box 03:03 Training a neural network 16 lectures • 2hr 14min Training: Section Introduction and Outline 02:49 What do all these symbols and letters mean? 09:45 What does it mean to "train" a neural network? 06:45 How to Brace Yourself to Learn Backpropagation 07:38 Categorical Cross-Entropy Loss Function 11:01 Training Logistic Regression with Softmax (part 1) 14:41 Training Logistic Regression with Softmax (part 2) 05:41 Backpropagation (part 1) 05:13 Backpropagation (part 2) 10:50 Backpropagation in code 17:07 Backpropagation (part 3) 16:12 The WRONG Way to Learn Backpropagation 03:52 E-Commerce Course Project: Training Logistic Regression with Softmax 08:11 E-Commerce Course Project: Training a Neural Network 06:19 Training Quiz 05:30 Training: Section Summary 02:41 Practical Machine Learning 10 lectures • 49min Practical Issues: Section Introduction and Outline 01:43 Donut and XOR Review 01:06 Donut and XOR Revisited 04:21 Neural Networks for Regression 11:38 Common nonlinearities and their derivatives 01:26 Practical Considerations for Choosing Activation Functions 07:45 Hyperparameters and Cross-Validation 04:10 Manually Choosing Learning Rate and Regularization Penalty 04:08 Why Divide by Square Root of D? 06:32 Practical Issues: Section Summary 06:10 TensorFlow, exercises, practice, and what to learn next 6 lectures • 53min TensorFlow plug-and-play example 19:18 Visualizing what a neural network has learned using TensorFlow Playground 11:35 Where to go from here 03:41 You know more than you think you know 04:52 How to get good at deep learning + exercises 05:07 Deep neural networks in just 3 lines of code with Sci-Kit Learn 08:49 Project: Facial Expression Recognition 8 lectures • 1hr 2min Facial Expression Recognition Project Introduction 04:51 Facial Expression Recognition Problem Description 12:21 The class imbalance problem 06:01 Utilities walkthrough 05:45 Facial Expression Recognition in Code (Binary / Sigmoid) 12:13 Facial Expression Recognition in Code (Logistic Regression Softmax) 08:57 Facial Expression Recognition in Code (ANN Softmax) 10:44 Facial Expression Recognition Project Summary 01:20 Backpropagation Supplementary Lectures 5 lectures • 31min Backpropagation Supplementary Lectures Introduction 01:03 Why Learn the Ins and Outs of Backpropagation? 08:53 Gradient Descent Tutorial 04:30 Help with Softmax Derivative 04:09 Backpropagation with Softmax Troubleshooting 11:55 Higher-Level Discussion 4 lectures • 39min What's the difference between "neural networks" and "deep learning"? 07:58 Who should take this course in 2020 and beyond? 08:48 Who should learn backpropagation in 2020 and beyond? Preview 11:18 Where does this course fit into your deep learning studies? 10:43 4 more sections Requirements Basic math (calculus derivatives, matrix arithmetic, probability) Install Numpy and Python Don't worry about installing TensorFlow, we will do that in the lectures. Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course Description This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called " backpropagation " using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features. Next, we implement a neural network using Google's new TensorFlow library. You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features . This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited. Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture! After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for. NOTE: If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow . I have other courses that cover more advanced topics, such as Convolutional Neural Networks , Restricted Boltzmann Machines , Autoencoders , and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects. This course focuses on " how to build and understand ", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation . It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: calculus (taking derivatives) matrix arithmetic probability Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file Be familiar with basic linear models such as linear regression and logistic regression WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) Who this course is for: Students interested in machine learning - you'll get all the tidbits you need to do well in a neural networks course Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks. Show more Show less Featured review Louis Chiu 90 courses 49 reviews Rating: 5.0 out of 5 a year ago Very good course for deep learning. Not just teaching the intuition and teach you how to use the api. But spend most of the time teaching the concept and derivation of the algorithm. Now I can really understand how I construct a neural network and without the api. However, as the tensorflow used in this course is really old, it may be better to take the tensorflow 2.0 course first. Show more Show less Instructor Lazy Programmer Inc. Artificial intelligence and machine learning engineer 4.6 Instructor Rating 115,455 Reviews 439,048 Students 29 Courses Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. 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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- Code a neural network from scratch in Python and numpy Code a neural network using Google's TensorFlow Describe different types of neural networks and the different types of problems they are used for Derive the backpropagation rule from first principles Create a neural network with an output that has K > 2 classes using softmax Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward" Install TensorFlow Course content 14 sections • 89 lectures • 11h 9m total length Expand all sections Welcome 3 lectures • 17min Introduction and Outline Preview 06:32 Where to get the code 05:01 How to Succeed in this Course 05:51 Review 6 lectures • 31min Review Section Introduction 01:58 What does machine learning do? 05:28 Neuron Predictions 05:00 Neuron Training 08:47 Deep Learning Readiness Test 05:33 Review Section Summary 03:52 Preliminaries: From Neurons to Neural Networks 2 lectures • 13min Neural Networks with No Math 04:20 Introduction to the E-Commerce Course Project 08:52 Classifying more than 2 things at a time 15 lectures • 1hr 23min Prediction: Section Introduction and Outline 05:39 From Logistic Regression to Neural Networks 05:12 Interpreting the Weights of a Neural Network 08:05 Softmax 02:54 Sigmoid vs. Softmax 01:30 Feedforward in Slow-Mo (part 1) 19:42 Feedforward in Slow-Mo (part 2) 10:55 Where to get the code for this course 01:30 Softmax in Code 03:39 Building an entire feedforward neural network in Python 06:23 E-Commerce Course Project: Pre-Processing the Data 05:24 E-Commerce Course Project: Making Predictions 03:55 Prediction Quizzes 03:25 Prediction: Section Summary 01:45 Suggestion Box 03:03 Training a neural network 16 lectures • 2hr 14min Training: Section Introduction and Outline 02:49 What do all these symbols and letters mean? 09:45 What does it mean to "train" a neural network? 06:45 How to Brace Yourself to Learn Backpropagation 07:38 Categorical Cross-Entropy Loss Function 11:01 Training Logistic Regression with Softmax (part 1) 14:41 Training Logistic Regression with Softmax (part 2) 05:41 Backpropagation (part 1) 05:13 Backpropagation (part 2) 10:50 Backpropagation in code 17:07 Backpropagation (part 3) 16:12 The WRONG Way to Learn Backpropagation 03:52 E-Commerce Course Project: Training Logistic Regression with Softmax 08:11 E-Commerce Course Project: Training a Neural Network 06:19 Training Quiz 05:30 Training: Section Summary 02:41 Practical Machine Learning 10 lectures • 49min Practical Issues: Section Introduction and Outline 01:43 Donut and XOR Review 01:06 Donut and XOR Revisited 04:21 Neural Networks for Regression 11:38 Common nonlinearities and their derivatives 01:26 Practical Considerations for Choosing Activation Functions 07:45 Hyperparameters and Cross-Validation 04:10 Manually Choosing Learning Rate and Regularization Penalty 04:08 Why Divide by Square Root of D? 06:32 Practical Issues: Section Summary 06:10 TensorFlow, exercises, practice, and what to learn next 6 lectures • 53min TensorFlow plug-and-play example 19:18 Visualizing what a neural network has learned using TensorFlow Playground 11:35 Where to go from here 03:41 You know more than you think you know 04:52 How to get good at deep learning + exercises 05:07 Deep neural networks in just 3 lines of code with Sci-Kit Learn 08:49 Project: Facial Expression Recognition 8 lectures • 1hr 2min Facial Expression Recognition Project Introduction 04:51 Facial Expression Recognition Problem Description 12:21 The class imbalance problem 06:01 Utilities walkthrough 05:45 Facial Expression Recognition in Code (Binary / Sigmoid) 12:13 Facial Expression Recognition in Code (Logistic Regression Softmax) 08:57 Facial Expression Recognition in Code (ANN Softmax) 10:44 Facial Expression Recognition Project Summary 01:20 Backpropagation Supplementary Lectures 5 lectures • 31min Backpropagation Supplementary Lectures Introduction 01:03 Why Learn the Ins and Outs of Backpropagation? 08:53 Gradient Descent Tutorial 04:30 Help with Softmax Derivative 04:09 Backpropagation with Softmax Troubleshooting 11:55 Higher-Level Discussion 4 lectures • 39min What's the difference between "neural networks" and "deep learning"? 07:58 Who should take this course in 2020 and beyond? 08:48 Who should learn backpropagation in 2020 and beyond? Preview 11:18 Where does this course fit into your deep learning studies? 10:43 4 more sections Requirements Basic math (calculus derivatives, matrix arithmetic, probability) Install Numpy and Python Don't worry about installing TensorFlow, we will do that in the lectures. Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course Description This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called " backpropagation " using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features. Next, we implement a neural network using Google's new TensorFlow library. You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features . This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited. Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture! After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for. NOTE: If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow . I have other courses that cover more advanced topics, such as Convolutional Neural Networks , Restricted Boltzmann Machines , Autoencoders , and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects. This course focuses on " how to build and understand ", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation . It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: calculus (taking derivatives) matrix arithmetic probability Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file Be familiar with basic linear models such as linear regression and logistic regression WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) Who this course is for: Students interested in machine learning - you'll get all the tidbits you need to do well in a neural networks course Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks. Show more Show less Featured review Louis Chiu 90 courses 49 reviews Rating: 5.0 out of 5 a year ago Very good course for deep learning. Not just teaching the intuition and teach you how to use the api. But spend most of the time teaching the concept and derivation of the algorithm. Now I can really understand how I construct a neural network and without the api. However, as the tensorflow used in this course is really old, it may be better to take the tensorflow 2.0 course first. Show more Show less Instructor Lazy Programmer Inc. Artificial intelligence and machine learning engineer 4.6 Instructor Rating 115,455 Reviews 439,048 Students 29 Courses Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. 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- Code a neural network using Google's TensorFlow Describe different types of neural networks and the different types of problems they are used for Derive the backpropagation rule from first principles Create a neural network with an output that has K > 2 classes using softmax Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward" Install TensorFlow Course content 14 sections • 89 lectures • 11h 9m total length Expand all sections Welcome 3 lectures • 17min Introduction and Outline Preview 06:32 Where to get the code 05:01 How to Succeed in this Course 05:51 Review 6 lectures • 31min Review Section Introduction 01:58 What does machine learning do? 05:28 Neuron Predictions 05:00 Neuron Training 08:47 Deep Learning Readiness Test 05:33 Review Section Summary 03:52 Preliminaries: From Neurons to Neural Networks 2 lectures • 13min Neural Networks with No Math 04:20 Introduction to the E-Commerce Course Project 08:52 Classifying more than 2 things at a time 15 lectures • 1hr 23min Prediction: Section Introduction and Outline 05:39 From Logistic Regression to Neural Networks 05:12 Interpreting the Weights of a Neural Network 08:05 Softmax 02:54 Sigmoid vs. Softmax 01:30 Feedforward in Slow-Mo (part 1) 19:42 Feedforward in Slow-Mo (part 2) 10:55 Where to get the code for this course 01:30 Softmax in Code 03:39 Building an entire feedforward neural network in Python 06:23 E-Commerce Course Project: Pre-Processing the Data 05:24 E-Commerce Course Project: Making Predictions 03:55 Prediction Quizzes 03:25 Prediction: Section Summary 01:45 Suggestion Box 03:03 Training a neural network 16 lectures • 2hr 14min Training: Section Introduction and Outline 02:49 What do all these symbols and letters mean? 09:45 What does it mean to "train" a neural network? 06:45 How to Brace Yourself to Learn Backpropagation 07:38 Categorical Cross-Entropy Loss Function 11:01 Training Logistic Regression with Softmax (part 1) 14:41 Training Logistic Regression with Softmax (part 2) 05:41 Backpropagation (part 1) 05:13 Backpropagation (part 2) 10:50 Backpropagation in code 17:07 Backpropagation (part 3) 16:12 The WRONG Way to Learn Backpropagation 03:52 E-Commerce Course Project: Training Logistic Regression with Softmax 08:11 E-Commerce Course Project: Training a Neural Network 06:19 Training Quiz 05:30 Training: Section Summary 02:41 Practical Machine Learning 10 lectures • 49min Practical Issues: Section Introduction and Outline 01:43 Donut and XOR Review 01:06 Donut and XOR Revisited 04:21 Neural Networks for Regression 11:38 Common nonlinearities and their derivatives 01:26 Practical Considerations for Choosing Activation Functions 07:45 Hyperparameters and Cross-Validation 04:10 Manually Choosing Learning Rate and Regularization Penalty 04:08 Why Divide by Square Root of D? 06:32 Practical Issues: Section Summary 06:10 TensorFlow, exercises, practice, and what to learn next 6 lectures • 53min TensorFlow plug-and-play example 19:18 Visualizing what a neural network has learned using TensorFlow Playground 11:35 Where to go from here 03:41 You know more than you think you know 04:52 How to get good at deep learning + exercises 05:07 Deep neural networks in just 3 lines of code with Sci-Kit Learn 08:49 Project: Facial Expression Recognition 8 lectures • 1hr 2min Facial Expression Recognition Project Introduction 04:51 Facial Expression Recognition Problem Description 12:21 The class imbalance problem 06:01 Utilities walkthrough 05:45 Facial Expression Recognition in Code (Binary / Sigmoid) 12:13 Facial Expression Recognition in Code (Logistic Regression Softmax) 08:57 Facial Expression Recognition in Code (ANN Softmax) 10:44 Facial Expression Recognition Project Summary 01:20 Backpropagation Supplementary Lectures 5 lectures • 31min Backpropagation Supplementary Lectures Introduction 01:03 Why Learn the Ins and Outs of Backpropagation? 08:53 Gradient Descent Tutorial 04:30 Help with Softmax Derivative 04:09 Backpropagation with Softmax Troubleshooting 11:55 Higher-Level Discussion 4 lectures • 39min What's the difference between "neural networks" and "deep learning"? 07:58 Who should take this course in 2020 and beyond? 08:48 Who should learn backpropagation in 2020 and beyond? Preview 11:18 Where does this course fit into your deep learning studies? 10:43 4 more sections Requirements Basic math (calculus derivatives, matrix arithmetic, probability) Install Numpy and Python Don't worry about installing TensorFlow, we will do that in the lectures. Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course Description This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called " backpropagation " using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features. Next, we implement a neural network using Google's new TensorFlow library. You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features . This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited. Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture! After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for. NOTE: If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow . I have other courses that cover more advanced topics, such as Convolutional Neural Networks , Restricted Boltzmann Machines , Autoencoders , and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects. This course focuses on " how to build and understand ", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation . It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: calculus (taking derivatives) matrix arithmetic probability Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file Be familiar with basic linear models such as linear regression and logistic regression WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) Who this course is for: Students interested in machine learning - you'll get all the tidbits you need to do well in a neural networks course Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks. Show more Show less Featured review Louis Chiu 90 courses 49 reviews Rating: 5.0 out of 5 a year ago Very good course for deep learning. Not just teaching the intuition and teach you how to use the api. But spend most of the time teaching the concept and derivation of the algorithm. Now I can really understand how I construct a neural network and without the api. However, as the tensorflow used in this course is really old, it may be better to take the tensorflow 2.0 course first. Show more Show less Instructor Lazy Programmer Inc. Artificial intelligence and machine learning engineer 4.6 Instructor Rating 115,455 Reviews 439,048 Students 29 Courses Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. 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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- Describe different types of neural networks and the different types of problems they are used for Derive the backpropagation rule from first principles Create a neural network with an output that has K > 2 classes using softmax Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward" Install TensorFlow Course content 14 sections • 89 lectures • 11h 9m total length Expand all sections Welcome 3 lectures • 17min Introduction and Outline Preview 06:32 Where to get the code 05:01 How to Succeed in this Course 05:51 Review 6 lectures • 31min Review Section Introduction 01:58 What does machine learning do? 05:28 Neuron Predictions 05:00 Neuron Training 08:47 Deep Learning Readiness Test 05:33 Review Section Summary 03:52 Preliminaries: From Neurons to Neural Networks 2 lectures • 13min Neural Networks with No Math 04:20 Introduction to the E-Commerce Course Project 08:52 Classifying more than 2 things at a time 15 lectures • 1hr 23min Prediction: Section Introduction and Outline 05:39 From Logistic Regression to Neural Networks 05:12 Interpreting the Weights of a Neural Network 08:05 Softmax 02:54 Sigmoid vs. Softmax 01:30 Feedforward in Slow-Mo (part 1) 19:42 Feedforward in Slow-Mo (part 2) 10:55 Where to get the code for this course 01:30 Softmax in Code 03:39 Building an entire feedforward neural network in Python 06:23 E-Commerce Course Project: Pre-Processing the Data 05:24 E-Commerce Course Project: Making Predictions 03:55 Prediction Quizzes 03:25 Prediction: Section Summary 01:45 Suggestion Box 03:03 Training a neural network 16 lectures • 2hr 14min Training: Section Introduction and Outline 02:49 What do all these symbols and letters mean? 09:45 What does it mean to "train" a neural network? 06:45 How to Brace Yourself to Learn Backpropagation 07:38 Categorical Cross-Entropy Loss Function 11:01 Training Logistic Regression with Softmax (part 1) 14:41 Training Logistic Regression with Softmax (part 2) 05:41 Backpropagation (part 1) 05:13 Backpropagation (part 2) 10:50 Backpropagation in code 17:07 Backpropagation (part 3) 16:12 The WRONG Way to Learn Backpropagation 03:52 E-Commerce Course Project: Training Logistic Regression with Softmax 08:11 E-Commerce Course Project: Training a Neural Network 06:19 Training Quiz 05:30 Training: Section Summary 02:41 Practical Machine Learning 10 lectures • 49min Practical Issues: Section Introduction and Outline 01:43 Donut and XOR Review 01:06 Donut and XOR Revisited 04:21 Neural Networks for Regression 11:38 Common nonlinearities and their derivatives 01:26 Practical Considerations for Choosing Activation Functions 07:45 Hyperparameters and Cross-Validation 04:10 Manually Choosing Learning Rate and Regularization Penalty 04:08 Why Divide by Square Root of D? 06:32 Practical Issues: Section Summary 06:10 TensorFlow, exercises, practice, and what to learn next 6 lectures • 53min TensorFlow plug-and-play example 19:18 Visualizing what a neural network has learned using TensorFlow Playground 11:35 Where to go from here 03:41 You know more than you think you know 04:52 How to get good at deep learning + exercises 05:07 Deep neural networks in just 3 lines of code with Sci-Kit Learn 08:49 Project: Facial Expression Recognition 8 lectures • 1hr 2min Facial Expression Recognition Project Introduction 04:51 Facial Expression Recognition Problem Description 12:21 The class imbalance problem 06:01 Utilities walkthrough 05:45 Facial Expression Recognition in Code (Binary / Sigmoid) 12:13 Facial Expression Recognition in Code (Logistic Regression Softmax) 08:57 Facial Expression Recognition in Code (ANN Softmax) 10:44 Facial Expression Recognition Project Summary 01:20 Backpropagation Supplementary Lectures 5 lectures • 31min Backpropagation Supplementary Lectures Introduction 01:03 Why Learn the Ins and Outs of Backpropagation? 08:53 Gradient Descent Tutorial 04:30 Help with Softmax Derivative 04:09 Backpropagation with Softmax Troubleshooting 11:55 Higher-Level Discussion 4 lectures • 39min What's the difference between "neural networks" and "deep learning"? 07:58 Who should take this course in 2020 and beyond? 08:48 Who should learn backpropagation in 2020 and beyond? Preview 11:18 Where does this course fit into your deep learning studies? 10:43 4 more sections Requirements Basic math (calculus derivatives, matrix arithmetic, probability) Install Numpy and Python Don't worry about installing TensorFlow, we will do that in the lectures. Being familiar with the content of my logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) will give you the proper context for this course Description This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training method called " backpropagation " using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features. Next, we implement a neural network using Google's new TensorFlow library. You should take this course if you are interested in starting your journey toward becoming a master at deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and I show you something that automatically learns features . This course provides you with many practical examples so that you can really see how deep learning can be used on anything. Throughout the course, we'll do a course project, which will show you how to predict user actions on a website given user data like whether or not that user is on a mobile device, the number of products they viewed, how long they stayed on your site, whether or not they are a returning visitor, and what time of day they visited. Another project at the end of the course shows you how you can use deep learning for facial expression recognition. Imagine being able to predict someone's emotions just based on a picture! After getting your feet wet with the fundamentals, I provide a brief overview of some of the newest developments in neural networks - slightly modified architectures and what they are used for. NOTE: If you already know about softmax and backpropagation, and you want to skip over the theory and speed things up using more advanced techniques along with GPU-optimization, check out my follow-up course on this topic, Data Science: Practical Deep Learning Concepts in Theano and TensorFlow . I have other courses that cover more advanced topics, such as Convolutional Neural Networks , Restricted Boltzmann Machines , Autoencoders , and more! But you want to be very comfortable with the material in this course before moving on to more advanced subjects. This course focuses on " how to build and understand ", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation . It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you. "If you can't implement it, you don't understand it" Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand". My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code? After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times... Suggested Prerequisites: calculus (taking derivatives) matrix arithmetic probability Python coding: if/else, loops, lists, dicts, sets Numpy coding: matrix and vector operations, loading a CSV file Be familiar with basic linear models such as linear regression and logistic regression WHAT ORDER SHOULD I TAKE YOUR COURSES IN?: Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course) Who this course is for: Students interested in machine learning - you'll get all the tidbits you need to do well in a neural networks course Professionals who want to use neural networks in their machine learning and data science pipeline. Be able to apply more powerful models, and know its drawbacks. Show more Show less Featured review Louis Chiu 90 courses 49 reviews Rating: 5.0 out of 5 a year ago Very good course for deep learning. Not just teaching the intuition and teach you how to use the api. But spend most of the time teaching the concept and derivation of the algorithm. Now I can really understand how I construct a neural network and without the api. However, as the tensorflow used in this course is really old, it may be better to take the tensorflow 2.0 course first. Show more Show less Instructor Lazy Programmer Inc. Artificial intelligence and machine learning engineer 4.6 Instructor Rating 115,455 Reviews 439,048 Students 29 Courses Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more. 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- Derive the backpropagation rule from first principles Create a neural network with an output that has K > 2 classes using softmax Describe the various terms related to neural networks, such as "activation", "backpropagation" and "feedforward" Install TensorFlow Course content 14 sections • 89 lectures • 11h 9m total length Expand all sections Welcome 3 lectures • 17min Introduction and Outline Preview 06:32 Where to get the code 05:01 How to Succeed in this Course 05:51 Review 6 lectures • 31min Review Section Introduction 01:58 What does machine learning do? 05:28 Neuron Predictions 05:00 Neuron Training 08:47 Deep Learning Readiness Test 05:33 Review Section Summary 03:52 Preliminaries: From Neurons to Neural Networks 2 lectures • 13min Neural Networks with No Math 04:20 Introduction to the E-Commerce Course Project 08:52 Classifying more than 2 things at a time 15 lectures • 1hr 23min Prediction: Section Introduction and Outline 05:39 From Logistic Regression to Neural Networks 05:12 Interpreting the Weights of a Neural Network 08:05 Softmax 02:54 Sigmoid vs. Softmax 01:30 Feedforward in Slow-Mo (part 1) 19:42 Feedforward in Slow-Mo (part 2) 10:55 Where to get the code for this course 01:30 Softmax in Code 03:39 Building an entire feedforward neural network in Python 06:23 E-Commerce Course Project: Pre-Processing the Data 05:24 E-Commerce Course Project: Making Predictions 03:55 Prediction Quizzes 03:25 Prediction: Section Summary 01:45 Suggestion Box 03:03 Training a neural network 16 lectures • 2hr 14min Training: Section Introduction and Outline 02:49 What do all these symbols and letters mean? 09:45 What does it mean to "train" a neural network? 06:45 How to Brace Yourself to Learn Backpropagation 07:38 Categorical Cross-Entropy Loss Function 11:01 Training Logistic Regression with Softmax (part 1) 14:41 Training Logistic Regression with Softmax (part 2) 05:41 Backpropagation (part 1) 05:13 Backpropagation (part 2) 10:50 Backpropagation in code 17:07 Backpropagation (part 3) 16:12 The WRONG Way to Learn Backpropagation 03:52 E-Commerce Course Project: Training Logistic Regression with Softmax 08:11 E-Commerce Course Project: Training a Neural Network 06:19 Training Quiz 05:30 Training: Section Summary 02:41 Practical Machine Learning 10