Module 1 Section 1 - Welcome to the course
Unit 1 What is Deep Learning?
Unit 2 Installing Python
Unit 3 How to get the dataset
Module 2 Section 2 - Part 1 - Artificial Neural Networks
Unit 1 Welcome to Part 1 - Artificial Neural Networks
Module 3 Section 3 - ANN Intuition
Unit 1 Plan of Attack
Unit 2 The Neuron
Unit 3 The Activation Function
Unit 4 How do Neural Networks work?
Unit 5 How do Neural Networks learn?
Unit 6 Gradient Descent
Unit 7 Stochastic Gradient Descent
Unit 8 Backpropagation
Module 4 Section 4 - Building an ANN
Unit 1 Prerequisites
Unit 2 How to get the dataset
Unit 3 Business Problem Description
Unit 4 Building an ANN - Step 1
Unit 5 Building an ANN - Step 2
Unit 6 Building an ANN - Step 3
Unit 7 Building an ANN - Step 4
Unit 8 Building an ANN - Step 5
Unit 9 Building an ANN - Step 6
Unit 10 Building an ANN - Step 7
Unit 11 Building an ANN - Step 8
Unit 12 Building an ANN - Step 9
Unit 13 Building an ANN - Step 10
Module 5 Section 5 - Homework Challenge - Should we say goodbye to that customer ?
Unit 1 Homework Instruction
Unit 2 Homework Solution
Module 6 Section 6 - Evaluating, Improving and Tuning the ANN
Unit 1 Evaluating the ANN
Unit 2 Improving the ANN
Unit 3 Tuning the ANN
Module 7 Section 7 - Homework Challenge - Put me one step down on the podium
Unit 1 Homework Instruction
Module 8 Section 8 - Part 2 - Convolutional Neural Networks
Unit 1 Welcome to Part 2 - Convolutional Neural Networks
Module 9 Section 9 - CNN Intuition
Unit 1 Plan of attack
Unit 2 What are convolutional neural networks?
Unit 3 Step 1 - Convolution Operation
Unit 4 Step 1(b) - ReLU Layer
Unit 5 Step 2 - Pooling
Unit 6 Step 3 - Flattening
Unit 7 Step 4 - Full Connection
Unit 8 Summary
Unit 9 Softmax & Cross-Entropy
Module 10 Section 10 - Building a CNN
Unit 1 How to get the dataset
Unit 2 Introduction to CNNs
Unit 3 Building a CNN - Step 1
Unit 4 Building a CNN - Step 2
Unit 5 Building a CNN - Step 3
Unit 6 Building a CNN - Step 4
Unit 7 Building a CNN - Step 5
Unit 8 Building a CNN - Step 6
Unit 9 Building a CNN - Step 7
Unit 10 Building a CNN - Step 8
Unit 11 Building a CNN - Step 9
Unit 12 Building a CNN - Step 10
Module 11 Section 11 - Homework - What's that pet ?
Unit 1 Homework Instruction
Unit 2 Homework Solution
Module 12 Section 12 - Evaluating, Improving and Tuning the CNN
Unit 1 Homework Challenge - Get the gold medal
Module 13 Section 13 - Part 3 - Recurrent Neural Networks
Unit 1 Welcome to Part 3 - Recurrent Neural Networks
Module 14 Section 14 - RNN Intuition
Unit 1 Plan of attack
Unit 2 The idea behind Recurrent Neural Networks
Unit 3 The Vanishing Gradient Problem
Unit 4 LSTMs
Unit 5 Practical intuition
Unit 6 EXTRA: LSTM Variations
Module 15 Section 15 - Building a RNN
Unit 1 Ethical Disclosure
Unit 2 How to get the dataset
Unit 3 Building a RNN - Step 1
Unit 4 Building a RNN - Step 2
Unit 5 Building a RNN - Step 3
Unit 6 Building a RNN - Step 4
Unit 7 Building a RNN - Step 5
Unit 8 Building a RNN - Step 6
Unit 9 Building a RNN - Step 7
Unit 10 Building a RNN - Step 8
Unit 11 Building a RNN - Step 9
Unit 12 Building a RNN - Step 10
Unit 13 Building a RNN - Step 11
Unit 14 Building a RNN - Step 12
Unit 15 Building a RNN - Step 13
Unit 16 Building a RNN - Step 14
Unit 17 Building a RNN - Step 15
Module 16 Section 16 - Evaluating, Improving and Tuning the RNN
Unit 1 Evaluating the RNN
Unit 2 Improving and Tuning the RNN - Homework Challenge
Unit 3 Improving and Tuning the RNN - Solution
Module 17 Section 17 - Part 4 - Self Organizing MapsEvaluating, Improving and Tuning the RNN
Unit 1 Welcome to Part 4 - Self Organizing Maps
Module 18 Section 18 - SOMs Intuition
Unit 1 Plan of attack
Unit 2 How do Self-Organizing Maps Work?
Unit 3 Why revisit K-Means?
Unit 4 K-Means Clustering (Refresher)
Unit 5 How do Self-Organizing Maps Learn? (Part 1)
Unit 6 How do Self-Organizing Maps Learn? (Part 2)
Unit 7 Live SOM example
Unit 8 Reading an Advanced SOM
Unit 9 EXTRA: K-means Clustering (part 2)
Unit 10 EXTRA: K-means Clustering (part 3)
Module 19 Section 19 - Building a SOM
Unit 1 How to get the dataset
Unit 2 Building a SOM - Step 1
Unit 3 Building a SOM - Step 2
Unit 4 Building a SOM - Step 3
Unit 5 Building a SOM - Step 4
Module 20 Section 20- Mega Case Study
Unit 1 Mega Case Study - Step 1
Unit 2 Mega Case Study - Step 2
Unit 3 Mega Case Study - Step 3
Unit 4 Mega Case Study - Step 4
Module 21 Section 21 - Part 5 - Boltzmann Machines
Unit 1 Welcome to Part 5 - Boltzmann Machines
Module 22 Section 22 - Boltzmann Machine Intuition
Unit 1 Plan of attack
Unit 2 Boltzmann Machine
Unit 3 Energy-Based Models (EBM)
Unit 4 Editing Wikipedia - Our Contribution to the World
Unit 5  Restricted Boltzmann Machine
Unit 6 Contrastive Divergence
Unit 7 Deep Belief Networks
Unit 8 Deep Boltzmann Machines
Module 23 Section 23 - Data Preprocessing TemplateBuilding a Boltzmann Mac
Unit 1 How to get the dataset
Unit 2 Installing PyTorch
Unit 3 Building a Boltzmann Machine - Introduction
Unit 4 Same Data Preprocessing in Parts 5 and 6
Unit 5 Building a Boltzmann Machine - Step 1
Unit 6 Building a Boltzmann Machine - Step 2
Unit 7 Building a Boltzmann Machine - Step 3
Unit 8 Building a Boltzmann Machine - Step 4
Unit 9 Building a Boltzmann Machine - Step 5
Unit 10 Building a Boltzmann Machine - Step 6
Unit 11 Building a Boltzmann Machine - Step 7
Unit 12 Building a Boltzmann Machine - Step 8
Unit 13 Building a Boltzmann Machine - Step 9
Unit 14 Building a Boltzmann Machine - Step 10
Unit 15 Building a Boltzmann Machine - Step 11
Unit 16 Building a Boltzmann Machine - Step 12
Unit 17 Building a Boltzmann Machine - Step 13
Unit 18 Building a Boltzmann Machine - Step 14
Module 24 Section 24 - Part 6 - AutoEncoders
Unit 1 Welcome to Part 6 - AutoEncoders
Module 25 Section 25 - AutoEncoders Intuition
Unit 1 Plan of attack
Unit 2 Auto Encoders
Unit 3 A Note on Biases
Unit 4 Training an Auto Encoder
Unit 5 Overcomplete hidden layers
Unit 6 Sparse Autoencoders
Unit 7 Denoising Autoencoders
Unit 8 Contractive Autoencoders
Unit 9 Stacked Autoencoders
Unit 10 Deep Autoencoders
Module 26 Section 26 - Building an AutoEncoder
Unit 1 How to get the dataset
Unit 2 Installing PyTorch
Unit 3 Same Data Preprocessing in Parts 5 and 6
Unit 4 Building an AutoEncoder - Step 1
Unit 5 Building an AutoEncoder - Step 2
Unit 6 Building an AutoEncoder - Step 3
Unit 7 Homework Challenge - Coding Exercise
Unit 8 Building an AutoEncoder - Step 4
Unit 9 Building an AutoEncoder - Step 5
Unit 10 Building an AutoEncoder - Step 6
Unit 11 Building an AutoEncoder - Step 7
Unit 12 Building an AutoEncoder - Step 8
Unit 13 Building an AutoEncoder - Step 9
Unit 14 Building an AutoEncoder - Step 10
Unit 15 Building an AutoEncoder - Step 11
Module 27 Section: 27 - Annex - Get the Machine Learning Basics
Unit 1 Annex - Get the Machine Learning Basics
Module 28 Section: 28 - Annex - Get the Machine Learning Basics
Unit 1 Simple Linear Regression Intuition - Step 1
Unit 2 Simple Linear Regression Intuition - Step 2
Unit 3 Multiple Linear Regression Intuition
Unit 4 Logistic Regression Intuition
Module 29 Section 29 - Data Preprocessing Template
Unit 1 Data Preprocessing - Step 1
Unit 2 Data Preprocessing - Step 2
Unit 3 Data Preprocessing - Step 3
Unit 4 Data Preprocessing - Step 4
Unit 5 Data Preprocessing - Step 5
Unit 6 Data Preprocessing - Step 6
Unit 7 Data Preprocessing Template
Module 30 Section 30 - Classification Template
Unit 1 Logistic Regression Implementation - Step 1
Unit 2 Logistic Regression Implementation - Step 2
Unit 3 Logistic Regression Implementation - Step 3
Unit 4 Logistic Regression Implementation - Step 4
Unit 5 Logistic Regression Implementation - Step 5
Unit 6 Classification Template
Module 31 Unpublished Section - Early Bird Bonus Lectures
There are no units in this module.
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Deep Learning A-Z


Course Description

*** As seen on Kickstarter ***
Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go – a game where intuition plays a key role.
But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence.
— Why Deep Learning A-Z? —
Here are five reasons we think Deep Learning A-Z™ really is different, and stands out from the crowd of other training programs out there:

The first and most important thing we focused on is giving the course a robust structure. Deep Learning is very broad and complex and to navigate this maze you need a clear and global vision of it.
That's why we grouped the tutorials into two volumes, representing the two fundamental branches of Deep Learning: Supervised Deep Learning and Unsupervised Deep Learning. With each volume focusing on three distinct algorithms, we found that this is the best structure for mastering Deep Learning.

So many courses and books just bombard you with the theory, and math, and coding… But they forget to explain, perhaps, the most important part: why you are doing what you are doing. And that's how this course is so different. We focus on developing an intuitive *feel* for the concepts behind Deep Learning algorithms.
With our intuition tutorials you will be confident that you understand all the techniques on an instinctive level. And once you proceed to the hands-on coding exercises you will see for yourself how much more meaningful your experience will be. This is a game-changer.

Are you tired of courses based on over-used, outdated data sets?
Yes? Well then you're in for a treat.
Inside this class we will work on Real-World datasets, to solve Real-World business problems. (Definitely not the boring iris or digit classification datasets that we see in every course). In this course we will solve six real-world challenges:
• Artificial Neural Networks to solve a Customer Churn problem
• Convolutional Neural Networks for Image Recognition
• Recurrent Neural Networks to predict Stock Prices
• Self-Organizing Maps to investigate Fraud
• Boltzmann Machines to create a Recomender System
• Stacked Autoencoders* to take on the challenge for the Netflix $1 Million prize
*Stacked Autoencoders is a brand new technique in Deep Learning which didn't even exist a couple of years ago. We haven't seen this method explained anywhere else in sufficient depth.

In Deep Learning A-Z™ we code together with you. Every practical tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means.
In addition, we will purposefully structure the code in such a way so that you can download it and apply it in your own projects. Moreover, we explain step-by-step where and how to modify the code to insert YOUR dataset, to tailor the algorithm to your needs, to get the output that you are after.
This is a course which naturally extends into your career.
Have you ever taken a course or read a book where you have questions but cannot reach the author?
Well, this course is different. We are fully committed to making this the most disruptive and powerful Deep Learning course on the planet. With that comes a responsibility to constantly be there when you need our help.
In fact, since we physically also need to eat and sleep we have put together a team of professional Data Scientists to help us out. Whenever you ask a question you will get a response from us within 48 hours maximum.

No matter how complex your query, we will be there. The bottom line is we want you to succeed.
— The Tools —
Tensorflow and Pytorch are the two most popular open-source libraries for Deep Learning. In this course you will learn both!
TensorFlow was developed by Google and is used in their speech recognition system, in the new google photos product, gmail, google search and much more. Companies using Tensorflow include AirBnb, Airbus, Ebay, Intel, Uber and dozens more.
PyTorch is as just as powerful and is being developed by researchers at Nvidia and leading universities: Stanford, Oxford, ParisTech. Companies using PyTorch include Twitter, Saleforce and Facebook.
So which is better and for what?
Well, in this course you will have an opportunity to work with both and understand when Tensorflow is better and when PyTorch is the way to go. Throughout the tutorials we compare the two and give you tips and ideas on which could work best in certain circumstances.
The interesting thing is that both these libraries are barely over 1 year old. That's what we mean when we say that in this course we teach you the most cutting edge Deep Learning models and techniques.

— More Tools —
Theano is another open source deep learning library. It's very similar to Tensorflow in its functionality, but nevertheless we will still cover it.
Keras is an incredible library to implement Deep Learning models. It acts as a wrapper for Theano and Tensorflow. Thanks to Keras we can create powerful and complex Deep Learning models with only a few lines of code. This is what will allow you to have a global vision of what you are creating. Everything you make will look so clear and structured thanks to this library, that you will really get the intuition and understanding of what you are doing.
— Even More Tools —
Scikit-learn the most practical Machine Learning library. We will mainly use it:
• to evaluate the performance of our models with the most relevant technique, k-Fold Cross Validation
• to improve our models with effective Parameter Tuning
• to preprocess our data, so that our models can learn in the best conditions
And of course, we have to mention the usual suspects. This whole course is based on Python and in every single section you will be getting hours and hours of invaluable hands-on practical coding experience.
Plus, throughout the course we will be using Numpy to do high computations and manipulate high dimensional arrays, Matplotlib to plot insightful charts and Pandas to import and manipulate datasets the most efficiently.

— Who Is This Course For? —
As you can see, there are lots of different tools in the space of Deep Learning and in this course we make sure to show you the most important and most progressive ones so that when you're done with Deep Learning A-Z™ your skills are on the cutting edge of today's technology.
If you are just starting out into Deep Learning, then you will find this course extremely useful. Deep Learning A-Z™ is structured around special coding blueprint approaches meaning that you won't get bogged down in unnecessary programming or mathematical complexities and instead you will be applying Deep Learning techniques from very early on in the course. You will build your knowledge from the ground up and you will see how with every tutorial you are getting more and more confident.
If you already have experience with Deep Learning, you will find this course refreshing, inspiring and very practical. Inside Deep Learning A-Z™ you will master some of the most cutting-edge Deep Learning algorithms and techniques (some of which didn't even exist a year ago) and through this course you will gain an immense amount of valuable hands-on experience with real-world business challenges. Plus, inside you will find inspiration to explore new Deep Learning skills and applications.

— Real-World Case Studies —
Mastering Deep Learning is not just about knowing the intuition and tools, it's also about being able to apply these models to real-world scenarios and derive actual measurable results for the business or project. That's why in this course we are introducing six exciting challenges:

#1 Churn Modelling Problem
In this part you will be solving a data analytics challenge for a bank. You will be given a dataset with a large sample of the bank's customers. To make this dataset, the bank gathered information such as customer id, credit score, gender, age, tenure, balance, if the customer is active, has a credit card, etc. During a period of 6 months, the bank observed if these customers left or stayed in the bank.
Your goal is to make an Artificial Neural Network that can predict, based on geo-demographical and transactional information given above, if any individual customer will leave the bank or stay (customer churn). Besides, you are asked to rank all the customers of the bank, based on their probability of leaving. To do that, you will need to use the right Deep Learning model, one that is based on a probabilistic approach.
If you succeed in this project, you will create significant added value to the bank. By applying your Deep Learning model the bank may significantly reduce customer churn.

#2 Image Recognition
In this part, you will create a Convolutional Neural Network that is able to detect various objects in images. We will implement this Deep Learning model to recognize a cat or a dog in a set of pictures. However, this model can be reused to detect anything else and we will show you how to do it – by simply changing the pictures in the input folder.
For example, you will be able to train the same model on a set of brain images, to detect if they contain a tumor or not. But if you want to keep it fitted to cats and dogs, then you will literally be able to a take a picture of your cat or your dog, and your model will predict which pet you have. We even tested it out on Hadelin’s dog!

#3 Stock Price Prediction
In this part, you will create one of the most powerful Deep Learning models. We will even go as far as saying that you will create the Deep Learning model closest to “Artificial Intelligence”. Why is that? Because this model will have long-term memory, just like us, humans.
The branch of Deep Learning which facilitates this is Recurrent Neural Networks. Classic RNNs have short memory, and were neither popular nor powerful for this exact reason. But a recent major improvement in Recurrent Neural Networks gave rise to the popularity of LSTMs (Long Short Term Memory RNNs) which has completely changed the playing field. We are extremely excited to include these cutting-edge deep learning methods in our course!
In this part you will learn how to implement this ultra-powerful model, and we will take the challenge to use it to predict the real Google stock price. A similar challenge has already been faced by researchers at Stanford University and we will aim to do at least as good as them.

#4 Fraud Detection
According to a recent report published by Markets & Markets the Fraud Detection and Prevention Market is going to be worth $33.19 Billion USD by 2021. This is a huge industry and the demand for advanced Deep Learning skills is only going to grow. That’s why we have included this case study in the course.
This is the first part of Volume 2 – Unsupervised Deep Learning Models. The business challenge here is about detecting fraud in credit card applications. You will be creating a Deep Learning model for a bank and you are given a dataset that contains information on customers applying for an advanced credit card.
This is the data that customers provided when filling the application form. Your task is to detect potential fraud within these applications. That means that by the end of the challenge, you will literally come up with an explicit list of customers who potentially cheated on their applications.

#5 & 6 Recommender Systems
From Amazon product suggestions to Netflix movie recommendations – good recommender systems are very valuable in today's World. And specialists who can create them are some of the top-paid Data Scientists on the planet.
We will work on a dataset that has exactly the same features as the Netflix dataset: plenty of movies, thousands of users, who have rated the movies they watched. The ratings go from 1 to 5, exactly like in the Netflix dataset, which makes the Recommender System more complex to build than if the ratings were simply “Liked” or “Not Liked”.
Your final Recommender System will be able to predict the ratings of the movies the customers didn’t watch. Accordingly, by ranking the predictions from 5 down to 1, your Deep Learning model will be able to recommend which movies each user should watch. Creating such a powerful Recommender System is quite a challenge so we will give ourselves two shots. Meaning we will build it with two different Deep Learning models.
Our first model will be Deep Belief Networks, complex Boltzmann Machines that will be covered in Part 5. Then our second model will be with the powerful AutoEncoders, my personal favorites. You will appreciate the contrast between their simplicity, and what they are capable of.
And you will even be able to apply it to yourself or your friends. The list of movies will be explicit so you will simply need to rate the movies you already watched, input your ratings in the dataset, execute your model and voila! The Recommender System will tell you exactly which movies you would love one night you if are out of ideas of what to watch on Netflix!

— Summary —
In conclusion, this is an exciting training program filled with intuition tutorials, practical exercises and real-World case studies.
We are super enthusiastic about Deep Learning and hope to see you inside the class!
Kirill & Hadelin