Data Science: Modern Deep Learning in Python

Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.

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Course Data

Lectures: 91
Length: 13h 11m
Skill Level: All Levels
Languages: English
Includes: Lifetime access, certificate of completion (shareable on LinkedIn, Facebook, and Twitter), Q&A forum

Course Description

This course continues where my first course, Deep Learning in Python, left off. You already know how to build an artificial neural network in Python, and you have a plug-and-play script that you can use for TensorFlow. Neural networks are one of the staples of machine learning, and they are always a top contender in Kaggle contests. If you want to improve your skills with neural networks and deep learning, this is the course for you.

You already learned about backpropagation, but there were a lot of unanswered questions. How can you modify it to improve training speed? In this course you will learn about batch and stochastic gradient descent, two commonly used techniques that allow you to train on just a small sample of the data at each iteration, greatly speeding up training time.

You will also learn about momentum, which can be helpful for carrying you through local minima and prevent you from having to be too conservative with your learning rate. You will also learn about adaptive learning rate techniques like AdaGrad, RMSprop, and Adam which can also help speed up your training.

Because you already know about the fundamentals of neural networks, we are going to talk about more modern techniques, like dropout regularization and batch normalization, which we will implement in both TensorFlow and Theano. The course is constantly being updated and more advanced regularization techniques are coming in the near future.

In my last course, I just wanted to give you a little sneak peak at TensorFlow. In this course we are going to start from the basics so you understand exactly what's going on - what are TensorFlow variables and expressions and how can you use these building blocks to create a neural network? We are also going to look at a library that's been around much longer and is very popular for deep learning - Theano. With this library we will also examine the basic building blocks - variables, expressions, and functions - so that you can build neural networks in Theano with confidence.

Theano was the predecessor to all modern deep learning libraries today. Today, we have almost TOO MANY options. Keras, PyTorch, CNTK (Microsoft), MXNet (Amazon / Apache), etc. In this course, we cover all of these! Pick and choose the one you love best.

Because one of the main advantages of TensorFlow and Theano is the ability to use the GPU to speed up training, I will show you how to set up a GPU-instance on AWS and compare the speed of CPU vs GPU for training a deep neural network.

With all this extra speed, we are going to look at a real dataset - the famous MNIST dataset (images of handwritten digits) and compare against various known benchmarks. This is THE dataset researchers look at first when they want to ask the question, "does this thing work?"

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.

Suggested Prerequisites:

  • calculus
  • matrix arithmetic
  • probability
  • Python coding: if/else, loops, lists, dicts, sets
  • Numpy coding: matrix and vector operations, loading a CSV file
  • linear regression, logistic regression
  • neural networks and backpropagation

Tips for success:

  • Use the video speed changer! Personally, I like to watch at 2x.
  • Take handwritten notes. This will drastically increase your ability to retain the information.
  • Write down the equations. If you don't, I guarantee it will just look like gibberish.
  • Ask lots of questions on the discussion board. The more the better!
  • Don't get discouraged if you can't solve every exercise right away. Sometimes it'll take hours, days, or maybe weeks!
  • Write code yourself, this is an applied course! Don't be a "couch potato".

Testimonials and Success Stories

I am one of your students. Yesterday, I presented my paper at ICCV 2019. You have a significant part in this, so I want to sincerely thank you for your in-depth guidance to the puzzle of deep learning. Please keep making awesome courses that teach us!

I just watched your short video on “Predicting Stock Prices with LSTMs: One Mistake Everyone Makes.” Giggled with delight.

You probably already know this, but some of us really and truly appreciate you. BTW, I spent a reasonable amount of time making a learning roadmap based on your courses and have started the journey.

Looking forward to your new stuff.

Thank you for doing this! I wish everyone who call’s themselves a Data Scientist would take the time to do this either as a refresher or learn the material. I have had to work with so many people in prior roles that wanted to jump right into machine learning on my teams and didn’t even understand the first thing about the basics you have in here!!

I am signing up so that I have the easy refresh when needed and the see what you consider important, as well as to support your great work, thank you.

Thank you, I think you have opened my eyes. I was using API to implement Deep learning algorithms and each time I felt I was messing out on some things. So thank you very much.

I have been intending to send you an email expressing my gratitude for the work that you have done to create all of these data science courses in Machine Learning and Artificial Intelligence. I have been looking long and hard for courses that have mathematical rigor relative to the application of the ML & AI algorithms as opposed to just exhibit some 'canned routine' and then viola here is your neural network or logistical regression. ...


I have now taken a few classes from some well-known AI profs at Stanford (Andrew Ng, Christopher Manning, …) with an overall average mark in the mid-90s. Just so you know, you are as good as any of them. But I hope that you already know that.

I wish you a happy and safe holiday season. I am glad you chose to share your knowledge with the rest of us.

Hi Sir I am a student from India. I've been wanting to write a note to thank you for the courses that you've made because they have changed my career. I wanted to work in the field of data science but I was not having proper guidance but then I stumbled upon your "Logistic Regression" course in March and since then, there's been no looking back. I learned ANNs, CNNs, RNNs, Tensorflow, NLP and whatnot by going through your lectures. The knowledge that I gained enabled me to get a job as a Business Technology Analyst at one of my dream firms even in the midst of this pandemic. For that, I shall always be grateful to you. Please keep making more courses with the level of detail that you do in low-level libraries like Theano.

I just wanted to reach out and thank you for your most excellent course that I am nearing finishing.

And, I couldn't agree more with some of your "rants", and found myself nodding vigorously!

You are an excellent teacher, and a rare breed.

And, your courses are frankly, more digestible and teach a student far more than some of the top-tier courses from ivy leagues I have taken in the past.

(I plan to go through many more courses, one by one!)

I know you must be deluged with complaints in spite of the best content around That's just human nature.

Also, satisfied people rarely take the time to write, so I thought I will write in for a change. :)

Hello, Lazy Programmer!

In the process of completing my Master’s at Hunan University, China, I am writing this feedback to you in order to express my deep gratitude for all the knowledge and skills I have obtained studying your courses and following your recommendations.

The first course of yours I took was on Convolutional Neural Networks (“Deep Learning p.5”, as far as I remember). Answering one of my questions on the Q&A board, you suggested I should start from the beginning – the Linear and Logistic Regression courses. Despite that I assumed I had already known many basic things at that time, I overcame my “pride” and decided to start my journey in Deep Learning from scratch. ...


By the way, if you are interested to hear. I used the HMM classification, as it was in your course (95% of the script, I had little adjustments there), for the Customer-Care department in a big known fintech company. to predict who will call them, so they can call him before the rush hours, and improve the service. Instead of a poem, I Had a sequence of the last 24 hours' events that the customer had, like: "Loaded money", "Usage in the food service", "Entering the app", "Trying to change the password", etc... the label was called or didn't call. The outcome was great. They use it for their VIP customers. Our data science department and I got a lot of praise.


Introduction and Outline

3 Lectures · 21min
  1. Introduction and Outline (09:20) (FREE preview available)
  2. Where to get the Code (09:21)
  3. How to Succeed in this Course (03:04)


7 Lectures · 01hr 00min
  1. Review (pt 1): Neuron Predictions (14:02)
  2. Review (pt 2): Neuron Learning (09:17)
  3. Review (pt 3): Artificial Neural Networks (12:10)
  4. Review Exercise Prompt (05:39)
  5. Review Code (pt 1) (05:51)
  6. Review Code (pt 2) (12:40)
  7. Review Summary (01:13)

Gradient Descent: Full vs Batch vs Stochastic

4 Lectures · 43min
  1. Stochastic Gradient Descent and Mini-Batch Gradient Descent (Theory) (16:15)
  2. SGD Exercise Prompt (03:34)
  3. Stochastic Gradient Descent and Mini-Batch Gradient Descent (Code pt 1) (11:08)
  4. Stochastic Gradient Descent and Mini-Batch Gradient Descent (Code pt 2) (12:10)

Momentum and adaptive learning rates

9 Lectures · 01hr 08min
  1. Using Momentum to Speed Up Training (06:11)
  2. Nesterov Momentum (06:37)
  3. Code for training a neural network using momentum (06:35)
  4. Variable and adaptive learning rates (11:46)
  5. Constant learning rate vs. RMSProp in Code (04:05)
  6. Adam Optimization (pt 1) (13:15)
  7. Adam Optimization (pt 2) (11:14)
  8. Adam in Code (05:44)
  9. Suggestion Box (03:10)

Choosing Hyperparameters

5 Lectures · 18min
  1. Hyperparameter Optimization: Cross-validation, Grid Search, and Random Search (03:20)
  2. Sampling Logarithmically (03:11)
  3. Grid Search in Code (07:12)
  4. Modifying Grid Search (01:23)
  5. Random Search in Code (03:46)

Weight Initialization

5 Lectures · 01hr 17min
  1. Weight Initialization Section Introduction (59:00)
  2. Vanishing and Exploding Gradients (06:07)
  3. Weight Initialization (08:21)
  4. Local vs. Global Minima (02:52)
  5. Weight Initialization Section Summary (01:39)


3 Lectures · 27min
  1. Theano Basics: Variables, Functions, Expressions, Optimization (07:47)
  2. Building a neural network in Theano (09:17)
  3. Is Theano Dead? (10:04)


3 Lectures · 31min
  1. TensorFlow Basics: Variables, Functions, Expressions, Optimization (07:27)
  2. Building a neural network in TensorFlow (09:43)
  3. What is a Session? (And more) (14:09)

GPU Speedup. Homework, and Other Misc. Topics

5 Lectures · 43min
  1. Setting up a GPU Instance on Amazon Web Services (07:07)
  2. Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer (22:15)
  3. Can Big Data be used to Speed Up Backpropagation? (03:22)
  4. How to Improve your Theano and Tensorflow Skills (04:40)
  5. Theano vs. TensorFlow (05:53)

Transition to the 2nd Half of the Course

1 Lectures · 05min
  1. Transition to the 2nd Half of the Course (05:25)

Project: Facial Expression Recognition

7 Lectures · 01hr 05min
  1. Facial Expression Recognition Project Introduction (04:52)
  2. Facial Expression Recognition Problem Description (12:22)
  3. The class imbalance problem (06:02)
  4. Utilities walkthrough (05:46)
  5. Class-Based ANN in Theano (19:10)
  6. Class-Based ANN in TensorFlow (15:29)
  7. Facial Expression Recognition Project Summary (01:21)

Modern Regularization Techniques

5 Lectures · 25min
  1. Modern Regularization Techniques Section Introduction (02:26)
  2. Dropout Regularization (11:39)
  3. Dropout Intuition (04:02)
  4. Noise Injection (05:23)
  5. Modern Regularization Techniques Section Summary (02:16)

Batch Normalization

9 Lectures · 42min
  1. Batch Normalization Section Introduction (02:04)
  2. Exponentially-Smoothed Averages (04:25)
  3. Batch Normalization Theory (10:55)
  4. Batch Normalization Tensorflow (part 1) (05:21)
  5. Batch Normalization Tensorflow (part 2) (05:35)
  6. Batch Normalization Theano (part 1) (04:21)
  7. Batch Normalization Theano (part 2) (06:34)
  8. Noise Perspective (01:59)
  9. Batch Normalization Section Summary (01:39)


4 Lectures · 19min
  1. Keras Discussion (06:49)
  2. Keras in Code (06:38)
  3. Keras Functional API (04:27)
  4. How to easily convert Keras into Tensorflow 2.0 code (01:49)


3 Lectures · 17min
  1. PyTorch Basics (11:36)
  2. PyTorch Dropout (02:56)
  3. PyTorch Batch Norm (02:58)

PyTorch, CNTK, and MXNet

1 Lectures · 49min
  1. PyTorch, CNTK, and MXNet (49:00)

Deep Learning Review Topics

3 Lectures · 15min
  1. What's the difference between "neural networks" and "deep learning"? (07:59)
  2. Manually Choosing Learning Rate and Regularization Penalty (04:09)
  3. Where does this course fit into your deep learning studies? (03:49)

Extra Help

1 Lectures · 03min
  1. How to Uncompress a .tar.gz file (03:18)

Setting Up Your Environment (Appendix/FAQ by Student Request)

3 Lectures · 42min
  1. Pre-Installation Check (04:13)
  2. Anaconda Environment Setup (20:21)
  3. How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow (17:33)

Extra Help With Python Coding for Beginners (Appendix/FAQ by Student Request)

4 Lectures · 42min
  1. How to Code Yourself (part 1) (15:55)
  2. How to Code Yourself (part 2) (09:24)
  3. Proof that using Jupyter Notebook is the same as not using it (12:29)
  4. Python 2 vs Python 3 (04:38)

Effective Learning Strategies for Machine Learning (Appendix/FAQ by Student Request)

4 Lectures · 59min
  1. How to Succeed in this Course (Long Version) (10:25)
  2. Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? (22:05)
  3. What order should I take your courses in? (part 1) (11:19)
  4. What order should I take your courses in? (part 2) (16:07)

Appendix / FAQ Finale

2 Lectures · 08min
  1. What is the Appendix? (02:48)
  2. Where to get discount coupons and FREE deep learning material (05:49)


  • Estimator API Tutorial
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