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Modern Deep Learning in Python

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 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 benchmarks. This is THE dataset researchers look at first when they want to ask the question, "does this thing work?"

These images are important part of deep learning history and are still used for testing today. Every deep learning expert should know them well.

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 (taking derivatives)

  • matrix arithmetic

  • probability

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

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

  • Know how to write a neural network with Numpy

TIPS (for getting through the course):

  • Watch it 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.

  • Realize that most exercises will take you days or weeks to complete.

  • Write code yourself, don't just sit there and look at my code.

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

  • Check out the lecture "What order should I take your courses in?" (available in the Appendix of any of my courses, including the free Numpy course)

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Udemy

Rating 4.3 based on 181 ratings
Length 9 hours
Starts On Demand (Start anytime)
Cost $13
From Udemy
Instructor Lazy Programmer Inc.
Download Videos Only via the Udemy mobile app
Language English
Subjects Data Science Business
Tags Data Science Business Development Data & Analytics

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What people are saying

According to other learners, here's what you need to know

lazy programmer in 9 reviews

As someone who very much enjoys coding , math and learning the basics and fundamentals (not just the API as Lazy Programmer would say), I found this course extremely helpful.

Before taking Lazy Programmer courses I spent a very long time (hundreds of hours) wandering around the animated videos in the youtube, which just made me more confused.

Lazy programmer teaches a great course.

This course was another great course by “The Lazy Programmer”.

I’ve done numerous other courses on Deep Learning and i have again come back to a Lazy Programmer application and found a more grounded and real base for my learning.

I like that part of Lazy programmer series of courses.

As usual, the Lazy Programmer explains it well for you, doesn't spoon feed, and makes you learn the material.

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neural networks in 8 reviews

Easy to grasp if you know about neural networks and you want to learn all the new deep learning tricks of the trade.

These are great once you know the basic of neural networks.

course is right on the topic of making practical neural networks.

great to see all these different methods of training neural networks packed into one course.

pca) Good course for scaling up neural networks.

Great course for learning how to optimize neural networks.

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modern deep learning in 5 reviews

This course was the perfect introduction to modern deep learning concepts.

It talks about a huge number of modern deep learning topics and associated implementations.

This is a great introduction to modern deep learning and neural networks packaged into a neatly organized course and simple effective explanations.

One of the best courses I have taken on deep learning, taking you from MLPs to modern deep learning.

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machine learning in 4 reviews

Thanks for the great course After finishing this course I started realizing that my knowledge about machine learning has started to solidify and my grip on ML methods is getting stronger.

please create Advanced Bayesian machine learning including gaussian processes Outstanding course.

This course could benefit from him posting another lecture on how to implement GPU instead of a CPU for machine learning.

It's a good course for advanced machine learning practitioners.

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Udemy

Rating 4.3 based on 181 ratings
Length 9 hours
Starts On Demand (Start anytime)
Cost $13
From Udemy
Instructor Lazy Programmer Inc.
Download Videos Only via the Udemy mobile app
Language English
Subjects Data Science Business
Tags Data Science Business Development Data & Analytics

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