What you'll learn

Effectively wield PyTorch, a Python-first framework, to build your deep learning projects
Master deep learning concepts and implement them in PyTorch

Requirements

You need to know basic python such as lists, dictionaries, loops, functions and classes
You need to know basic differentiation
You need to know basic algebra

Description

Growing Importance of Deep Learning

Deep learning underpins a lot of important and increasingly important applications today ranging from facial recognition, to self-driving cars, to medical diagnostics and more.

Made for Anyone

Although many courses are very mathematical or too practical in nature, this course strikes a careful balance between the two to provide a solid foundation in deep learning for you to explore further if you are interested in research in the field of deep learning and/or applied deep learning. It is purposefully made for anyone without a strong background in mathematics. And for those with a strong background, it would accelerate your learning in understanding the different models in deep learning.

Code As You Learn

This entire course is delivered in a Python Notebook such that you can follow along the videos and replicate the results. You can practice and tweak the models until you truly understand every line of code as we go along. I highly recommend you to type every line of code when you are listening to the videos as this will help a lot in getting used to the syntax.

Gradual Learning Style

The thing about many guides out there is that they lack the transition from the very basics and people often get lost or miss out vital links that are critical in understanding certain models. Because of this, you can see how every single topic is closely linked with one another. In fact, at the beginning of every topic from logistic regression, I take the time to carefully explain how one model is simply a modification from the previous. That is the marvel of deep learning, we can trace back some part of it to linear regression where we will start.

Diagram-Driven Code

This course uses more than 100 custom-made diagrams where I took hundreds of hours to carefully create such that you can clearly see the transition from one model to another and understand the models comprehensively. Also, the diagrams are created so you can clearly see the link between the theory that I would teach and the code you would learn.

Mentor Availability

When I first started learning, I wished I had a mentor to guide me through the basics till the advanced theories where you can publish research papers and/or implement very complicated projects. And this course provides you with free access to ask any question, no matter how basic. I will be there and try my very best to answer your question. Even if the material is covered here, I will take the effort to point you to where you can learn here and more resources beyond this course.

Math Prerequisite FAQ

This is not a course that emphasizes heavily on the mathematics behind deep learning. It focuses on getting you to understand how everything works first which is very important for you to easily catch up on the mathematics later on. There are mathematics involved but they are limited with the sole aim to enhance your understanding and provide a gentle learning curve for future courses that would dive much deeper into it.

Latest Python Notebooks Compatible with PyTorch 0.4 and 1.0

There are very small changes from PyTorch 0.3 for this deep learning series where you will find it is extremely easy to transit over!

Who this course is for:

Anyone who wants to learn deep learning
Deep learning researchers using other frameworks like TensorFlow, Keras, Torch, and Caffe
Any python programmer