I am now an NVIDIA Deep Learning Institute (DLI) instructor

As you know that through my deep learning courses I have been vigorously pushing for making deep learning more accessible to everyone.

And I am thankful to announce that NVIDIA has given me the opportunity to be an NVIDIA Deep Learning Institute (DLI) certified instructor and ambassador in NUS to enable everyone to solve the most challenging problems in deep learning.

Cheers,
Ritchie

AI Hacker T-Shirt

Check out our first AI Hacker T-Shirt! We created this because the team used it for conferences lately.

Grab it now at $26 (we chose the most premium quality because we don’t like itchy shirts).

Best-Selling Course in 2 Weeks

We are a best-selling course on Udemy in 2 weeks coming highly recommended by learners as young as 14 years old.

Sign up for our course to get started with your deep learning journey!

We Are Featured on Facebook and Twitter by PyTorch

We’ve had the honour to be featured on Twitter and Facebook by Pytorch.

Sign up for our course now!

Practical Deep Learning with PyTorch

I have launched a new course that is the first in a series that puts you on a path to deep learning wizardry with PyTorch.

Topics Covered:

  1. PyTorch Fundamentals
  2. Linear Regression
  3. Logistic Regression
  4. Feedforward Neural Networks
  5. Convolutional Neural Networks (CNNs)
  6. Recurrent Neural Networks (RNNs)
  7. Long Short-Term Memory Networks (LSTMs)

 

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.

 

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.

Udemy link: https://www.udemy.com/practical-deep-learning-with-pytorch

Deep Learning Seminar for Medical Diagnostics and Self-Driving Car

A talk by Marek Bardoński, Senior Deep Learning Research Engineer, NVIDIA Switzerland. This is hosted by Ritchie Ng, Deep Learning Researcher, NExT Search Centre, NUS.

We will be touching on cutting-edge applications of deep learning for self-driving cars and medical diagnostics. Also there will be a tutorial followed by networking with deep learning researchers from NUS and NVIDIA.

Getting to The Hangar by NUS Enterprise: http://www.pathwaze.org/guides/hangar-nus-enterprise/