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About Us

We deploy a top-down approach that enables you to grasp deep learning theories and code easily and quickly. We have open-sourced all our materials through our Deep Learning Wizard Wikipedia. For visual learners, feel free to sign up for our video course and join over 3000 deep learning wizards.

To this date, we have taught thousands of students across more than 120+ countries from students as young as 15 to postgraduates and professionals in leading MNCs and research institutions around the world.

PyTorch as our Preferred Deep Learning Library

We chose PyTorch because it integrates with Python well with similar syntax that allows you to quickly pick it up and implement your projects and research papers on GPU and CPU. It is also actively maintained by Facebook.

# It is this easy! 
import torch

# Create a variable of value 1 each.
a = torch.Tensor([1])
b = torch.Tensor([1])

# Add the 2 variables to give you 2, it's that simple!
c = a + b

Made for Visual and Book Lovers

We are visual creatures, that is why we offer detailed video courses on Udemy that have shown to accelerate learning and boost knowledge retention. Our courses are updated regularly to ensure our codes are compatible with the latest version of PyTorch.

For book lovers, you will be happy to know Deep Learning Wizard's wikipedia will always be updated first prior to our release of video courses.

Experienced Research and Applied Team

Ritchie Ng

Currently I am leading artificial intelligence with my colleagues in, an AI hedge fund based in Singapore comprising quants and traders from JPMorgan and Nomura. I have built the whole AI tech stack in a production environment with rigorous time-sensitive and fail-safe software testing powering multi-million dollar trades daily. Additionally, I co-run, as portfolio manager, our systematic end-to-end deep learning portfolio with the CIO.

I am also an NVIDIA Deep Learning Institute instructor leading all deep learning workshops in NUS, Singapore and conducting workshops across Southeast Asia.

In my free time, I'm into deep learning research with MILA and NUS where I am a research associate in NExT (NUS).

My passion for enabling anyone to leverage on deep learning has led to the creation of Deep Learning Wizard where I have taught and still continue to teach more than 2000 students in over 60 countries around the world. The course is recognized by Soumith Chintala, Facebook AI Research, and Alfredo Canziani, Post-Doctoral Associate under Yann Lecun, as the first comprehensive PyTorch Video Tutorial.

I have taught Deep Learning Foundations with Alfredo Canziani at Rwanda, Africa for the African Masters in Machine Intelligence (AMMI) in 2018 supported by Google and Facebook.

I was previously conducting research in meta-learning for hyperparameter optimization for deep learning algorithms in NExT Search Centre that is jointly setup between National University of Singapore (NUS), Tsinghua University and University of Southampton led by co-directors Prof Tat-Seng Chua (KITHCT Chair Professor at the School of Computing), Prof Sun Maosong (Dean of Department of Computer Science and Technology, Tsinghua University), and Prof Dame Wendy Hall (Director of the Web Science Institute, University of Southampton).

I graduated from NUS where I was an NUS Global Merit Scholar, Chua Thian Poh Community Leadership Programme Fellow, Philip Yeo Innovation Fellow, and NUS Enterprise I&E Praticum Award recipient.

Check out my profile link at

Jie Fu

I am undergoing my postdoc journey at Montreal Institute for Learning Algorithms (MILA) to prepare for the coming AI winter, as Eddard Stark said "He won't be a boy forever and winter is coming" -- Game of Thrones. I am privileged to work with Christopher Pal.

I earned my PhD degree from National University of Singapore (NUS), and was fortunately under the supervision of Tat-Seng Chua and Huan Xu, also closely working with Jiashi Feng and Kian Hsiang Low.

I am interested in machine learning. More specifically, my research is focused on deep learning, probabilistic reasoning, reinforcement learning and neural abstract machines. I am especially excited about reducing the gap between theoretical and practical algorithms in a principled and efficient manner.

Check out my profile link at


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