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 2300 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.
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# It is this easy! import torch # Create a variable of value 1 each. a = torch.Tensor() b = torch.Tensor() # 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¶
Currently I am leading artificial intelligence with my colleagues in ensemblecap.ai, an AI hedge fund based in Singapore. I am also an NVIDIA Deep Learning Institute instructor enabling developers, data scientists, and researchers leverage on deep learning to solve the most challenging problems. Also, I’m into deep learning research with researchers based in NExT++ (NUS) and MILA.
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 Tutorial.
I was previously conducting research in deep learning, computer vision and natural language processing in NExT Search Centre led by Professor Tat-Seng Chua that is jointly setup between National University of Singapore (NUS) and Tsinghua University and is part of NUS Smart Systems Institute. During my time there, I managed to publish in top-tier conferences and workshops like ICML and IJCAI.
Check out my profile link at ritchieng.com
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 bigaidream.github.io