Note that these tutorials expect some knowledge of deep learning concepts. While some of the concepts are explained we are mainly focusing on (in detail) how to implement them in python with Pytorch.
I have compiled a list of additional resources that cover many of the concepts we look at, the YouTube series section are incredibly valuable!
Deep learning google sheets
If you have any good resources let me know and I can add them!
If you can't find an explaination on something you want to know let me know and i'll try to find it!
Some level of basic Python programming knowledge is expected.
More sections to come!
Let me know if you want to see anything else!
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Pytorch Youtube Playlist
Reinforcement Learning Youtube Playlist
Let me know if you want to see a video on any particular section!
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Section 0 -> Python basics that will be expected knowledge
Section 1 -> Implementing some basic Machine Learning Algorithms in Python with Numpy
Section 2 -> Pytorch intro and basics, basic Machine Learning Algorithms with Pytorch
Section 3 -> Multi-Layer Perceptron (MLP) for Classification and Non-Linear Regression
Section 4 -> Pytorch Convolutions and CNNs
Section 5 -> Pytorch Transfer Learning
Section 6 -> Pytorch Tools and Training Techniques
Section 7 -> Pytorch Autoencoders and Representation Learning
Section 8 -> Pytorch Bounding Box Detection and Image Segmentation
Section 9 -> Pytorch Image Generation
Section 10 -> Pytorch Trained Model Interpretation
Section 11 -> Pytorch Reinforcement Learning
Section 12 -> Using Sequential Data
Section 13 -> All about Attention
Section 14 -> Transformer Time
Section 15 -> Deploying Models??
notebooks -> Tutorials and Skeleton code (Start here)
solutions -> Skeleton code Solutions
data -> Data and Images
