Learn and practice essential programming skills for conducting machine learning and deep learning research.
- Numpy
- Scikit-Learn
- PyTorch, Logistic Regression + Multilayer Perception
- Lecture: https://youtu.be/6Q3AzGDd5oc
- Practice: https://youtu.be/Tdz8dx8wYTE
- Autoencoders & Denoising Autoencoders
- Lecture: https://youtu.be/Mx2n4OtHq_s
- Practice: https://youtu.be/J0YcBKmLfbA
- Variational Autoencoders
- Lecture: https://youtu.be/EErBe3-iOg4
- Practice: https://youtu.be/YSTvAc8kjmU
- Generative Adversarial Networks
- Lecture: https://youtu.be/4d5vatIV21g
- Practice: https://youtu.be/ZKOavcIfxW0
- Convolutional Neural Networks
- Lecture: https://youtu.be/5UxKiIThs-I
- Practice: https://youtu.be/QKout3TEgWU
- Word2Vec + Subword Encoding
- Recurrent Neural Network + Sequence-to-Sequence
- Lecture: https://youtu.be/R6PDzmMOHXI
- Practice: https://youtu.be/FglgBa7rc_g
- Image-To-Text
- Lecture: https://youtu.be/NoWMhfFI51c
- Practice: https://youtu.be/G7IBb1T2IL4
- Transformers
- Lecture: https://youtu.be/h8avp8yDKV4
- Practice: https://youtu.be/7lJgMQQoCg8
- BERT (& GPT)
- Lecture: https://youtu.be/OFUYHRbHqOk
- Practice: https://youtu.be/HS94-5QPWt4
- Graph Neural Networks
- Lecture: https://youtu.be/RRGS9jBBg_M
- Practice: https://youtu.be/tr7nq-4WfEA
- Neural Ordinary Differential Equations
- Lecture: https://youtu.be/sIFnARdTVvE
- Practice: https://youtu.be/0oSayyDp2M0
- Python 3
- jupyter notebook/ jupyterlab
- Numpy
- Torch
- Torch Vision
- Sklearn
- Matplotlib
- https://arxiv.org/pdf/1806.09055.pdf
- https://arxiv.org/pdf/1706.03762.pdf
- https://arxiv.org/pdf/2005.14165.pdf
- https://arxiv.org/abs/1310.4546
- https://deepmind.com/research/publications/mastering-game-go-deep-neural-networks-tree-search
- https://proceedings.neurips.cc/paper/2017/file/2650d6089a6d640c5e85b2b88265dc2b-Paper.pdf
Credit to: Associate Prof Edward Choi, KAIST