-
Machine learning basics.
This part briefly introduces the fundamental ML problems-- regression, classification, dimensionality reduction, and clustering-- and the traditional ML models and numerical algorithms for solving the problems.
-
Neural network basics.
This part covers the multilayer perceptron, backpropagation, and deep learning libraries, with focus on Keras.
-
Convolutional neural networks (CNNs).
This part is focused on CNNs and its application to computer vision problems.
-
CNN basics
[slides].
-
Tricks for improving test accuracy
[slides].
-
Feature scaling and batch normalization
[slides].
-
Advanced topics on CNNs
[slides].
-
Popular CNN architectures
[slides].
-
Further reading:
-
[style transfer (Section 8.1, Chollet's book)]
-
[visualize CNN (Section 5.4, Chollet's book)]
-
Recurrent neural networks (RNNs).
This part introduces RNNs and its applications in natural language processing (NLP).
-
Transformer Models.
-
Autoencoders.
This part introduces autoencoders for dimensionality reduction and image generation.
-
Generative Adversarial Networks (GANs).
-
Deep Reinforcement Learning.
-
Parallel Computing.
-
Adversarial Robustness.
This part introduces how to attack neural networks using adversarial examples and how to defend from the attack.
-
Meta Learning.
-
Neural Architecture Search (NAS).
请发表评论