Lu, Yukuan

The concepts of datasets, samples, labels in Machine Learning.

Sources:

1. Mu Li et al. 1. Introduction. Dive into Deep Learning.

2. Cross-validation: evaluating estimator performance

Decorators are a significant part of Python. In simple words: they are functions which take other functions as inputs and output their modified versions.

Sources:

1. Python Tips: Decorators

Sources:

1. 廖雪峰的教程

Tutorial about using inversese proxy to connect a server in a private network which your machine don't have access to.

Resources for essential topics of Machine Learning and Deep learning, including Natural language processing (NLP), Computer Vision (CV), Reinforcement Learning (RL), Self-Supervised Learning (SSL), etc.

After several years of learning, I don't need these resources anymore. But I still preserve them to share with those who're new to this field.

Sources:

1. James McClellan, Ronald Schafer & Mark Yoder. (2015). IIR Filters. DSP First (2nd ed., pp. 394-457). Pearson.

Common problems of Shannon entropy in information theory.

Sources:

1. The spelled-out intro to neural networks and backpropagation: building micrograd
2. Stanford CS231N, Lecture 4

Other useful resources:

1. StatQuest's Neural Networks videos

2. Efficient backprop. You need do download it via

   1	wget http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf

The python script and jupyter notebook used in this article can be found at here.

This article is a step-by-step explanation of neural networks which are extensively used in machine learning. It only involves the most basic case where the input of a neuron is a vector (1-D tensor)and output of a neuron is a scalar (0-D tensor). But the idea holds for higher, please reter to Derivatives, Backpropagation, and Vectorization for details.

# TODO

Ref:

1. EE 376A: Information Theory. Winter 2018. Lecture 6. - Stanford University
2. EE 376A: Information Theory. Winter 2017. Lecture 4. - Stanford University
3. Elements of Information Theory

Sources:

1. James McClellan, Ronald Schafer & Mark Yoder. (2015). FIR Filters. DSP First (2nd ed., pp. 147-193). Pearson.