MachineLearning-Study-Path/README.md at main · youwei1-sudo/MachineLearning-Study-Path · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
# Machine-learning Engineer Growing path

ML learning path
--
By difficulties in general :

Intro:
  1. Regression(Linear, logistic)
  2. KNN, K-means, desecion tree
  3. Artifical neuron network(MLP/CNN/LSTM)
      1. Backpropagation, Forward Propergation
      2. Max pooling, Activation Functions
  4.  RL: bandit/MDP/DP/TD learning/MC
  5. Coding:
      1. Python (Numpy, matplotlib, scikit-learn, open-cv , pytorch, Tensorflow etc)
      2. Good to have: GPU programming (CUDA C++ etc)
      3. Good to have: Parallel Computing
      4. Good to have: Matlab, R, Julia
      5. Gits, Unix enviorment

Beginner:

  1. Bayes optimization,  Bayesian Linear Regression/logistic Regression
  2. FFT
  3. NLP Transformer/BERT/GPT
  4. GMM/HMM
  5. EM/Baum-Welch/Viterbi/KalmanFilter(UKF)
  6. Graphical model/Belief propagation
  7. Junction tree
  8. SVM/Kernel Method/RKHS
  9. model based RL/Policy gradient/actor-critic


Medium:
  Missing Cuz i am still learning

Hard:
  Missing Cuz i am far away , hhh

Math you need:
--
  Intro: Calculus, Linear Algebra, probability theory

  Beginner: Convex optimization(Ex. Constrained opt/kkt condition）, Graph theory, High-dim probabilty theory(Ex. Normal distribution and its linear transform, conditional probablity), Bayesian inference， Stochastic process

  中级：概率论（数族分布，GLM，测度论，skewness，kurtosis），凸优化，统计力学（spin glass model），泛函分析

## cite source
![IMG_055C85BA7194-1](https://github.com/youwei1-sudo/MachineLearning-Study-Path/assets/45375571/2fc27e58-094a-4cce-a3eb-45c4e127f9a1)
![ ](https://github.com/youwei1-sudo/MachineLearning-Study-Path/assets/45375571/37d43689-f895-4035-aed8-95aca0514fec)

## Effiency way to manage your code and conduct your experiement
Deep learning Enterprise Intergration
1. code package
2. Rule to create file & directory
3. Management software

https://github.com/youwei1-sudo/MachineLearning-Study-Path/wiki/Enterprise-Integration(代码管理)

## Developer Knowledge / learning path(backend)
https://roadmap.sh/backend

## General Deep Learning tips
https://jeffmacaluso.github.io/post/DeepLearningRulesOfThumb/


## Fine Tuning guide
https://github.com/google-research/tuning_playbook

## ML course i think good for ML basis


Machine Learning

10-601, Spring 2015

Carnegie Mellon University

Tom Mitchell and Maria-Florina Balcan
http://www.cs.cmu.edu/~ninamf/courses/601sp15/lectures.shtml