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Deep learning Flower Book + machine learning watermelon book electronic version I found
2022-07-07 07:25:00 【Program Yuanke】
Today I recommend two sets of good study notes , It's very detailed . Deep learning Flower Book + Machine learning watermelon book PDF file .
《 Deep learning 》, also called “ Flower Book ”. The book is written by three big men Ian Goodfellow、Yoshua Bengio and Aaron Courville writing , It's a classic textbook for in-depth learning , Known as deep learning “ Bible ”.
Watermelon book 《 machine learning 》 Undoubtedly, it is a must read book of machine learning . This book is an introduction to this field , As far as possible, it covers all aspects of the basic knowledge of machine learning .
For these two sets of information , I highly recommend , This time I will share it for free .
Free download method is attached at the end of the article
Deep learning flower book content display
The first part : Fundamentals of Applied Mathematics and machine learning
linear algebra
Probability and information theory
Numerical calculation
Fundamentals of machine learning
The second part : Deep networks : Modern practice
Deep feedforward network
Regularization in deep learning
Optimization in depth model
Convolution network
Sequence modeling : Loops and recursive networks
Practical methodology
application
The third part : Deep learning research
Include ( Linear factor model 、 Self encoder 、 It means learning 、 Structured probability model in deep learning 、 The monte carlo method 、 Straight face partition function 、 Approximate inference 、 Deep survival model ...)
English original
stay AI In the field , There are many course materials about deep learning , and 《 Deep learning Deep Learning》 It is a classic textbook recognized by the industry as a foundation in the field of deep learning , This is free sharing .
Machine learning watermelon book content display
Chapter one : The introduction
- introduction
- Basic terminology
- Hypothetical space
- Generalize preferences
- development history
- Application status
- Reading materials
Chapter two : Model evaluation and selection
- Empirical error and over fitting
- Evaluation methods
- Performance metrics
- Comparative test
- Deviation and variance
- Reading materials
The third chapter : Linear model
- Basic form
- Linear regression
- Log probability regression
- Linear discriminant analysis
- Multi category learning
- Category imbalance
- Reading materials
Chapter four : Decision tree
- The basic flow
- Divide and choose
- Pruning
- Continuous and missing values
- Multivariate decision trees
- Reading materials
The fifth chapter : neural network
- Neuron model
- Perceptrons and multilayer networks
- Error back propagation algorithm
- Global minimum and local minimum
- Other common neural networks
- Deep learning
- Reading materials
Chapter six : Support vector machine
- Interval and support vector
- Antithesis
- Kernel function
- Soft interval and regularization
- Support vector regression
- Nuclear method
- Reading materials
Chapter vii. : Bayesian classification
Chapter viii. : Integrated learning
Chapter nine : clustering
.......
If there is too much content, this article will not show it one by one .
The book introduces in detail different types of algorithms in the field of machine learning ( for example : Supervised learning 、 Unsupervised learning 、 Semi-supervised learning 、 Reinforcement learning 、 Integrated dimensionality reduction 、 Feature selection, etc ), It records my understanding ideas and expanded knowledge points in the learning process , I hope it is helpful for new people to read watermelon books !
Click on the business card below , Scan the code to get the full version for free PDF file .
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