Deep learning Flower Book + machine learning watermelon book electronic version I found

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 .