Deep learning 500 questions

 

 

In recent years , Deep learning in pronunciation 、 Images 、 Natural language processing and other fields have achieved very good results , Naturally, it has become a hot spot for technicians to learn .

 

In order to help the partners who are learning deep learning , An outstanding graduate of Sichuan University , stay GitHub Created a project on ：《 Deep learning 500 ask 》, Through the form of question and answer to the commonly used probability knowledge 、 linear algebra 、 machine learning 、 Deep learning 、 Computer vision and other hot issues , To help yourself and readers in need . The book is divided into 15 Chapters , near 20 swastika .

As of today , The project has acquired 2106 individual 「star」 as well as 465 individual 「fork」（GitHub Project address ：https://github.com/scutan90/DeepLearning-500-questions）

Although this book is not finished , But it's worth reading , Now let's introduce in detail what is in the book ：

Chapter I fundamentals of Mathematics

This chapter mainly explains the basic knowledge of Mathematics , It not only covers the relevant basic concepts , It also includes the connection between each other , Such as scalar 、 vector 、 The connection between tensors ; The difference between tensor and matrix , There are also common probability distributions ：

Besides , It also explains different types of probability distributions and statistics （ expect 、 variance 、 covariance 、 Correlation number ） Related basic knowledge of

 

 

 

Second, machine learning foundation

 

This chapter lists the common algorithms and the advantages and disadvantages of common classification algorithms 、 Evaluation usage of classification algorithm 、 The relationship between big data and deep learning , The second chapter covers many knowledge points, but it is very comprehensive .

Chapter III foundation of deep learning

This chapter begins with the topic , To describe neural networks , The book starts with the simplest neural network , And then layer by layer , The common model structures of neural networks are listed , How to choose a deep learning development platform and other key content , For example, the common model structure of neural network is as follows ：

Chapter four classic network

This chapter introduces several classic Networks , Include LeNet-5、AlexNet、 visualization ZFNet- Deconvolution 、GoogleNet Model structure and model interpretation of , Such as LeNet-5 The structure of the model is as follows ：

After reading the above contents , Have you been eager to read this unfinished book in depth ？ Or you are working in this field , It can also help the author improve the book .

Finally, attach GitHub Address ：https://github.com/scutan90/DeepLearning-500-questions