Learning deep learning and interviewing is definitely inseparable from the following 5 important materials, let alone the Chinese version!

Data collection: \

Scan the background reply: 3070, you can get the electronic version

Content abstract

1. TensorFlow deep learning

Characteristics of books

******** Chinese, including basic introduction, theory and practice

2. PyTorch

The tutorial is fully restored from the official PyTorch version directory. It includes simple environment building, quick start related API, advanced operation, image processing actual combat, text processing actual combat, GAN and reinforcement learning, etc., basically covering all current knowledge points related to deep learning.

 

Tutorial directory

3. Statistical Learning Methods (2nd edition)

Brief Introduction:

Statistical learning method, namely machine learning method, is an important subject in the field of computer and its application. This book is divided into supervised learning and unsupervised learning, comprehensively and systematically introduces the main methods of statistical learning. Including perceptron, K-nearest neighbor, Naive Bayes, decision tree, Logistic regression and maximum entropy model, support vector machine, lifting method, EM algorithm, hidden Markov model and conditional random field, And clustering methods, singular value decomposition, principal component analysis, latent semantic analysis, probabilistic latent semantic analysis, Markov chain Monte Carlo method, latent Dirichlet assignment and PageRank algorithm. Each chapter introduces a method, except for four chapters on statistical, supervised, and unsupervised learning, which provide an overview and summary. The narration tries to start with specific problems or examples, from the simple to the deep, clarify ideas, give the necessary mathematical derivation, so that readers can master the essence of statistical learning methods and learn to use them. In order to meet the needs of readers for further study, the book also introduces some relevant research, gives a few exercises, and lists the main references. This book is a teaching reference book for statistical machine learning and related courses. It is suitable for college students and graduate students majoring in text data mining, information retrieval and natural language processing, as well as for r & D personnel engaged in computer application.

The courseware

4. Neural Networks and Deep Learning, Chinese version

\

introduce

Neural Networks and Deep Learning is a free online book that requires a moderate amount of mathematical knowledge and is both theoretical and hands-on. It provides the best solutions to many problems in the fields of image recognition, speech recognition and natural language processing, and teaches readers the many core concepts behind neural networks and deep learning.

5. Data Structures and Algorithms in Python

The course overview

The Peking University Open course “Data Structures and Algorithms in Python” is launched for the second time in Chinese University MOOC. The course will focus on the idea of “algorithm + data structure = program” and will be problem-solver oriented to help students improve their theoretical, abstract and design abilities.

This course pays attention to the practice and application of data structure and algorithm, with vivid examples and programming practice in the curriculum, guide students to actively build data abstraction and the thinking mode of hierarchical analysis, through solving practical problems to deepen to the data structure and corresponding processing algorithm learning experience, and learn through practical application to weigh the cost, time and space and other resources To achieve the optimal application effect.

Data collection: \

Scan the background reply: 3070, you can get the electronic version