u013237424的专栏

Neo4j 4.2.0 | 简体中文版 4.2.0 | 简体中文版 Docker 版 | 简体中文版使用指南 Neo4j 3.5.24 | 简体中文版 3.5.24 ToNeo4j 4.0 (导入精灵) | Excel 示例 | MySQL 示例 Neo4j Desktop 1.3.11 Neo4j Bloom 1.4.0 Cypher Shell 4.1.0 APOC 4.1.0.2 GDS 1.3.4 ALGO 3.5.14.0 Neo4j Driver N

何晓冬 京东AI研究院常务副院长，深度学习和 语音及语言实验室主任，深度学习在图像上的ppt介绍，前沿科学的发展程度和在线案例！

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Key Features Learn PyTorch for implementing cutting-edge deep learning algorithms. Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios; Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples;

What is Machine Learning? What problems does it try to solve? What are the main categories and fundamental concepts of Machine Learning systems? • The main steps in a typical Machine Learning project. • Learning by fitting a model to data. • Optimizing a cost function. • Handling, cleaning, and preparing data. • Selecting and engineering features. • Selecting a model and tuning hyperparameters using cross-validation. • The main challenges of Machine Learning, in particular underfitting and overfitting (the bias/variance tradeoff). • Reducing the dimensionality of the training data to fight the curse of dimensionality. • The most common learning algorithms: Linear and Polynomial Regression, Logistic Regression, k-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests, and Ensemble methods. What are neural nets? What are they good for? • Building and training neural nets using TensorFlow. • The most important neural net architectures: feedforward neural nets, convolutional nets, recurrent nets, long short-term memory (LSTM) nets, and autoencoders. • Techniques for training deep neural nets. • Scaling neural networks for huge datasets. • Reinforcement learning.

Derive useful insights from your data using Python. You will learn both basic and advanced concepts, including text and language syntax, structure, and semantics. You will focus on algorithms and techniques, such as text classification, clustering, topic modeling, and text summarization. Text Analytics with Python teaches you the techniques related to natural language processing and text analytics, and you will gain the skills to know which technique is best suited to solve a particular problem. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems. What You Will Learn: Understand the major concepts and techniques of natural language processing (NLP) and text analytics, including syntax and structure Build a text classification system to categorize news articles, analyze app or game reviews using topic modeling and text summarization, and cluster popular movie synopses and analyze the sentiment of movie reviews Implement Python and popular open source libraries in NLP and text analytics, such as the natural language toolkit (nltk), gensim, scikit-learn, spaCy and Pattern

This book starts off by laying the foundation for Natural Language Processing and why Python is one of the best options to build an NLP-based expert system with advantages such as Community support, availability of frameworks and so on. Laterit gives you a better understanding of available free forms of corpus and different types of dataset. After this, you will know how to choose a dataset for natural language processing applications and find the right NLP techniques to process sentences in datasets and understand their structure. You will also learn how to tokenize different parts of sentences and ways to analyze them. During the course of the book, you will explore the semantic as well as syntactic analysis of text. You will understand how to solve various ambiguities in processing human language and will come across various scenarios while performing text analysis. You will learn the very basics of getting the environment ready for natural language processing, move on to the initial setup, and then quickly understand sentences and language parts. You will learn the power of Machine Learning and Deep Learning to extract information from text data. By the end of the book, you will have a clear understanding of natural language processing and will have worked on multiple examples that implement NLP in the real world.

Starting with installing Hadoop YARN, MapReduce, HDFS, and other Hadoop ecosystem components, with this book, you will soon learn about many exciting topics such as MapReduce patterns, using Hadoop to solve analytics, classifications, online marketing, recommendations, and data indexing and searching. You will learn how to take advantage of Hadoop ecosystem projects including Hive, HBase, Pig, Mahout, Nutch, and Giraph and be introduced to deploying in cloud environments.

《Deep Learning with TensorFlow》[随书源代码,《Deep Learning with TensorFlow》[随书源代码

tensorflow深度学习，使用encoder-decoder模型，进行诗词训练

TensorFlow For Machine Intelligence: A hands-on introduction to learning algorithms by Sam Abrahams English | 23 July 2016 | ASIN: B01IZ43JV4 | 322 Pages | AZW3/MOBI/EPUB/PDF (conv)

Key Features Implement various deep-learning algorithms in Keras and see how deep-learning can be used in games See how various deep-learning models and practical use-cases can be implemented using Ke ras A practical, hands-on guide with real-world examples to give you a strong foundation in Keras Book Description This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GAN). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks.