Mastering+Java+Machine+Learning-Packt+Publishing(2017).epub下载

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Chapter 1, Machine Learning Review, is a refresher of basic concepts and techniques that the reader would have learned from Packt's Learning Machine Learning in Java or a similar text. This chapter is a review of concepts such as data, data transformation, sampling and bias, features and their impor
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Chapter 1, Machine Learning Review, is a refresher of basic concepts and techniques that the reader would have learned from Packt's Learning Machine Learning in Java or a similar text. This chapter is a review of concepts such as data, data transformation, sampling and bias, features and their importance, supervised learning, unsupervised learning, big data learning, stream and real-time learning, probabilistic graphic models, and semi-supervised learning. Chapter 2, Practical Approach to Real-World Supervised Learning, cobwebs dusted, dives straight into the vast field of supervised learning and the full spectrum of associated techniques. We cover the topics of feature selection and reduction, linear modeling, logistic models, non-linear models, SVM and kernels, ensemble learning techniques such as bagging and boosting, validation techniques and evaluation metrics, and model selection. Using WEKA and RapidMiner, we carry out a detailed case study, going through all the steps from data analysis to analysis of model performance. As in each of the other chapters, the case study is presented as an example to help the reader understand how the techniques introduced in the chapter are applied in real life. The dataset used in the case study is UCI HorseColic. Chapter 3, Unsupervised Machine Learning Techniques, presents many advanced methods in clustering and outlier techniques, with applications. Topics covered are feature selection and reduction in unsupervised data, clustering algorithms, evaluation methods in clustering, and anomaly detection using statistical, distance, and distribution techniques. At the end of the chapter, we perform a case study for both clustering and outlier detection using a real-world image dataset, MNIST. We use the Smile API to do feature reduction and ELKI for learning. Chapter 4, Semi-supervised Learning and Active Learning, gives details of algorithms and techniques for learning when only a small amount labeled data is present. Topics

It is not so often in life that you get a second chance. I remember that only days after we stopped editing the first edition, I kept asking myself, "Why didn't I...?", or "What the heck was I thinking saying it like that?", and on and on. In fact, the first project I started working on after it was

TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models with very high accuracy. This book is your guide to mastering deep learning with TensorFlow with the help of 12 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. You'll learn to train different types of deep learning models using TensorFlow, including CNNs, RNNs, LSTMs, and generative adversarial networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, enterprise AI, and natural language processing, to name a few. You'll train high-performance models to generate captions for images automatically, predict the performance of stocks, and create intelligent chatbots. Some advanced aspects, such as recommender systems and reinforcement learning, are also covered in this book. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow to tackle any kind of problem.

学习Java的主要问题之一是跟上发行数量的增加。例如，Ja...

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