Preface I think Python is an amazing platform for machine learning. There are so many algorithms and so much power ready to use. I am often asked the question: How do you use Python for machine learning? This book is my definitive answer to that question. It contains my very best knowledge and ideas on how to work through predictive modeling machine learning projects using the Python ecosystem. It is the book that I am also going to use as a refresher at the start of a new project. I’m really proud of this book and I hope that you find it a useful companion on your machine learning journey with Python. Jason Brownlee Melbourne, Australia 2016

一次课程实验作业，用人脸数据集进行降维处理显示降维处理后的图像

Building Machine Learning Systems with Python: Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow, 3rd Edition

python machine learning it is a good book for people who want to learn deep in ML.

Python Machine Learning Second Edition Python Machine Learning Second Edition Copyright © 2017 Packt Publishing

数据来自：UCI机器学习库 http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wpbc.names “每个记录代表一个乳腺癌病例的随访数据。这些是自1984年以来Wolberg博士所见的连续患者，仅包括那些在诊断时表现出浸润性乳腺癌并且没有远处转移证据的病例。

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统计学习经典教程，小说风格，非扫描高清版，自购收藏。 Will Zach find Alice, the missing love of his life, and save the world? Will he survive the bridge of death? Can he escape the zombie horde? Statistically speaking the odds don’t look good…. Reluctant hero Zach Slade wakes up to find that his soul mate Alice has vanished. To find her, he must solve a puzzle using the only clue he has – Alice’s unfinished research report. If only he hadn’t skipped science class to form a band. The more Zach unravels the enigma of reality, the more he sense that something is very wrong. Did Alice ever exist? Who is the mysterious Professor Milton? What is causing people to forget who they are? And why is everyone intent on teaching him statistics? Join Zach on his bizarre journey … It will transform your understanding of statistics forever.

Preface I wrote this book to help machine learning practitioners, like you, get on top of linear algebra, fast. Linear Algebra Is Important in Machine Learning There is no doubt that linear algebra is important in machine learning. Linear algebra is the mathematics of data. It’s all vectors and matrices of numbers. Modern statistics is described using the notation of linear algebra and modern statistical methods harness the tools of linear algebra. Modern machine learning methods are described the same way, using the notations and tools drawn directly from linear algebra. Even some classical methods used in the field, such as linear regression via linear least squares and singular-value decomposition, are linear algebra methods, and other methods, such as principal component analysis, were born from the marriage of linear algebra and statistics. To read and understand machine learning, you must be able to read and understand linear algebra. Practitioners Study Linear Algebra Too Early If you ask how to get started in machine learning, you will very likely be told to start with linear algebra. We know that knowledge of linear algebra is critically important, but it does not have to be the place to start. Learning linear algebra first, then calculus, probability, statistics, and eventually machine learning theory is a long and slow bottom-up path. A better fit for developers is to start with systematic procedures that get results, and work back to the deeper understanding of theory, using working results as a context. I call this the top-down or results-first approach to machine learning, and linear algebra is not the first step, but perhaps the second or third. Practitioners Study Too Much Linear Algebra When practitioners do circle back to study linear algebra, they learn far more of the field than is required for or relevant to machine learning. Linear algebra is a large field of study that has tendrils into engineering, physics and quantum physics. There are also

Welcome to Long Short-Term Memory Networks With Python. Long Short-Term Memory (LSTM) recurrent neural networks are one of the most interesting types of deep learning at the moment. They have been used to demonstrate world-class results in complex problem domains such as language translation, automatic image captioning, and text generation. LSTMs are very di↵erent to other deep learning techniques, such as Multilayer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs), in that they are designed specifically for sequence prediction problems. I designed this book for you to rapidly discover what LSTMs are, how they work, and how you can bring this important technology to your own sequence prediction problems.