Machine Learning and Deep Learning with Python, scikitlearn, and TensorFlow

Table of Contents Giving Computers the Ability to Learn from Data Training Simple Machine Learning Algorithms for Classification A Tour of Machine Learning Classifiers Using ScikitLearn Building Good Training Sets  Data Preprocessing Compressing Data via Dimensionality Reduction Learning Best Practices for Model Evaluation and Hyperparameter Tuning Combining Different Models for Ensemble Learning Applying Machine Learning to Sentiment Analysis Embedding a Machine Learning Model into a Web Application Predicting Continuous Target Variables with Regression Analysis Working with Unlabeled Data  Clustering Analysis Implementing a Multilayer Artificial Neural Network from Scratch Parallelizing Neural Network Training with TensorFlow Going Deeper  The Mechanics of TensorFlow Classifying Images with Deep Convolutional Neural Networks Modeling Sequential Data using Recurrent Neural Networks
 46.64MB
HandsOn Machine Learning with ScikitLearn, Keras, and TensorFlow 2ed 2019.epub
20110526Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two productionready Python frameworks—ScikitLearn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. * Explore the machine learning landscape, particularly neural nets * Use ScikitLearn to track an example machinelearning project endtoend * Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods * Use the TensorFlow library to build and train neural nets * Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning * Learn techniques for training and scaling deep neural nets
 1.28MB
Learning scikitlearn_ Machine Learning in Python
20180404Learning scikitlearn_ Machine Learning in Python [Garreta & Moncecchi 20131125
 21.65MB
HandsOn Machine Learning with ScikitLearn and TensorFlow [Kindle Edition]
20170315HandsOn Machine Learning with ScikitLearn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron English  13 Mar. 2017  ASIN: B06XNKV5TS  581 Pages  AZW3  21.66 MB Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two productionready Python frameworks—scikitlearn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikitlearn to track an example machinelearning project endtoend Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details
 45.20MB
HandsOn Machine Learning with ScikitLearn and TensorFlow
20171228This book assumes that you know close to nothing about Machine Learning. Its goal is to give you the concepts, the intuitions, and the tools you need to actually implement programs capable of learning from data. We will cover a large number of techniques, from the simplest and most commonly used (such as linear regression) to some of the Deep Learning techniques that regularly win competitions. Rather than implementing our own toy versions of each algorithm, we will be using actual productionready Python frameworks: •ScikitLearn is very easy to use, yet it implements many Machine Learning algorithms efficiently, so it makes for a great entry point to learn Machine Learning. •TensorFlow is a more complex library for distributed numerical computation using data flow graphs. It makes it possible to train and run very large neural net‐ works efficiently by distributing the computations across potentially thousands of multiGPU servers. TensorFlow was created at Google and supports many of their largescale Machine Learning applications. It was opensourced in November 2015. The book favors a handson approach, growing an intuitive understanding of Machine Learning through concrete working examples and just a little bit of theory. While you can read this book without picking up your laptop, we highly recommend you experiment with the code examples available online as Jupyter notebooks at https://github.com/ageron/handsonml.
 1.42MB
Learning scikitlearn Machine Learning in Python(PACKT,2013)
20160117Machine learning, the art of creating applications that learn from experience and data, has been around for many years. However, in the era of “big data”, huge amounts of information is being generated. This makes machine learning an unavoidable source of new databased approximations for problem solving. With Learning scikitlearn: Machine Learning in Python, you will learn to incorporate machine learning in your applications. The book combines an introduction to some of the main concepts and methods in machine learning with practical, handson examples of realworld problems. Ranging from handwritten digit recognition to document classification, examples are solved step by step using Scikitlearn and Python. The book starts with a brief introduction to the core concepts of machine learning with a simple example. Then, using realworld applications and advanced features, it takes a deep dive into the various machine learning techniques. You will learn to evaluate your results and apply advanced techniques for preprocessing data. You will also be able to select the best set of features and the best methods for each problem. With Learning scikitlearn: Machine Learning in Python you will learn how to use the Python programming language and the scikitlearn library to build applications that learn from experience, applying the main concepts and techniques of machine learning.
 8.40MB
HandsOn Machine Learning with ScikitLearn and TensorFlow [EPUB]
20170327HandsOn Machine Learning with ScikitLearn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron English  2017  ISBN: 1491962291  566 Pages  EPUB  8.41 MB Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two productionready Python frameworks—scikitlearn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikitlearn to track an example machinelearning project endtoend Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details
 44.73MB
HandsOn Machine Learning with ScikitLearn and TensorFlow 原版pdf by Géron
20180505In 2006, Geoffrey Hinton et al. published a paper1 showing how to train a deep neural network capable of recognizing handwritten digits with stateoftheart precision (>98%). They branded this technique “Deep Learning.” Training a deep neural net was widely considered impossible at the time,2 and most researchers had abandoned the idea since the 1990s. This paper revived the interest of the scientific community and before long many new papers demonstrated that Deep Learning was not only possible, but capable of mindblowing achievements that no other Machine Learning (ML) technique could hope to match (with the help of tremendous computing power and great amounts of data). This enthusiasm soon extended to many other areas of Machine Learning.
 10.5MB
Python Machine Learning Machine Learning and Deep Learning
20180327Python Machine Learning Machine Learning and Deep Learning with Python, scikitlearn, and TensorFlow, 2nd Edition 很受推荐
 41.61MB
HandsOn Machine Learning with ScikitLearn and TensorFlow: Concepts, Tools, and
20180729Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two productionready Python frameworks—scikitlearn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikitlearn to track an example machinelearning project endtoend Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details
 8.40MB
HandsOn Machine Learning with ScikitLearn and TensorFlow (epub)
20180104#1 Best Seller in Artificial Intelligence in Amazon By using concrete examples, minimal theory, and two productionready Python frameworks—scikitlearn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikitlearn to track an example machinelearning project endtoend Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details
 16.5MB
HandsOn Machine Learning with ScikitLearn and TensorFlow ...
20171205This book assumes that you know close to nothing about Machine Learning. Its goal is to give you the concepts, the intuitions, and the tools you need to actually implement programs capable of learning from data. We will cover a large number of techniques, from the simplest and most commonly used (such as linear regression) to some of the Deep Learning techniques that regularly win competitions. Rather than implementing our own toy versions of each algorithm, we will be using actual productionready Python frameworks: ScikitLearn is very easy to use, yet it implements many Machine Learning algorithms efficiently, so it makes for a great entry point to learn Machine Learning. TensorFlow is a more complex library for distributed numerical computation using data flow graphs. It makes it possible to train and run very large neural networks efficiently by distributing the computations across potentially thousands of multiGPU servers. TensorFlow was created at Google and supports many of their large scale Machine Learning applications.
 10.17MB
Python Machine Learning and Deep Learning with python, sklearn, tf,2nd Edition
20180905Python Machine Learning: Machine Learning and Deep Learning with Python, scikitlearn, and TensorFlow, 2nd EditionSep 20, 2017 by Sebastian Raschka and Vahid Mirjalili
 1.27MB
Python Machine Learning Machine Learning And Deep Learning From Scratch
20200622Python Machine Learning Machine Learning And Deep Learning From Scratch Illustrated With Python, ScikitLearn, Keras, Theano And Tensorflow by Moubachir Madani Fadoul (zlib.org).pdf
 192.64MB
Python Machine Learning (2ed) 原版电子书+配套代码
20190114Python Machine Learning  Machine Learning and Deep Learning with Python, scikitlearn and TensorFlow（第二版）原版电子书+配套代码！
 6.90MB
scikitlearn Cookbook  Second Edition
20180421Learn to use scikitlearn operations and functions for Machine Learning and deep learning applications. About This Book Handle a variety of machine learning tasks effortlessly by leveraging the power of scikitlearn Perform supervised and unsupervised learning with ease, and evaluate the performance of your model Practical, easy to understand recipes aimed at helping you choose the right machine learning algorithm
 20.42MB
Python Machine Learning
20171116Python Machine Learning: Machine Learning and Deep Learning with Python, scikitlearn, and TensorFlow, 2nd Edition
 16.14MB
Python Machine Learning 2nd Edition by Sebastian Raschka, Vahid Mirjalili
20171113Python Machine Learning: Machine Learning and Deep Learning with Python, scikitlearn, and TensorFlow, 2nd Edition epup格式，但可以用PDF阅读器直接打开。
 7.12MB
HandsOn Machine Learning with ScikitLearn and Tensorflow
20180915本书是PDF版本，还带目录链接，使用起来非常方便，希望能对学习Tensorflow的同学带来帮助。

下载
智慧校园可视化警校联防系统解决方案.pptx
智慧校园可视化警校联防系统解决方案.pptx

下载
基于NXP TEA1993新世代65W高效率同步整流adaptor方案电路方案
基于NXP TEA1993新世代65W高效率同步整流adaptor方案电路方案

下载
[20170214]永久基本农田数据库标准(2017年版).doc
[20170214]永久基本农田数据库标准(2017年版).doc

下载
社团发现的几篇具体论文+论文翻译.rar
社团发现的几篇具体论文+论文翻译.rar

下载
基于 Microchip(Atmel) ATSAM4S16AU 的四轴飞行器解决方案电路方案
基于 Microchip(Atmel) ATSAM4S16AU 的四轴飞行器解决方案电路方案

下载
fastreport.zip
fastreport.zip

下载
基于美国QUALCOMM蓝牙5.0的对讲机电路方案
基于美国QUALCOMM蓝牙5.0的对讲机电路方案

下载
baidu_offline_mapmaster.zip
baidu_offline_mapmaster.zip

下载
ZipForge v6.92 for Delphi 10.4 Sydney Full Source.rar
ZipForge v6.92 for Delphi 10.4 Sydney Full Source.rar

下载
基于python开发的海关数据采集工具v2.1下载
基于python开发的海关数据采集工具v2.1下载