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## Hands–On Machine Learning with Scikit–Learn and TensorFlow ler livros on-line

- Autor: Aurelien Geron
- Editor: O′Reilly
- Data de publicação: 2017-03-24
- ISBN: 1491962291
- Número de páginas: 566 pages
- Tag: hands, machine, learning, scikit, learn, tensorflow

**Graphics in this book are printed in black and white**.

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 production-ready Python frameworks--scikit-learn 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 scikit-learn to track an example machine-learning project end-to-end
- 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

## Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems (English Edition) ler livros on-line

- Autor: Aurélien Géron
- Editor: O'Reilly Media
- Data de publicação: 2017-03-13
- Número de páginas: 576 pages
- Tag: hands, machine, learning, scikit, learn, tensorflow, concepts, tools, techniques, build, intelligent, systems, english, edition

**Graphics in this book are printed in black and white**.

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 production-ready Python frameworks—scikit-learn 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 scikit-learn to track an example machine-learning project end-to-end
- 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

## Applied Artificial Intelligence: Neural networks and deep learning with Python and TensorFlow (English Edition) ler livros on-line

- Autor: Wolfgang Beer
- Editor: Wolfgang Beer (www.smartlab.at)
- Data de publicação: 2017-01-04
- Número de páginas: 76 pages
- Tag: applied, artificial, intelligence, neural, networks, learning, python, tensorflow, english, edition

What are the secrets of modern Artificial Intelligence?

How does AI beat humans in various domains, such as playing Go or predicting the future?

How can I implement my own Artificial Intelligence and push it into production with Google TensorFlow?

This book is about uncovering the basics of Artificial Neural Networks (ANN) and deep learning and how to implement AI models for production systems by using TensorFlow.

The first part of this book explains how to implement your own neural networks in Python and to apply this technique to any given problem. In step-by-step examples, the reader learns how to implement neural networks in Python and to solve non-linear problems. The book explains how neural networks are built, trained with sample data sets and how these networks are capable of solving complex problems.

The simplicity of the tutorial as well as the simple syntax of the Python language quickly enables the reader to transfer that knowledge and algorithms to any other programming language of choice.

Examples cover the design of simple neural networks for solving math functions or character recognition by using neural networks written in Python.

The second part of the book shows how to build machine learning models in Google TensorFlow and how to bring your Artificial Intelligence into production.

TensorFlow is one of the most advanced open source machine learning frameworks available today. TensorFlow easily enables data scientists to push their Artificial Intelligence into a scalable production environment.

The third part of the book is dedicated to practical and fun machine learning examples, such as to calculate book recommendations or to predict the chance of survival for passengers of RMS Titanic.

Outline

Introduction

Artificial intelligence

Neural networks and deep learning

Activation of a neuron

Training a single neuron

Model a network of neurons

Handwriting and character recognition

AI in production with TensorFlow

System architecture

Distribution architecture

Building your first computational graph

Visualizing a computational graph with TensorBoard

Implement a first linear regression model

TensorFlow high level learning API: tf.estimator

Titanic: Can we train a model to predict survival?

Crowd Intelligence: Build your own book recommender

Summary

References

Contact and download links

Credits

How does AI beat humans in various domains, such as playing Go or predicting the future?

How can I implement my own Artificial Intelligence and push it into production with Google TensorFlow?

This book is about uncovering the basics of Artificial Neural Networks (ANN) and deep learning and how to implement AI models for production systems by using TensorFlow.

The first part of this book explains how to implement your own neural networks in Python and to apply this technique to any given problem. In step-by-step examples, the reader learns how to implement neural networks in Python and to solve non-linear problems. The book explains how neural networks are built, trained with sample data sets and how these networks are capable of solving complex problems.

The simplicity of the tutorial as well as the simple syntax of the Python language quickly enables the reader to transfer that knowledge and algorithms to any other programming language of choice.

Examples cover the design of simple neural networks for solving math functions or character recognition by using neural networks written in Python.

The second part of the book shows how to build machine learning models in Google TensorFlow and how to bring your Artificial Intelligence into production.

TensorFlow is one of the most advanced open source machine learning frameworks available today. TensorFlow easily enables data scientists to push their Artificial Intelligence into a scalable production environment.

The third part of the book is dedicated to practical and fun machine learning examples, such as to calculate book recommendations or to predict the chance of survival for passengers of RMS Titanic.

Outline

Introduction

Artificial intelligence

Neural networks and deep learning

Activation of a neuron

Training a single neuron

Model a network of neurons

Handwriting and character recognition

AI in production with TensorFlow

System architecture

Distribution architecture

Building your first computational graph

Visualizing a computational graph with TensorBoard

Implement a first linear regression model

TensorFlow high level learning API: tf.estimator

Titanic: Can we train a model to predict survival?

Crowd Intelligence: Build your own book recommender

Summary

References

Contact and download links

Credits

## TensorFlow For Dummies (English Edition) ler livros on-line

- Autor: Matthew Scarpino
- Editor: For Dummies
- Data de publicação: 2018-03-07
- Número de páginas: 338 pages
- Tag: tensorflow, dummies, english, edition

**Become a machine learning pro!**

Google TensorFlow has become the darling of financial firms and research organizations, but the technology can be intimidating and the learning curve is steep. Luckily, *TensorFlow For Dummies* is here to offer you a friendly, easy-to-follow book on the subject. Inside, you’ll find out how to write applications with TensorFlow, while also grasping the concepts underlying machine learning—all without ever losing your cool!

Machine learning has become ubiquitous in modern society, and its applications include language translation, robotics, handwriting analysis, financial prediction, and image recognition. TensorFlow is Google's preeminent toolset for machine learning, and this hands-on guide makes it easy to understand, even for those without a background in artificial intelligence.

- Install TensorFlow on your computer
- Learn the fundamentals of statistical regression and neural networks
- Visualize the machine learning process with TensorBoard
- Perform image recognition with convolutional neural networks (CNNs)
- Analyze sequential data with recurrent neural networks (RNNs)
- Execute TensorFlow on mobile devices and the Google Cloud Platform (GCP)

If you’re a manager or software developer looking to use TensorFlow for machine learning, this is the book you’ll want to have close by.

## Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow (Machine Learning in Python) (English Edition) ler livros on-line

- Autor: LazyProgrammer
- Data de publicação: 2016-03-11
- Número de páginas: 50 pages
- Tag: learning, python, master, science, machine, learning, modern, neural, networks, written, python, theano, tensorflow, machine, learning, python, english, edition

## Deep Learning

Deep learning is making waves. At the time of this writing (March 2016), Google’s AlghaGo program just beat 9-dan professional Go player Lee Sedol at the game of Go, a Chinese board game.

Experts in the field of Artificial Intelligence thought we were 10 years away from achieving a victory against a top professional Go player, but progress seems to have accelerated!

While deep learning is a complex subject, it is not any more difficult to learn than any other machine learning algorithm. I wrote this book to introduce you to the basics of neural networks. You will get along fine with undergraduate-level math and programming skill.

All the materials in this book can be downloaded and installed for free. We will use the Python programming language, along with the numerical computing library Numpy. I will also show you in the later chapters how to build a deep network using Theano and TensorFlow, which are libraries built specifically for deep learning and can accelerate computation by taking advantage of the GPU.

Unlike other machine learning algorithms, deep learning is particularly powerful because it automatically learns features. That means you don’t need to spend your time trying to come up with and test “kernels” or “interaction effects” - something only statisticians love to do. Instead, we will let the neural network learn these things for us. Each layer of the neural network learns a different abstraction than the previous layers. For example, in image classification, the first layer might learn different strokes, and in the next layer put the strokes together to learn shapes, and in the next layer put the shapes together to form facial features, and in the next layer have a high level representation of faces.

On top of all this, deep learning is known for winning its fair share Kaggle contests. These are machine learning contests that are open to anyone in the world who are allowed to use any machine learning technique they want. Deep learning is that powerful.

Do you want a gentle introduction to this “dark art”, with practical code examples that you can try right away and apply to your own data? Then this book is for you.

## Who is this book NOT for?

Deep Learning and Neural Networks are usually taught at the upper-year undergraduate level. That should give you some idea of the type of knowledge you need to understand this kind of material.

You absolutely need exposure to calculus to understand deep learning, no matter how simple the instructor makes things. Linear algebra would help. I will assume familiarity with Python (although it is an easy language to pick up). You will need to have some concept of machine learning. If you know about algorithms like logistic regression already, this book is perfect for you. If not, you might want to check out my “prerequisites” book, at: http://amzn.com/B01D7GDRQ2

On the other hand, this book is more like a casual primer than a dry textbook. If you are looking for material on more advanced topics, like LSTMs, convolutional neural networks, or reinforcement learning, I have online courses that teach this material, for example: https://www.udemy.com/deep-learning-convolutional-neural-networks-theano-tensorflow

New libraries like TensorFlow are being updated constantly. This is not an encyclopedia for these libraries (as such a thing would be impossible to keep up to date). In the one (1!!!) month since the book was first published, no less than THREE new wrapper libraries for TensorFlow have been released to make coding deep networks easier. To try and incorporate every little update would not only be impossible, but would continually cause parts of the book to be obsolete. Nobody wants that. This book, rather, includes fundamentals. Understanding these building blocks will make tackling these new libraries and features a piece of cake - that is my goal.

## Learning TensorFlow ler livros on-line

- Autor: Tom Hope
- Editor: O′Reilly
- Data de publicação: 2017-09-12
- ISBN: 1491978511
- Número de páginas: 242 pages
- Tag: learning, tensorflow

Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics.

Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience--from data scientists and engineers to students and researchers. You'll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems in TensorFlow.

- Get up and running with TensorFlow, rapidly and painlessly
- Learn how to use TensorFlow to build deep learning models from the ground up
- Train popular deep learning models for computer vision and NLP
- Use extensive abstraction libraries to make development easier and faster
- Learn how to scale TensorFlow, and use clusters to distribute model training
- Deploy TensorFlow in a production setting

## Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition (English Edition) ler livros on-line

- Autor: Sebastian Raschka
- Editor: Packt Publishing
- Data de publicação: 2017-09-20
- Número de páginas: 624 pages
- Tag: python, machine, learning, machine, learning, learning, python, scikit, learn, tensorflow, edition, english, edition

#### Key Features

- Second edition of the bestselling book on Machine Learning
- A practical approach to key frameworks in data science, machine learning, and deep learning
- Use the most powerful Python libraries to implement machine learning and deep learning
- Get to know the best practices to improve and optimize your machine learning systems and algorithms

#### Book Description

Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka's bestselling book, Python Machine Learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.

Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library.

Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you'll be ready to meet the new data analysis opportunities in today's world.

If you've read the first edition of this book, you'll be delighted to find a new balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You'll be able to learn and work with TensorFlow more deeply than ever before, and get essential coverage of the Keras neural network library, along with the most recent updates to scikit-learn.

#### What you will learn

- Understand the key frameworks in data science, machine learning, and deep learning
- Harness the power of the latest Python open source libraries in machine learning
- Explore machine learning techniques using challenging real-world data
- Master deep neural network implementation using the TensorFlow library
- Learn the mechanics of classification algorithms to implement the best tool for the job
- Predict continuous target outcomes using regression analysis
- Uncover hidden patterns and structures in data with clustering
- Delve deeper into textual and social media data using sentiment analysis

#### 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 Scikit-Learn
- 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

## The Mostly Mathless Guide to TensorFlow Machine Learning (English Edition) ler livros on-line

- Autor: Henry Dang
- Data de publicação: 2018-01-26
- Número de páginas: 65 pages
- Tag: mostly, mathless, guide, tensorflow, machine, learning, english, edition

# Description

Machine learning is hard. But it doesn't have to be. Between the linear algebra, multivariate calculus, probability theory, topology, and statistics, machine learning can seem all but impossible without a PhD in mathematics. The reality is that you can understand and become proficient in machine learning with the math skills of an

**eighth grader**.

In Henry Dang's,

*The Mostly Mathless Guide to TensorFlow Machine Learning*, you will learn the high-level ideas behind machine learning, and even create several different kinds of neural networks, all without knowing

*any*of the complicated math. At its core, machine learning is simple and beautiful. But this simplicity can be lost in the large waves of long formulas and complicated math symbols.

**In this book, you will be able to**

- Understand the key ideas behind machine learning and neural networks, without any of the math
- Understand the
*why*behind machine learning, and not just the*how* - Use Python and TensorFlow to create machine learning programs
- Create three different kinds of neural networks -- vanilla neural networks, convolutional neural networks, and recurrent neural networks
- Accurately identify handwritten numbers with up to a 99% accuracy using the MNIST dataset, in multiple different ways

## Practical Computer Vision Applications Using Deep Learning with CNNs: With Detailed Examples in Python Using TensorFlow and Kivy ler livros on-line

- Autor: Ahmed Fawzy Gad
- Editor: Apress
- Data de publicação: 2019-01-07
- ISBN: 1484241665
- Número de páginas: 405 pages
- Tag: practical, computer, vision, applications, using, learning, detailed, examples, python, using, tensorflow

Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book

*starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms.*For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to give you a full understanding of the model.

After consolidating the basics, you will use TensorFlow to build a practical image-recognition model that you will deploy to a web server using Flask, making it accessible over the Internet. Using Kivy and NumPy, you will create cross-platform data science applications with low overheads.

This book will help you apply deep learning and computer vision concepts from scratch, step-by-step from conception to production.

**What You Will Learn**

- Understand how ANNs and CNNs work
- Create computer vision applications and CNNs from scratch using Python
- Follow a deep learning project from conception to production using TensorFlow
- Use NumPy with Kivy to build cross-platform data science applications

**Who This Book Is For**

Data scientists, machine learning and deep learning engineers, software developers.

## Machine Learning: A Hands-On, Project-Based Introduction to Machine Learning for Absolute Beginners: Mastering Engineering ML Systems using Scikit-Learn and TensorFlow (English Edition) ler livros on-line

- Autor: Gabriel Rhys
- Editor: Tech Time Media Publications
- Data de publicação: 2017-10-09
- Número de páginas: 179 pages
- Tag: machine, learning, hands, project, based, introduction, machine, learning, absolute, beginners, mastering, engineering, systems, using, scikit, learn, tensorflow, english, edition

## Can Machines Really Learn?

**Machine learning (ML) is a type of artificial intelligence (AI)**that provides computers with the ability to learn without being explicitly programmed.

Machine learning has become an essential pillar of IT in all aspects, even though it has been hidden in the recent past. We are increasingly being surrounded by several machine learning-based apps across a broad spectrum of industries. From search engines to anti-spam filters to credit card fraud detection systems, list of machine learning applications is ever-expanding in scope and applications.

The goal of this book is to provide you with a

**hands-on, project-based overview of machine learning systems**and how they are applied over a vast spectrum of applications that underpins AI technology from

**Absolute Beginners to Experts.**

This book is a fast-paced, thorough introduction to Machine Learning that will have you writing programs, solving problems, and making things that work in no time.

This book presents algorithms and approaches in such a way that grounds them in larger systems as you learn about a variety of topics, including:

- Supervised and Unsupervised learning methods
- Artificial Neural Networks
**Hands-on projects based on Real-world applications**- Bayesian learning method
- Reinforcement learning
- And much more

By the end of this book, you should have a strong understanding of machine learning so that you can pursue any further and more advanced learning.

**Learning Outcomes: By the end of this book, you will be able to:**

- Identify potential applications of machine learning in practice
- Describe the core differences in analyses enabled by regression, classification, and clustering
- Select the appropriate machine learning task for a potential application
- Apply regression, classification, and clustering
- Represent your data as features to serve as input to machine learning models
- Utilize a dataset to fit a model to analyze new data
- Build an end-to-end application that uses machine learning at its core
- Implement these techniques in Python