Best deep learning python tensorflow for 2018
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1. Machine Learning for Absolute Beginners: A Plain English Introduction
Description
Ready to spin up a virtual GPU instance and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?
Well, hold on there...
Before you embark on your epic journey into the world of machine learning, there is basic theory to march through first.
But rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first. As a clear and concise alternative to a textbook, this book offers a practical and high-level introduction to machine learning.
Machine Learning for Absolute Beginners has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home.
This title opens with a general introduction to machine learning from a macro level. The second half of the book is more practical and dives into introducing specific algorithms applied in machine learning, including their pros and cons. At the end of the book, I share insights and advice on further learning and careers in this space.
Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle deep learning and Scikit-learn, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King moment - as a fully grown Simba looking over the Pride Lands of Africa - then this is the book to gently hoist you up and offer you a clear lay of the land.
In this step-by-step guide you will learn:
- The very basics of Machine Learning that all beginners need to master- Association Analysis used in the retail and E-commerce space
- Recommender Systems as you've seen online, including Amazon
- Decision Trees for visually mapping and classifying decision processes
- Regression Analysis to create trend lines and predict trends
- Data Reduction and Principle Component Analysis to cut through the noise
- k-means and k-nearest Neighbor (k-nn) Clustering to discover new data groupings
- Introduction to Deep Learning/Neural Networks
- Bias/Variance to optimize your machine learning model
- How to build your first machine learning model to predict video game sales using Python
- Careers in the field
Please also note that under Amazons Matchbook program, the purchaser of this book can add the Kindle version of this title (valued at $3.99 USD) to their Amazon Kindle library at no cost.
2. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Feature
O Reilly MediaDescription
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 frameworksscikit-learn and TensorFlowauthor Aurlien Gron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. Youll 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 youve 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
3. Introduction to Machine Learning with Python: A Guide for Data Scientists
Description
Machine learning has become an integral part of many commercial applications and research projects, but this field is not exclusive to large companies with extensive research teams. If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.
Youll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Mller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.
With this book, youll learn:
- Fundamental concepts and applications of machine learning
- Advantages and shortcomings of widely used machine learning algorithms
- How to represent data processed by machine learning, including which data aspects to focus on
- Advanced methods for model evaluation and parameter tuning
- The concept of pipelines for chaining models and encapsulating your workflow
- Methods for working with text data, including text-specific processing techniques
- Suggestions for improving your machine learning and data science skills
4. Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning For Beginners)
Description
Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition.Ready to crank up a virtual server and smash through petabytes of data? Want to add 'Machine Learning' to your LinkedIn profile?
Well, hold on there...
Before you embark on your epic journey into the world of machine learning, there is some theory and statistical principles to march through first.
But rather than spend $30-$50 USD on a dense long textbook, you may want to read this book first. As a clear and concise alternative to a textbook, this book provides a practical and high-level introduction to the practical components and statistical concepts found in machine learning. Machine Learning for Absolute Beginners Second Edition has been written and designed for absolute beginners. This means plain-English explanations and no coding experience required. Where core algorithms are introduced, clear explanations and visual examples are added to make it easy and engaging to follow along at home. This major new edition features many topics not covered in the First Edition, including Cross Validation, Data Scrubbing and Ensemble Modeling. Please note that this book is not a sequel to the First Edition, but rather a restructured and revamped version of the First Edition. Readers of the First Edition should not feel compelled to purchase this Second Edition. Disclaimer: If you have passed the 'beginner' stage in your study of machine learning and are ready to tackle coding and deep learning, you would be well served with a long-format textbook. If, however, you are yet to reach that Lion King momentas a fully grown Simba looking over the Pride Lands of Africathen this is the book to gently hoist you up and offer you a clear lay of the land.In this step-by-step guide you will learn:
- How to download free datasets - What tools and machine learning libraries you need - Data scrubbing techniques, including one-hot encoding, binning and dealing with missing data - Preparing data for analysis, including k-fold Validation - Regression analysis to create trend lines - Clustering, including k-means and k-nearest Neighbors - The basics of Neural Networks - Bias/Variance to improve your machine learning model - Decision Trees to decode classification - How to build your first Machine Learning Model to predict house values using Python Frequently Asked Questions Q: Do I need programming experience to complete this book? A: This book is designed for absolute beginners, so no programming experience is required. However, two of the later chapters introduce Python to demonstrate an actual machine learning model, so you will see programming language used in this book. Q: I have already purchased the First Edition of this book, should I purchase this Second Edition? A: As majority of the topics from the First Edition are covered in the Second Edition, you may be better served reading a more advanced title on machine learning. Q: Can I get access to the Kindle version of this book? A: Yes. Under Amazons Matchbook program, the purchaser of this book can add the Kindle version of this title (valued at $3.99 USD) to their Amazon Kindle library at no cost. Q: Does this book include everything I need to become a machine learning expert? A: This book is designed for readers taking their first steps in machine learning and further learning will be required beyond this book to master machine learning.5. Mastering Machine Learning with scikit-learn - Second Edition: Apply effective learning algorithms to real-world problems using scikit-learn
Description
Key Features
- Master popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks
- Learn how to build and evaluate performance of efficient models using scikit-learn
- Practical guide to master your basics and learn from real life applications of machine learning
Book Description
Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. Using powerful algorithms and techniques offered by machine learning you can automate any analytical model.
This book examines a variety of machine learning models including popular machine learning algorithms such as k-nearest neighbors, logistic regression, naive Bayes, k-means, decision trees, and artificial neural networks. It discusses data preprocessing, hyperparameter optimization, and ensemble methods. You will build systems that classify documents, recognize images, detect ads, and more. You will learn to use scikit-learns API to extract features from categorical variables, text and images; evaluate model performance, and develop an intuition for how to improve your models performance.
By the end of this book, you will master all required concepts of scikit-learn to build efficient models at work to carry out advanced tasks with the practical approach.
What you will learn
- Review fundamental concepts such as bias and variance
- Extract features from categorical variables, text, and images
- Predict the values of continuous variables using linear regression and K Nearest Neighbors
- Classify documents and images using logistic regression and support vector machines
- Create ensembles of estimators using bagging and boosting techniques
- Discover hidden structures in data using K-Means clustering
- Evaluate the performance of machine learning systems in common tasks
About the Author
Gavin Hackeling is a data scientist and author. He was worked on a variety of machine learning problems, including automatic speech recognition, document classification, object recognition, and semantic segmentation. An alumnus of the University of North Carolina and New York University, he lives in Brooklyn with his wife and cat.
Table of Contents
- The Fundamentals of Machine Learning
- Simple linear regression
- Classification and Regression with K Nearest Neighbors
- Feature Extraction and Preprocessing
- From Simple Regression to Multiple Regression
- From Linear Regression to Logistic Regression
- Naive Bayes
- Nonlinear Classification and Regression with Decision Trees
- From Decision Trees to Random Forests, and other Ensemble Methods
- The Perceptron
- From the Perceptron to Support Vector Machines
- From the Perceptron to Artificial Neural Networks
- Clustering with K-Means
- Dimensionality Reduction with Principal Component Analysis
6. Machine Learning: The New AI (The MIT Press Essential Knowledge Series)
Feature
Mit PressDescription
A concise overview of machine learningcomputer programs that learn from datawhich underlies applications that include recommendation systems, face recognition, and driverless cars.
Today, machine learning underlies a range of applications we use every day, from product recommendations to voice recognitionas well as some we don't yet use everyday, including driverless cars. It is the basis of the new approach in computing where we do not write programs but collect data; the idea is to learn the algorithms for the tasks automatically from data. As computing devices grow more ubiquitous, a larger part of our lives and work is recorded digitally, and as Big Data has gotten bigger, the theory of machine learningthe foundation of efforts to process that data into knowledgehas also advanced. In this book, machine learning expert Ethem Alpaydin offers a concise overview of the subject for the general reader, describing its evolution, explaining important learning algorithms, and presenting example applications.
Alpaydin offers an account of how digital technology advanced from number-crunching mainframes to mobile devices, putting today's machine learning boom in context. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. Alpaydin then considers some future directions for machine learning and the new field of data science, and discusses the ethical and legal implications for data privacy and security.
7. scikit-learn Cookbook - Second Edition: Over 80 recipes for machine learning in Python with scikit-learn
Description
Learn to use scikit-learn 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 scikit-learn
- 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
Who This Book Is For
Data Analysts already familiar with Python but not so much with scikit-learn, who want quick solutions to the common machine learning problems will find this book to be very useful. If you are a Python programmer who wants to take a dive into the world of machine learning in a practical manner, this book will help you too.
What You Will Learn
- Build predictive models in minutes by using scikit-learn
- Understand the differences and relationships between Classification and Regression, two types of Supervised Learning.
- Use distance metrics to predict in Clustering, a type of Unsupervised Learning
- Find points with similar characteristics with Nearest Neighbors.
- Use automation and cross-validation to find a best model and focus on it for a data product
- Choose among the best algorithm of many or use them together in an ensemble.
- Create your own estimator with the simple syntax of sklearn
- Explore the feed-forward neural networks available in scikit-learn
In Detail
Python is quickly becoming the go-to language for analysts and data scientists due to its simplicity and flexibility, and within the Python data space, scikit-learn is the unequivocal choice for machine learning. This book includes walk throughs and solutions to the common as well as the not-so-common problems in machine learning, and how scikit-learn can be leveraged to perform various machine learning tasks effectively.
The second edition begins with taking you through recipes on evaluating the statistical properties of data and generates synthetic data for machine learning modelling. As you progress through the chapters, you will comes across recipes that will teach you to implement techniques like data pre-processing, linear regression, logistic regression, K-NN, Nave Bayes, classification, decision trees, Ensembles and much more. Furthermore, you ll learn to optimize your models with multi-class classification, cross validation, model evaluation and dive deeper in to implementing deep learning with scikit-learn. Along with covering the enhanced features on model section, API and new features like classifiers, regressors and estimators the book also contains recipes on evaluating and fine-tuning the performance of your model.
By the end of this book, you will have explored plethora of features offered by scikit-learn for Python to solve any machine learning problem you come across.
Style and Approach
This book consists of practical recipes on scikit-learn that target novices as well as intermediate users. It goes deep into the technical issues, covers additional protocols, and many more real-live examples so that you are able to implement it in your daily life scenarios.
8. Machine Learning in Python: Essential Techniques for Predictive Analysis
Feature
WileyDescription
Learn a simpler and more effective way to analyze data andpredict outcomes with PythonMachine Learning in Python shows you how to successfullyanalyze data using only two core machine learning algorithms, andhow to apply them using Python. By focusing on two algorithmfamilies that effectively predict outcomes, this book is able toprovide full descriptions of the mechanisms at work, and theexamples that illustrate the machinery with specific, hackablecode. The algorithms are explained in simple terms with no complexmath and applied using Python, with guidance on algorithmselection, data preparation, and using the trained models inpractice. You will learn a core set of Python programmingtechniques, various methods of building predictive models, and howto measure the performance of each model to ensure that the rightone is used. The chapters on penalized linear regression andensemble methods dive deep into each of the algorithms, and you canuse the sample code in the book to develop your own data analysissolutions.
Machine learning algorithms are at the core of data analyticsand visualization. In the past, these methods required a deepbackground in math and statistics, often in combination with thespecialized R programming language. This book demonstrates howmachine learning can be implemented using the more widely used andaccessible Python programming language.
- Predict outcomes using linear and ensemble algorithmfamilies
- Build predictive models that solve a range of simple andcomplex problems
- Apply core machine learning algorithms using Python
- Use sample code directly to build custom solutions
Machine learning doesn't have to be complex and highlyspecialized. Python makes this technology more accessible to a muchwider audience, using methods that are simpler, effective, and welltested. Machine Learning in Python shows you how to do this,without requiring an extensive background in math orstatistics.
9. Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition
Description
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
10. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Description
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.
The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworksscikit-learn, Keras, and TensorFlowto help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Youll 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 youve learned, all you need is programming experience to get started.
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