Applied Deep Learning With Python PDF Download

Applied Deep Learning with Python PDF
Author: Alex Galea
Publisher: Packt Publishing Ltd
ISBN: 1789806992
Size: 30.75 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 334
View: 1529

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Applied Deep Learning With Python Book Description:

A hands-on guide to deep learning that’s filled with intuitive explanations and engaging practical examples Key Features Designed to iteratively develop the skills of Python users who don’t have a data science background Covers the key foundational concepts you’ll need to know when building deep learning systems Full of step-by-step exercises and activities to help build the skills that you need for the real-world Book Description Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before we train our first predictive model. We’ll explore a variety of approaches to classification like support vector networks, random decision forests and k-nearest neighbours to build out your understanding before we move into more complex territory. It’s okay if these terms seem overwhelming; we’ll show you how to put them to work. We’ll build upon our classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. It’s after this that we start building out our keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. By guiding you through a trained neural network, we’ll explore common deep learning network architectures (convolutional, recurrent, generative adversarial) and branch out into deep reinforcement learning before we dive into model optimization and evaluation. We’ll do all of this whilst working on a production-ready web application that combines Tensorflow and Keras to produce a meaningful user-friendly result, leaving you with all the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively. What you will learn Discover how you can assemble and clean your very own datasets Develop a tailored machine learning classification strategy Build, train and enhance your own models to solve unique problems Work with production-ready frameworks like Tensorflow and Keras Explain how neural networks operate in clear and simple terms Understand how to deploy your predictions to the web Who this book is for If you're a Python programmer stepping into the world of data science, this is the ideal way to get started.

Machine Learning Python PDF Download

Machine Learning Python PDF
Author: Anthony Williams
Publisher: Createspace Independent Publishing Platform
ISBN: 9781977829696
Size: 44.67 MB
Format: PDF, ePub
Category :
Languages : en
Pages : 186
View: 3793

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Machine Learning Python Book Description:

Machine Learning Python - 2 BOOK BUNDLE!! Book 1: Artificial Intelligence with Python It is more than apparent that artificial intelligence techniques and practices will navigate the changes in the near future and simply shape the world. It is fair to say that AP is leading approach when it comes to the various scientific fields as well as various industries and today, it is almost impossible the world without advancements in the artificial intelligence field. Experts and scientists both agree that artificial intelligence is the field which will most certainly shape our economic future, automotive industry, health care, cybersecurity as well as cybercrime. Over the coming decades, AI will greatly impact every aspect of our lives including our work, careers, education, care for elderly and much more. Eventually, it will alter the world completely, as machines will pursue complex goals independently of their creators. AI tools have become mainstream tools when it comes to the various industries and science fields since these tools greatly reduce costs, increase profits and even save lives. If you understand the basic concept behind different AI techniques and approaches, you will be able to greatly benefit from it in various aspects. In order to maximize the benefits of AI advancements, you have to be ready to embark on different challenges. However, with this book, you will be able to overcome challenges and the reward is a success. What you will learn in this book: Different artificial intelligence approaches and goals How to define AI system Basic AI techniques Reinforcement learning How to build a recommender system Genetic and logic programming And much, much more... Book 2: Reinforcement Learning with Python Reinforcement learning is one of those data science fields, which will most certainly shape the world. The changes are already visible since we have self-driving cars, robots and much more we used to see only in some futuristic movies. Reinforcement learning is widely used machine learning technique, a computational approach when it comes to the different software agents, which are trying to maximize the total amount of possible reward they receive while interacting with some uncertain as well as very complex environments. This book is divided into seven chapters in which you will get to reinforcement techniques and methodology better. The first chapters will introduce you to the main concept laying being reinforcement learning techniques. Further, you will see what is the difference between reinforcement learning and other machine learning techniques. The book also provides some of the basic solution methods when it comes to the Markov decision processes, dynamic programming, Monte Carlo methods and temporal difference learning. What you will learn by reading this book: Types of fundamental machine learning algorithms in comparison to reinforcement learning Essentials of reinforcement learning process Marko decision processes and basic parameters How to integrate reinforcement learning algorithm using OpenAI Gym How to integrate Monte Carlo methods for prediction Monte Carlo tree search Dynamic programming in Python for policy evaluation, policy iteration and value iteration Temporal difference learning or TD And much, much more... Get this book bundle NOW and SAVE money!

Python Machine Learning PDF Download

Python Machine Learning PDF
Author: Sebastian Raschka
Publisher: Packt Publishing Ltd
ISBN: 1789958296
Size: 12.63 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 770
View: 6311

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Python Machine Learning Book Description:

Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Key Features Third edition of the bestselling, widely acclaimed Python machine learning book Clear and intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices Book Description Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. What you will learn Master the frameworks, models, and techniques that enable machines to 'learn' from data Use scikit-learn for machine learning and TensorFlow for deep learning Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more Build and train neural networks, GANs, and other models Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who This Book Is For If you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.

Hands On Machine Learning For Cybersecurity PDF Download

Hands On Machine Learning for Cybersecurity PDF
Author: Soma Halder
Publisher: Packt Publishing Ltd
ISBN: 178899096X
Size: 66.29 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 318
View: 5795

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Hands On Machine Learning For Cybersecurity Book Description:

Get into the world of smart data security using machine learning algorithms and Python libraries Key Features Learn machine learning algorithms and cybersecurity fundamentals Automate your daily workflow by applying use cases to many facets of security Implement smart machine learning solutions to detect various cybersecurity problems Book Description Cyber threats today are one of the costliest losses that an organization can face. In this book, we use the most efficient tool to solve the big problems that exist in the cybersecurity domain. The book begins by giving you the basics of ML in cybersecurity using Python and its libraries. You will explore various ML domains (such as time series analysis and ensemble modeling) to get your foundations right. You will implement various examples such as building system to identify malicious URLs, and building a program to detect fraudulent emails and spam. Later, you will learn how to make effective use of K-means algorithm to develop a solution to detect and alert you to any malicious activity in the network. Also learn how to implement biometrics and fingerprint to validate whether the user is a legitimate user or not. Finally, you will see how we change the game with TensorFlow and learn how deep learning is effective for creating models and training systems What you will learn Use machine learning algorithms with complex datasets to implement cybersecurity concepts Implement machine learning algorithms such as clustering, k-means, and Naive Bayes to solve real-world problems Learn to speed up a system using Python libraries with NumPy, Scikit-learn, and CUDA Understand how to combat malware, detect spam, and fight financial fraud to mitigate cyber crimes Use TensorFlow in the cybersecurity domain and implement real-world examples Learn how machine learning and Python can be used in complex cyber issues Who this book is for This book is for the data scientists, machine learning developers, security researchers, and anyone keen to apply machine learning to up-skill computer security. Having some working knowledge of Python and being familiar with the basics of machine learning and cybersecurity fundamentals will help to get the most out of the book

Introduction To Machine Learning With Python PDF Download

Introduction to Machine Learning with Python PDF
Author: Andreas C. Müller
Publisher: "O'Reilly Media, Inc."
ISBN: 1449369898
Size: 16.99 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 400
View: 3498

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Introduction To Machine Learning With Python Book 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. You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller 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, you’ll 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

Deep Learning With Python Second Edition PDF Download

Deep Learning with Python  Second Edition PDF
Author: François Chollet
Publisher:
ISBN: 9781617296864
Size: 38.40 MB
Format: PDF, Kindle
Category :
Languages : en
Pages : 400
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Deep Learning With Python Second Edition Book Description:

Deep Learning with Python, Second Edition is a comprehensive introduction to the field of deep learning using Python and the powerful Keras library. Written by Google AI researcher François Chollet, the creator of Keras, this revised edition has been updated with new chapters, new tools, and cutting-edge techniques drawn from the latest research. You’ll build your understanding through practical examples and intuitive explanations that make the complexities of deep learning accessible and understandable.About the TechnologyMachine learning has made remarkable progress in recent years. We’ve gone from near-unusable speech recognition, to near-human accuracy. From machines that couldn't beat a serious Go player, to defeating a world champion. Medical imaging diagnostics, weather forecasting, and natural language question answering have suddenly become tractable problems. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications across every industry sectorAbout the bookDeep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. You’ll learn directly from the creator of Keras, François Chollet, building your understanding through intuitive explanations and practical examples. Updated from the original bestseller with over 50% new content, this second edition includes new chapters, cutting-edge innovations, and coverage of the very latest deep learning tools. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.

Deep Learning With Python PDF Download

Deep Learning with Python PDF
Author: Francois Chollet
Publisher: Manning Publications
ISBN: 9781617294433
Size: 15.84 MB
Format: PDF, ePub, Mobi
Category : Machine learning
Languages : en
Pages : 384
View: 7349

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Deep Learning With Python Book Description:

Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran�ois Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning--a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran�ois Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author Fran�ois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance

Deep Learning PDF Download

Deep Learning PDF
Author: Frank Millstein
Publisher: Frank Millstein
ISBN:
Size: 71.49 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 266
View: 5355

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Deep Learning Book Description:

Deep Learning - 2 BOOK BUNDLE!! Deep Learning with Keras This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition and much more. Furthermore, you will get familiar with recurrent neural networks like LSTM and GAN as you explore processing sequence data like time series, text, and audio. The book will definitely be your best companion on this great deep learning journey with Keras introducing you to the basics you need to know in order to take next steps and learn more advanced deep neural networks. Here Is a Preview of What You’ll Learn Here… The difference between deep learning and machine learning Deep neural networks Convolutional neural networks Building deep learning models with Keras Multi-layer perceptron network models Activation functions Handwritten recognition using MNIST Solving multi-class classification problems Recurrent neural networks and sequence classification And much more... Convolutional Neural Networks in Python This book covers the basics behind Convolutional Neural Networks by introducing you to this complex world of deep learning and artificial neural networks in a simple and easy to understand way. It is perfect for any beginner out there looking forward to learning more about this machine learning field. This book is all about how to use convolutional neural networks for various image, object and other common classification problems in Python. Here, we also take a deeper look into various Keras layer used for building CNNs we take a look at different activation functions and much more, which will eventually lead you to creating highly accurate models able of performing great task results on various image classification, object classification and other problems. Therefore, at the end of the book, you will have a better insight into this world, thus you will be more than prepared to deal with more complex and challenging tasks on your own. Here Is a Preview of What You’ll Learn In This Book… Convolutional neural networks structure How convolutional neural networks actually work Convolutional neural networks applications The importance of convolution operator Different convolutional neural networks layers and their importance Arrangement of spatial parameters How and when to use stride and zero-padding Method of parameter sharing Matrix multiplication and its importance Pooling and dense layers Introducing non-linearity relu activation function How to train your convolutional neural network models using backpropagation How and why to apply dropout CNN model training process How to build a convolutional neural network Generating predictions and calculating loss functions How to train and evaluate your MNIST classifier How to build a simple image classification CNN And much, much more! Get this book bundle NOW and SAVE money!

Python 3 For Machine Learning PDF Download

Python 3 for Machine Learning PDF
Author: Oswald Campesato
Publisher: Stylus Publishing, LLC
ISBN: 1683924940
Size: 29.98 MB
Format: PDF, ePub, Docs
Category : Computers
Languages : en
Pages : 364
View: 654

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Python 3 For Machine Learning Book Description:

This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fundamental concepts of machine learning. The sixth chapter is devoted to machine learning classifiers, such as logistic regression, k-NN, decision trees, random forests, and SVMs. The final chapter includes material on NLP and RL. Keras-based code samples are included to supplement the theoretical discussion. The book also contains separate appendices for regular expressions, Keras, and TensorFlow 2. Features: Provides the reader with basic Python 3 programming concepts related to machine learning Includes separate appendices for regular expressions, Keras, and TensorFlow 2