Le Machine Learning (apprentissage automatique) est de plus en plus utilisé pour la reconnaissance vocale, la traduction automatique, les jeux ou la conduite automobile ... Le Machine Learning gagne rapidement les entreprises, mais il y a pénurie de livres centrés sur la programmation et les conseils pratiques. Cet ouvrage est conçu pour les informaticiens qui souhaitent s'initier à cette discipline d'avenir. Il ne requiert que peu de connaissances en mathématiques et présente les fondamentaux du Machine Learning d'une façon très pratique à l'aide de Scikit-Learn qui est l'un des frameworks de ML les plus utilisés actuellement, à la fois performant et simple d'usage. Des exercices corrigés permettent de s'assurer que l'on a assimilé les concepts et que l'on maîtrise les outils. Des compléments en ligne interactifs avec Jupyter Notebook complètent le livre avec des exemples exécutables que le lecteur peut tester.
Author: Omar Mohout
Publisher: Die Keure Publishing
Pricing is hard as it determines your market position, whether your customers buy from you and whether you can provide the level of service required by those customers Lean Pricing is a practical toolkit that will positively influence your pricing strategy, revealing insights in the different pricing methods and tactics used by successful companies. You will discover a great number of case studies where these methods are successfully applied which will help you set-up or optimize your current pricing strategy. This book will answer the following key questions: • What price can you ask? • What pricing strategy will you adopt? • Whether you launched a startup or work for a big tech company is not important. As long as you believe that pricing plays a key role in your success, this book will provide the guidance, insights and inspiration you need. Lean Pricing is part of the Lean series, a series of books tackling the challenges that technology entrepreneurs and companies are facing. A must-have for startups ! EXCERPT The aim of this book is to provide insights in the different pricing methods, strategies and tactics to set pricing, as well as plenty of case studies where these methods are successfully applied. This is not a book for people that are looking for complex economic theories around price setting. It is rather a no-nonsense, ready-to-apply comprehensive guide for creating and reviewing your pricing strategy that will serve as a work of reference for a long time to come. ABOUT THE AUTHOR Omar Mohout is a Growth Engineer. He is an expert in building repeatable, scalable customer acquisition engines for born-on-the-web companies. Omar is an entrepreneur that turned startup advisor & mentor.
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.
In an age of overflowing data, Machine Learning and Data Science seem to be all the rage. By analyzing data, computers are able to "learn" and generalize from examples of things happening in the real world. They can make predictions and answer questions such as “How much should I price this product?” and “Which type of document is this?”.Prediction APIs are making Machine Learning accessible to everyone and this book is the first that teaches how to use them. You will learn the possibilities offered by these APIs, how to formulate your own Machine Learning problem, and what are the key concepts to grasp — not how algorithms work, so it doesn't take a university degree to understand.Learn more at http://www.louisdorard.com/machine-learning-book
Machine Learning and Security
Author: Clarence Chio, David Freeman
Publisher: "O'Reilly Media, Inc."
Can machine learning techniques solve our computer security problems and finally put an end to the cat-and-mouse game between attackers and defenders? Or is this hope merely hype? Now you can dive into the science and answer this question for yourself! With this practical guide, you’ll explore ways to apply machine learning to security issues such as intrusion detection, malware classification, and network analysis. Machine learning and security specialists Clarence Chio and David Freeman provide a framework for discussing the marriage of these two fields, as well as a toolkit of machine-learning algorithms that you can apply to an array of security problems. This book is ideal for security engineers and data scientists alike. Learn how machine learning has contributed to the success of modern spam filters Quickly detect anomalies, including breaches, fraud, and impending system failure Conduct malware analysis by extracting useful information from computer binaries Uncover attackers within the network by finding patterns inside datasets Examine how attackers exploit consumer-facing websites and app functionality Translate your machine learning algorithms from the lab to production Understand the threat attackers pose to machine learning solutions
Author: Ian Goodfellow, Yoshua Bengio, Aaron Courville
Publisher: MIT Press
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. • Gain a solid reason to use machine learning • Frame your question using financial markets laws • Know your data • Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how.
Natural Language Processing (NLP) offers unbounded opportunities for solving interesting problems in artificial intelligence, making it the latest frontier for developing intelligent, deep learning-based applications. If you're a developer or researcher ready to dive deeper into this rapidly growing area of artificial intelligence, this practical book shows you how to use the PyTorch deep learning framework to implement recently discovered NLP techniques. To get started, all you need is a machine learning background and experience programming with Python. Authors Delip Rao and Goku Mohandas provide you with a solid grounding in PyTorch, and deep learning algorithms, for building applications involving semantic representation of text. Each chapter includes several code examples and illustrations. Get extensive introductions to NLP, deep learning, and PyTorch Understand traditional NLP methods, including NLTK, SpaCy, and gensim Explore embeddings: high quality representations for words in a language Learn representations from a language sequence, using the Recurrent Neural Network (RNN) Improve on RNN results with complex neural architectures, such as Long Short Term Memories (LSTM) and Gated Recurrent Units Explore sequence-to-sequence models (used in translation) that read one sequence and produce another
Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You’ll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques
Explore TensorFlow's capabilities to perform efficient deep learning on images Key Features Discover image processing for machine vision Build an effective image classification system using the power of CNNs Leverage TensorFlow’s capabilities to perform efficient deep learning Book Description TensorFlow is Google’s popular offering for machine learning and deep learning, quickly becoming a favorite tool for performing fast, efficient, and accurate deep learning tasks. Hands-On Deep Learning for Images with TensorFlow shows you the practical implementations of real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient image processing using the power of deep learning. With the help of this book, you will get to grips with the different paradigms of performing deep learning such as deep neural nets and convolutional neural networks, followed by understanding how they can be implemented using TensorFlow. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow and Keras. What you will learn Build machine learning models particularly focused on the MNIST digits Work with Docker and Keras to build an image classifier Understand natural language models to process text and images Prepare your dataset for machine learning Create classical, convolutional, and deep neural networks Create a RESTful image classification server Who this book is for Hands-On Deep Learning for Images with TensorFlow is for you if you are an application developer, data scientist, or machine learning practitioner looking to integrate machine learning into application software and master deep learning by implementing practical projects in TensorFlow. Knowledge of Python programming and basics of deep learning are required to get the best out of this book.
A gentle journey through the mathematics of the Mandelbrot and Julia fractals, and making your own using the Python computer language. Mathematics can be fun, exciting, surprising, and stunningly beautiful. But too few people ever experience this, associating it instead with boring and apparently pointless exercises in trigonometry and solving equations. This guide will take you on an emotional journey, starting from very simple ideas, and exploring some surprising and intricately beautiful behaviors of the very simple mathematics that underlies the famous Mandelbrot fractal. You won't need anything more than basic school mathematics. Part 1 is about ideas. It introduces the mathematical ideas underlying the Mandelbrot fractal, gently with lots of illustrations and examples. Part 2 is practical. It introduces the popular and easy to learn Python programming language, and gradually builds up a program to calculate and visualise the Mandelbrot fractal. Part 3 extends these ideas. It reveals the related Julia fractals, creates 3-dimensional landscapes and shows how even more interesting images can be made using mathematical filters. The fractal image on the cover of this book is created using only the ideas and code developed in this book.
Machine learning analyzes big data to uncover patterns invisible to humans. These technologies help Internet users find things online, make it possible to quickly translate speech, and create smarter video game opponents. Big data and machine learning are used everywhere in society, and the opportunities for their uses are endless.
Author: Stuart Russell, Peter Norvig
Publisher: Createspace Independent Publishing Platform
Artificial Intelligence: A Modern Approach offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
Neural networks have been a mainstay of artificial intelligence since its earliest days. Now, exciting new technologies such as deep learning and convolution are taking neural networks in bold new directions. In this book, we will demonstrate the neural networks in a variety of real-world tasks such as image recognition and data science. We examine current neural network technologies, including ReLU activation, stochastic gradient descent, cross-entropy, regularization, dropout, and visualization.
First Published in 1990. Routledge is an imprint of Taylor & Francis, an informa company.