The The Machine Learning Workshop

Author : Hyatt Saleh
Publisher : Packt Publishing Ltd
Page : 285 pages
File Size : 51,8 Mb
Release : 2020-07-22
Category : Computers
ISBN : 9781838985462

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The The Machine Learning Workshop by Hyatt Saleh Pdf

Take a comprehensive and step-by-step approach to understanding machine learning Key FeaturesDiscover how to apply the scikit-learn uniform API in all types of machine learning modelsUnderstand the difference between supervised and unsupervised learning modelsReinforce your understanding of machine learning concepts by working on real-world examplesBook Description Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms. The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you’ll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one. By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms. What you will learnUnderstand how to select an algorithm that best fits your dataset and desired outcomeExplore popular real-world algorithms such as K-means, Mean-Shift, and DBSCANDiscover different approaches to solve machine learning classification problemsDevelop neural network structures using the scikit-learn packageUse the NN algorithm to create models for predicting future outcomesPerform error analysis to improve your model's performanceWho this book is for The Machine Learning Workshop is perfect for machine learning beginners. You will need Python programming experience, though no prior knowledge of scikit-learn and machine learning is necessary.

Author : Mirza Rahim Baig,Thomas V. Joseph,Nipun Sadvilkar,Mohan Kumar Silaparasetty,Anthony So
Publisher : Packt Publishing Ltd
Page : 473 pages
File Size : 53,7 Mb
Release : 2020-07-31
Category : Computers
ISBN : 9781839210563

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The Deep Learning Workshop by Mirza Rahim Baig,Thomas V. Joseph,Nipun Sadvilkar,Mohan Kumar Silaparasetty,Anthony So Pdf

Take a hands-on approach to understanding deep learning and build smart applications that can recognize images and interpret text Key Features Understand how to implement deep learning with TensorFlow and Keras Learn the fundamentals of computer vision and image recognition Study the architecture of different neural networks Book Description Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You'll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you'll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you'll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you'll have learned the skills essential for building deep learning models with TensorFlow and Keras. What you will learn Understand how deep learning, machine learning, and artificial intelligence are different Develop multilayer deep neural networks with TensorFlow Implement deep neural networks for multiclass classification using Keras Train CNN models for image recognition Handle sequence data and use it in conjunction with RNNs Build a GAN to generate high-quality synthesized images Who this book is for If you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly.

Author : Alessandro Palmas,Emanuele Ghelfi,Dr. Alexandra Galina Petre,Mayur Kulkarni,Anand N.S.,Quan Nguyen,Aritra Sen,Anthony So,Saikat Basak
Publisher : Packt Publishing Ltd
Page : 821 pages
File Size : 54,7 Mb
Release : 2020-08-18
Category : Computers
ISBN : 9781800209961

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The The Reinforcement Learning Workshop by Alessandro Palmas,Emanuele Ghelfi,Dr. Alexandra Galina Petre,Mayur Kulkarni,Anand N.S.,Quan Nguyen,Aritra Sen,Anthony So,Saikat Basak Pdf

Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide Key FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook Description Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you’ll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you’ll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning. What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is for If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.

Author : Hyatt Saleh
Publisher : Unknown
Page : 286 pages
File Size : 41,7 Mb
Release : 2020-07-21
Category : Computers
ISBN : 1839219068

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The Machine Learning Workshop - Second Edition by Hyatt Saleh Pdf

Author : HYATT. SALEH
Publisher : Unknown
Page : 0 pages
File Size : 49,8 Mb
Release : 2020
Category : Electronic
ISBN : 1801070067

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MACHINE LEARNING WORKSHOP by HYATT. SALEH Pdf

Author : Peter Edwards,Derek Sleeman
Publisher : Morgan Kaufmann
Page : 497 pages
File Size : 42,8 Mb
Release : 2014-06-28
Category : Computers
ISBN : 9781483298535

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Machine Learning Proceedings 1992 by Peter Edwards,Derek Sleeman Pdf

Machine Learning Proceedings 1992

Author : Hyatt Saleh
Publisher : Packt Publishing Ltd
Page : 329 pages
File Size : 53,6 Mb
Release : 2020-07-22
Category : Computers
ISBN : 9781838981846

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The The Deep Learning with PyTorch Workshop by Hyatt Saleh Pdf

Get a head start in the world of AI and deep learning by developing your skills with PyTorch Key FeaturesLearn how to define your own network architecture in deep learningImplement helpful methods to create and train a model using PyTorch syntaxDiscover how intelligent applications using features like image recognition and speech recognition really process your dataBook Description Want to get to grips with one of the most popular machine learning libraries for deep learning? The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you’re starting from scratch. It's no surprise that deep learning's popularity has risen steeply in the past few years, thanks to intelligent applications such as self-driving vehicles, chatbots, and voice-activated assistants that are making our lives easier. This book will take you inside the world of deep learning, where you'll use PyTorch to understand the complexity of neural network architectures. The Deep Learning with PyTorch Workshop starts with an introduction to deep learning and its applications. You'll explore the syntax of PyTorch and learn how to define a network architecture and train a model. Next, you'll learn about three main neural network architectures - convolutional, artificial, and recurrent - and even solve real-world data problems using these networks. Later chapters will show you how to create a style transfer model to develop a new image from two images, before finally taking you through how RNNs store memory to solve key data issues. By the end of this book, you'll have mastered the essential concepts, tools, and libraries of PyTorch to develop your own deep neural networks and intelligent apps. What you will learnExplore the different applications of deep learningUnderstand the PyTorch approach to building neural networksCreate and train your very own perceptron using PyTorchSolve regression problems using artificial neural networks (ANNs)Handle computer vision problems with convolutional neural networks (CNNs)Perform language translation tasks using recurrent neural networks (RNNs)Who this book is for This deep learning book is ideal for anyone who wants to create and train deep learning models using PyTorch. A solid understanding of the Python programming language and its packages will help you grasp the topics covered in the book more quickly.

Author : Jeremy Howard,Sylvain Gugger
Publisher : O'Reilly Media
Page : 624 pages
File Size : 42,9 Mb
Release : 2020-06-29
Category : Computers
ISBN : 9781492045496

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Deep Learning for Coders with fastai and PyTorch by Jeremy Howard,Sylvain Gugger Pdf

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Author : Aaron Jones,Christopher Kruger,Benjamin Johnston
Publisher : Packt Publishing Ltd
Page : 549 pages
File Size : 42,8 Mb
Release : 2020-07-29
Category : Computers
ISBN : 9781800206243

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The Unsupervised Learning Workshop by Aaron Jones,Christopher Kruger,Benjamin Johnston Pdf

Learning how to apply unsupervised algorithms on unlabeled datasets from scratch can be easier than you thought with this beginner's workshop, featuring interesting examples and activities Key FeaturesGet familiar with the ecosystem of unsupervised algorithmsLearn interesting methods to simplify large amounts of unorganized dataTackle real-world challenges, such as estimating the population density of a geographical areaBook Description Do you find it difficult to understand how popular companies like WhatsApp and Amazon find valuable insights from large amounts of unorganized data? The Unsupervised Learning Workshop will give you the confidence to deal with cluttered and unlabeled datasets, using unsupervised algorithms in an easy and interactive manner. The book starts by introducing the most popular clustering algorithms of unsupervised learning. You'll find out how hierarchical clustering differs from k-means, along with understanding how to apply DBSCAN to highly complex and noisy data. Moving ahead, you'll use autoencoders for efficient data encoding. As you progress, you'll use t-SNE models to extract high-dimensional information into a lower dimension for better visualization, in addition to working with topic modeling for implementing natural language processing (NLP). In later chapters, you'll find key relationships between customers and businesses using Market Basket Analysis, before going on to use Hotspot Analysis for estimating the population density of an area. By the end of this book, you'll be equipped with the skills you need to apply unsupervised algorithms on cluttered datasets to find useful patterns and insights. What you will learnDistinguish between hierarchical clustering and the k-means algorithmUnderstand the process of finding clusters in dataGrasp interesting techniques to reduce the size of dataUse autoencoders to decode dataExtract text from a large collection of documents using topic modelingCreate a bag-of-words model using the CountVectorizerWho this book is for If you are a data scientist who is just getting started and want to learn how to implement machine learning algorithms to build predictive models, then this book is for you. To expedite the learning process, a solid understanding of the Python programming language is recommended, as you'll be editing classes and functions instead of creating them from scratch.

Author : Suvrit Sra,Sebastian Nowozin,Stephen J. Wright
Publisher : MIT Press
Page : 509 pages
File Size : 54,5 Mb
Release : 2012
Category : Computers
ISBN : 9780262016469

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Optimization for Machine Learning by Suvrit Sra,Sebastian Nowozin,Stephen J. Wright Pdf

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

Author : Anthony So,Thomas V. Joseph,Robert Thas John,Andrew Worsley,Dr. Samuel Asare
Publisher : Packt Publishing Ltd
Page : 823 pages
File Size : 54,8 Mb
Release : 2020-08-28
Category : Computers
ISBN : 9781800569409

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The The Data Science Workshop by Anthony So,Thomas V. Joseph,Robert Thas John,Andrew Worsley,Dr. Samuel Asare Pdf

Gain expert guidance on how to successfully develop machine learning models in Python and build your own unique data platforms Key FeaturesGain a full understanding of the model production and deployment processBuild your first machine learning model in just five minutes and get a hands-on machine learning experienceUnderstand how to deal with common challenges in data science projectsBook Description Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities. The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search. Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch. By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently. What you will learnExplore the key differences between supervised learning and unsupervised learningManipulate and analyze data using scikit-learn and pandas librariesUnderstand key concepts such as regression, classification, and clusteringDiscover advanced techniques to improve the accuracy of your modelUnderstand how to speed up the process of adding new featuresSimplify your machine learning workflow for productionWho this book is for This is one of the most useful data science books for aspiring data analysts, data scientists, database engineers, and business analysts. It is aimed at those who want to kick-start their careers in data science by quickly learning data science techniques without going through all the mathematics behind machine learning algorithms. Basic knowledge of the Python programming language will help you easily grasp the concepts explained in this book.

Author : Anthony So,William So,Zsolt Nagy
Publisher : Packt Publishing Ltd
Page : 419 pages
File Size : 48,6 Mb
Release : 2020-07-22
Category : Computers
ISBN : 9781800203730

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The The Applied Artificial Intelligence Workshop by Anthony So,William So,Zsolt Nagy Pdf

With knowledge and information shared by experts, take your first steps towards creating scalable AI algorithms and solutions in Python, through practical exercises and engaging activities Key FeaturesLearn about AI and ML algorithms from the perspective of a seasoned data scientistGet practical experience in ML algorithms, such as regression, tree algorithms, clustering, and moreDesign neural networks that emulate the human brainBook Description You already know that artificial intelligence (AI) and machine learning (ML) are present in many of the tools you use in your daily routine. But do you want to be able to create your own AI and ML models and develop your skills in these domains to kickstart your AI career? The Applied Artificial Intelligence Workshop gets you started with applying AI with the help of practical exercises and useful examples, all put together cleverly to help you gain the skills to transform your career. The book begins by teaching you how to predict outcomes using regression. You’ll then learn how to classify data using techniques such as k-nearest neighbor (KNN) and support vector machine (SVM) classifiers. As you progress, you'll explore various decision trees by learning how to build a reliable decision tree model that can help your company find cars that clients are likely to buy. The final chapters will introduce you to deep learning and neural networks. Through various activities, such as predicting stock prices and recognizing handwritten digits, you'll learn how to train and implement convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By the end of this applied AI book, you'll have learned how to predict outcomes and train neural networks and be able to use various techniques to develop AI and ML models. What you will learnCreate your first AI game in Python with the minmax algorithmImplement regression techniques to simplify real-world dataExperiment with classification techniques to label real-world dataPerform predictive analysis in Python using decision trees and random forestsUse clustering algorithms to group data without manual supportLearn how to use neural networks to process and classify labeled imagesWho this book is for The Applied Artificial Intelligence Workshop is designed for software developers and data scientists who want to enrich their projects with machine learning. Although you do not need any prior experience in AI, it is recommended that you have knowledge of high school-level mathematics and at least one programming language, preferably Python. Although this is a beginner's book, experienced students and programmers can improve their Python skills by implementing the practical applications given in this book.

Author : Mehryar Mohri,Afshin Rostamizadeh,Ameet Talwalkar
Publisher : MIT Press
Page : 505 pages
File Size : 48,9 Mb
Release : 2018-12-25
Category : Computers
ISBN : 9780262351362

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Foundations of Machine Learning, second edition by Mehryar Mohri,Afshin Rostamizadeh,Ameet Talwalkar Pdf

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.

Author : Hyatt Saleh
Publisher : Unknown
Page : 330 pages
File Size : 44,7 Mb
Release : 2020-07-20
Category : Computers
ISBN : 1838989218

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The Deep Learning with PyTorch Workshop by Hyatt Saleh Pdf

Author : Alessandro Palmas,Emanuele Ghelfi,Alexandra Petre,Mayur Kulkarni,Anand N. S.,Quân Nguyễn,Aritra Sen,Anthony So,Saikat Basak
Publisher : Unknown
Page : 0 pages
File Size : 46,7 Mb
Release : 2020
Category : Algorithms
ISBN : OCLC:1192526858

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The Reinforcement Learning Workshop by Alessandro Palmas,Emanuele Ghelfi,Alexandra Petre,Mayur Kulkarni,Anand N. S.,Quân Nguyễn,Aritra Sen,Anthony So,Saikat Basak Pdf

Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide Key Features Use TensorFlow to write reinforcement learning agents for performing challenging tasks Learn how to solve finite Markov decision problems Train models to understand popular video games like Breakout Book Description Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, you'll be guided through different RL environments and frameworks. You'll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you've explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you'll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you'll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you'll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning. What you will learn Use OpenAI Gym as a framework to implement RL environments Find out how to define and implement reward function Explore Markov chain, Markov decision process, and the Bellman equation Distinguish between Dynamic Programming, Monte Carlo, and Temporal Difference Learning Understand the multi-armed bandit problem and explore various strategies to solve it Build a deep Q model network for playing the video game Breakout Who this book is for If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.