Pragmatic Machine Learning With Python

Pragmatic Machine Learning with Python PDF
Author: Avishek Nag
Publisher: BPB Publications
ISBN: 938984536X
Size: 46.41 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 340
View: 117

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Pragmatic Machine Learning With Python

by Avishek Nag, Pragmatic Machine Learning With Python Books available in PDF, EPUB, Mobi Format. Download Pragmatic Machine Learning With Python books, An easy-to-understand guide to learn practical Machine Learning techniques with Mathematical foundations KEY FEATURES - A balanced combination of underlying mathematical theories & practical examples with Python code - Coverage of latest topics like multi-label classification, Text Mining, Doc2Vec, Word2Vec, XMeans clustering, unsupervised outlier detection, techniques to deploy ML models in production-grade systems with PMML, etc - Coverage of sufficient & relevant visualization techniques specific to any topic DESCRIPTION This book will be ideal for working professionals who want to learn Machine Learning from scratch. The first chapter will be an introductory chapter to make readers comfortable with the idea of Machine Learning and the required mathematical theories. There will be a balanced combination of underlying mathematical theories corresponding to any Machine Learning topic and its implementation using Python. Most of the implementations will be based on ‘scikit-learn,’ but other Python libraries like ‘Gensim’ or ‘PyTorch’ will also be used for some topics like text analytics or deep learning. The book will be divided into chapters based on primary Machine Learning topics like Classification, Regression, Clustering, Deep Learning, Text Mining, etc. The book will also explain different techniques of putting Machine Learning models into production-grade systems using Big Data or Non-Big Data flavors and standards for exporting models. WHAT WILL YOU LEARN - Get familiar with practical concepts of Machine Learning from ground zero - Learn how to deploy Machine Learning models in production - Understand how to do “Data Science Storytelling” - Explore the latest topics in the current industry about Machine Learning WHO THIS BOOK IS FOR This book would be ideal for experienced Software Professionals who are trying to get into the field of Machine Learning. Anyone who wishes to Learn Machine Learning concepts and models in the production lifecycle. TABLE OF CONTENTS 1. Introduction to Machine Learning & Mathematical preliminaries 2. Classification 3. Regression 4. Clustering 5. Deep Learning & Neural Networks 6. Miscellaneous Unsupervised Learning 7. Text Mining 8. Machine Learning models in production 9. Case Studies & Data Science Storytelling


Large Scale Machine Learning With Python

Large Scale Machine Learning with Python PDF
Author: Bastiaan Sjardin
Publisher: Packt Publishing Ltd
ISBN: 1785888021
Size: 72.20 MB
Format: PDF
Category : Computers
Languages : en
Pages : 420
View: 7043

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Large Scale Machine Learning With Python

by Bastiaan Sjardin, Large Scale Machine Learning With Python Books available in PDF, EPUB, Mobi Format. Download Large Scale Machine Learning With Python books, Learn to build powerful machine learning models quickly and deploy large-scale predictive applications About This Book Design, engineer and deploy scalable machine learning solutions with the power of Python Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful. What You Will Learn Apply the most scalable machine learning algorithms Work with modern state-of-the-art large-scale machine learning techniques Increase predictive accuracy with deep learning and scalable data-handling techniques Improve your work by combining the MapReduce framework with Spark Build powerful ensembles at scale Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine In Detail Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python. Style and Approach This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly. Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production. This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.


Pragmatic Deep Learning For Dummies

Pragmatic Deep Learning for Dummies PDF
Author: Benjamin Young
Publisher:
ISBN: 9781796530865
Size: 42.90 MB
Format: PDF, Mobi
Category : Computers
Languages : en
Pages : 404
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Pragmatic Deep Learning For Dummies

by Benjamin Young, Pragmatic Deep Learning For Dummies Books available in PDF, EPUB, Mobi Format. Download Pragmatic Deep Learning For Dummies books, Summary This book will help you learn and grasp deep learning technology from ground zero with many interesting real world examples. It could also be used as a quick guide on how to use and understand deep learning in the real life. Description AI especially Deep Learning has made tremendous progress in recent years. It start to spread to all industries. Quote from Andrew Ng, a famous AI researcher: "AI is the new electricity. About 100 years ago, electricity transformed every major industry. AI has advanced to the point where it has the power to transform every major sector in coming years." Unless you are a refresh graduated student with AI/deep learning major, many of us do not have a formal machine learning/deep learning training before, so it is time to keep updated with latest technology. This book will help you learn and grasp deep learning technology from ground zero with many interesting real world examples using python/keras. The books could also be used as a quick guide on how to use and understand deep learning in the real life. Readers should have basic knowledge of python, scripting etc. Free lifetime upgrade for later editions ( as an electronic copy ). Please contact author for this. Table of Contents Introduction What is deep learning Deep neural network basic conectps Python and NumPy basic Deep learning development environments MNIST CNN example: A deep dive of how to handle image data Pre-trained model, transfer learning and fine-tuning Recurrent neural network - how to handle sequences data Natural Langauge Processing Optical character recogniztion Audio processing, speech processing Autoencoder network Deep reinforcement learning Learning from scratch (self-play) AlphaZero How to deploy deep learning model.


Programming Machine Learning

Programming Machine Learning PDF
Author: Paolo Perrotta
Publisher: Pragmatic Bookshelf
ISBN: 9781680506600
Size: 54.15 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 342
View: 5301

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Programming Machine Learning

by Paolo Perrotta, Programming Machine Learning Books available in PDF, EPUB, Mobi Format. Download Programming Machine Learning books, You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty. Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system. Start from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. Apart from that, you'll only need your computer, and your code-adept brain.


Pragmatic Ai

Pragmatic AI PDF
Author: Noah Gift
Publisher: Addison-Wesley Professional
ISBN: 0134863917
Size: 18.52 MB
Format: PDF
Category : Computers
Languages : en
Pages : 99998
View: 5888

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Pragmatic Ai

by Noah Gift, Pragmatic Ai Books available in PDF, EPUB, Mobi Format. Download Pragmatic Ai books, Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.


Pragmatic Ai And Machine Learning Core Principles

Pragmatic AI and Machine Learning Core Principles PDF
Author: Noah Gift
Publisher:
ISBN:
Size: 53.15 MB
Format: PDF, ePub, Docs
Category :
Languages : en
Pages :
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Pragmatic Ai And Machine Learning Core Principles

by Noah Gift, Pragmatic Ai And Machine Learning Core Principles Books available in PDF, EPUB, Mobi Format. Download Pragmatic Ai And Machine Learning Core Principles books, 4+ Hours of Video Instruction Machine Learning is the scientific study of models and algorithms that train a computer to make predictions without explicit instruction. Machine Learning is a subset of Artificial Intelligence, which can be defined as computers that mimic human problem-solving. This video demonstrates the core principles of Machine Learning and AI, including supervised Machine Learning, unsupervised Machine Learning, neural networks, and social network theory. Learn to master the foundational concepts of AI and Machine Learning. The LiveLessons video starts with an overview of Artificial Intelligence and covers applications of AI across industries and opportunities in AI for individuals, organizations, and ecosystems. It also covers the difference between narrow, general, and super AI. Description Shore up the foundational knowledge necessary to work with Artificial Intelligence and Machine Learning! This LiveLesson video covers the core principles of Artificial Intelligence and Machine Learning, including how to frame a problem in terms of Machine Learning and how Machine Learning is different than statistics. Learn about fundamental concepts including nearest neighbors, decision trees, and neural networks. The video wraps up covering timely machine learning topics such as cluster analysis, dimensionality reduction, and social networks. Access the code repository for this LiveLesson at https://github.com/noahgift/fundamentals_ai_ml . About the Instructor Noah Gift is lecturer and consultant at UC Davis Graduate School of Management MSBA program the Graduate Data Science program, MSDS, at Northwestern, the Graduate Data Science program at UC Berkeley. He is teaching and designing graduate Machine Learning, AI, Data Science courses and consulting on Machine Learning and Cloud Architecture for students and faculty. These responsibilities include leading a multi-cloud certification initiative for students. Noah is a Python Software Foundation Fellow, AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect and AWS Academy Accredited Instructor, Google Certified Professional Cloud Architect, and Microsoft MTA on Python. Noah was selected to the SME Machine Learning team due to accomplishments in the area of Machine Learning on the AWS platform. He has published more than 100 technical publications, including several books on subjects ranging from Cloud Machine Learning to DevOps. Gift received an MBA fro...


Building Machine Learning Systems With Python

Building Machine Learning Systems with Python PDF
Author: Luis Pedro Coelho
Publisher: Packt Publishing Ltd
ISBN: 1788622227
Size: 26.21 MB
Format: PDF
Category : Computers
Languages : en
Pages : 406
View: 6107

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Building Machine Learning Systems With Python

by Luis Pedro Coelho, Building Machine Learning Systems With Python Books available in PDF, EPUB, Mobi Format. Download Building Machine Learning Systems With Python books, Get more from your data by creating practical machine learning systems with Python Key Features Develop your own Python-based machine learning system Discover how Python offers multiple algorithms for modern machine learning systems Explore key Python machine learning libraries to implement in your projects Book Description Machine learning allows systems to learn things without being explicitly programmed to do so. Python is one of the most popular languages used to develop machine learning applications, which take advantage of its extensive library support. This third edition of Building Machine Learning Systems with Python addresses recent developments in the field by covering the most-used datasets and libraries to help you build practical machine learning systems. Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python, being a dynamic language, allows for fast exploration and experimentation. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and being introduced to libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling and creating recommendation systems. With Building Machine Learning Systems with Python, you’ll gain the tools and understanding required to build your own systems, all tailored to solve real-world data analysis problems. By the end of this book, you will be able to build machine learning systems using techniques and methodologies such as classification, sentiment analysis, computer vision, reinforcement learning, and neural networks. What you will learn Build a classification system that can be applied to text, images, and sound Employ Amazon Web Services (AWS) to run analysis on the cloud Solve problems related to regression using scikit-learn and TensorFlow Recommend products to users based on their past purchases Understand different ways to apply deep neural networks on structured data Address recent developments in the field of computer vision and reinforcement learning Who this book is for Building Machine Learning Systems with Python is for data scientists, machine learning developers, and Python developers who want to learn how to build increasingly complex machine learning systems. You will use Python's machine learning capabilities to develop effective solutions. Prior knowledge of Python programming is expected.


Building Machine Learning Systems With Python Second Edition

Building Machine Learning Systems with Python   Second Edition PDF
Author: Luis Pedro Coelho
Publisher: Packt Publishing Ltd
ISBN: 178439288X
Size: 52.20 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 326
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Building Machine Learning Systems With Python Second Edition

by Luis Pedro Coelho, Building Machine Learning Systems With Python Second Edition Books available in PDF, EPUB, Mobi Format. Download Building Machine Learning Systems With Python Second Edition books, This book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems.


Hands On Deep Learning For Finance

Hands On Deep Learning for Finance PDF
Author: Luigi Troiano
Publisher: Packt Publishing Ltd
ISBN: 1789615348
Size: 19.69 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 442
View: 5970

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Hands On Deep Learning For Finance

by Luigi Troiano, Hands On Deep Learning For Finance Books available in PDF, EPUB, Mobi Format. Download Hands On Deep Learning For Finance books, Take your quantitative strategies to the next level by exploring nine examples that make use of cutting-edge deep learning technologies, including CNNs, LSTMs, GANs, reinforcement learning, and CapsNets Key Features Implement deep learning techniques and algorithms to build financial models Apply modern AI techniques in quantitative market modeling and investment decision making Leverage Python libraries for rapid development and prototyping Book Description Quantitative methods are the vanguard of the investment management industry. This book shows how to enhance trading strategies and investments in financial markets using deep learning algorithms. This book is an excellent reference to understand how deep learning models can be leveraged to capture insights from financial data. You will implement deep learning models using Python libraries such as TensorFlow and Keras. You will learn various deep learning algorithms to build models for understanding financial market dynamics and exploiting them in a systematic manner. This book takes a pragmatic approach to address various aspects of asset management. The information content in non-structured data like news flow is crystalized using BLSTM. Autoencoders for efficient index replication is discussed in detail. You will use CNN to develop a trading signal with simple technical indicators, and improvements offered by more complex techniques such as CapsNets. Volatility is given due emphasis by demonstrating the superiority of forecasts employing LSTM, and Monte Carlo simulations using GAN for value at risk computations. These are then brought together by implementing deep reinforcement learning for automated trading. This book will serve as a continuing reference for implementing deep learning models to build investment strategies. What you will learn Implement quantitative financial models using the various building blocks of a deep neural network Build, train, and optimize deep networks from scratch Use LSTMs to process data sequences such as time series and news feeds Implement convolutional neural networks (CNNs), CapsNets, and other models to create trading strategies Adapt popular neural networks for pattern recognition in finance using transfer learning Automate investment decisions by using reinforcement learning Discover how a risk model can be constructed using D-GAN Who this book is for If you're a finance or investment professional who wants to lead the development of quantitative strategies, this book is for you. With this practical guide, you'll be able to use deep learning methods for building financial models and incorporating them in your investment process. Anyone who wants to enter the fascinating domain of quantitative finance using the power of deep learning algorithms and techniques will also find this book useful. Basic knowledge of machine learning and Python programming is required.


Deep Learning Illustrated

Deep Learning Illustrated PDF
Author: Jon Krohn
Publisher: Addison-Wesley Professional
ISBN: 0135121728
Size: 59.92 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 416
View: 6561

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Deep Learning Illustrated

by Jon Krohn, Deep Learning Illustrated Books available in PDF, EPUB, Mobi Format. Download Deep Learning Illustrated books, "The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." –Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.