Deep Learning with Python

Are you stuck in getting started with machine learning with python? A Step-By-Step Guide to Learn and Master Python Machine Learning walks you through steps for getting started with Machine Learning w

Deep Learning is now ubiquitous with applied machine learning. All of the technology giants (e.g. Google, Microsoft, Apple, etc.) are focusing on deep learning based techniques for data analytics and

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using r

The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this c

Powerful smart applications using deep learning algorithms to dominate numerical computing, deep learning, and functional programming.Key FeaturesExplore machine learning techniques with prominent ope

Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutio

Get started with MATLAB for deep learning and AI with this in-depth primer. In this book, you start with machine learning fundamentals, then move on to neural networks, deep learning, and then convolu

This book will teach you how to deploy large-scale dataset in deep neural networks with Hadoop for optimal performance.Starting with understanding what deep learning is, and what the various models as

A practical, scenario-based tutorial, this book will help you get to grips with machine learning with Python and start building your own machine learning projects. By the end of the book you will have

A practical, scenario-based tutorial, this book will help you get to grips with machine learning with Python and start building your own machine learning projects. By the end of the book you will have

Deep Learning in Practice helps you learn how to develop and optimize a model for your projects using Deep Learning (DL) methods and architectures.Key features: Demonstrates a quick review on Python, NumPy, and TensorFlow fundamentals.Explains and provides examples of deploying TensorFlow and Keras in several projects.Explains the fundamentals of Artificial Neural Networks (ANNs).Presents several examples and applications of ANNs.Learning the most popular DL algorithms features.Explains and provides examples for the DL algorithms that are presented in this book.Analyzes the DL network's parameter and hyperparameters.Reviews state-of-the-art DL examples.Necessary and main steps for DL modeling.Implements a Virtual Assistant Robot (VAR) using DL methods.Necessary and fundamental information to choose a proper DL algorithm.Gives instructions to learn how to optimize your DL model IN PRACTICE.This book is useful for undergraduate and graduate students, as well as practitioners in industry

Become proficient in deriving insights from time-series data and analyzing a model's performanceKey Features: Explore popular and modern machine learning methods including the latest online and deep learning algorithmsLearn to increase the accuracy of your predictions by matching the right model with the right problemMaster time-series via real-world case studies on operations management, digital marketing, finance, and healthcareBook Description: Machine learning has emerged as a powerful tool to understand hidden complexities in time-series datasets, which frequently need to be analyzed in areas as diverse as healthcare, economics, digital marketing, and social sciences. These datasets are essential for forecasting and predicting outcomes or for detecting anomalies to support informed decision making.This book covers Python basics for time-series and builds your understanding of traditional autoregressive models as well as modern non-parametric models. You will become confident with

Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects.If you've been curious about machine learning but didn't k

A richly-illustrated, full-color introduction to deep learning that offers visual and conceptual explanations instead of equations. You'll learn how to use key deep learning algorithms without the nee

The world of machine learning is changing all the time. It is so amazing the idea that we are able to take a computer and let it learn as it goes. Without having to write out all of the codes that we need for every situation out there or every input that the user may pick, we are able to write out codes in machine learning, even with Python, in order to let the computer or device learn and make decisions on its own.This guidebook is going to take a closer look at how Python machine learning is able to work, as well as how you can use some of the tools and techniques that come with this process for your own needs. When you are interested in learning more about what machine learning is all about, as well as how you can use a part of the coding from Python inside of this process, then this guidebook is the tool for you Some of the topics that we will explore when we go through this guidebook will include: Understanding some of the basics of machine learning;Some of the different parts tha

This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the w

With much success already attributed to deep learning, this discipline has started making waves throughout science broadly and the life sciences in particular. With this practical book, developers and

How deep learning -- from Google Translate to driverless cars to personal cognitive assistants -- is changing our lives and transforming every sector of the economy.The deep learning revolution has br

Deep Learning for Search teaches readers how to leverage neural networks, NLP, and deep learning techniques to improve search performance.Deep Learning for Search teaches readers how to improve the ef

Foreword by Oliver Schabenberger, PhDExecutive Vice President, Chief Operating Officer and Chief Technology Officer SASDive into deep learning! Machine learning and deep learning are ubiquitous in our

Discover the concepts of deep learning used for natural language processing (NLP), with full-fledged examples of neural network models such as recurrent neural networks, long short-term memory ne

Learn how to solve challenging machine learning problems with Tensorflow, Google’s revolutionary new system for deep learning. If you have some background with basic linear algebra and calculus,

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a t

Looking for one central source where you can learn key findings on machine learning?Deep Learning: The Definitive Guide provides developers and data scientists with the most practical information ava

A gentle journey through the mathematics of neural networks, and making your own using the Python computer language. Neural networks are a key element of deep learning and artificial intelligence, whi

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wr

Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data.Machine Learning with

Learn a simpler and more effective way to analyze data and predict outcomes with PythonMachine Learning in Python shows you how to successfully analyze data using only two core machine learning algori

This book provides a comprehensive overview of the recent advancement in the field of automatic speech recognition with a focus on deep learning models including deep neural networks and many of their

Once you've come to grips with the core Python language, learning how to build Python applications presents a far more interesting challenge. Many critics consider this classic book, now updated for P

Everything you need to know to get into Python coding, with 7 books in one Python All-in-One For Dummies is your one-stop source for answers to all your Python questions. From creating apps to building complex web sites to sorting big data, Python provides a way to get the work done. This book is great as a starting point for those new to coding, and it also makes a perfect reference for experienced coders looking for more than the basics. Apply your Python skills to data analysis, learn to write AI-assisted code using GitHub CoPilot, and discover many more exciting uses for this top programming language. Get started coding in Python--even if you're new to computer programmingReference all the essentials and the latest updates, so your code is air-tightLearn how Python can be a solution for large-scale projects and big datasetsAccelerate your career path with this comprehensive guide to learning PythonExperienced and would-be coders alike will love this easy-to-follow guide to learning

This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations of how realistic deep neural networks actually work. To make results from the theoretical forefront accessible, the authors eschew the subject's traditional emphasis on intimidating formality without sacrificing accuracy. Straightforward and approachable, this volume balances detailed first-principle derivations of novel results with insight and intuition for theorists and practitioners alike. This self-contained textbook is ideal for students and researchers interested in artificial intelligence with minimal prerequisites of linear algebra, calculus, and informal probability theory, and it can easily fill a semester-long course on deep learning theory. For the first time, the exciting practical advances in modern artificial intelligence capabilities can be ma

Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature, including classification, sequence labelling, and sequence-to-sequence problems. Topics covered include statistical machine learning and deep learning models, text classification and structured prediction models, generative and discriminative models, supervised and unsupervised learning with latent variables, neural networks, and transition-based methods. Rich connections are drawn between concepts throughout the book, equipping students with the tools needed to establish a deep understanding of NLP solutions, adapt existing models, and confidently develop innovative models of their own. Featuring a host of examples, intuition, and end of chapter exercises, plus sample code available as an onli

Much has been written about the escalating intolerance of worldviews other than one's own. Reasoned arguments based on facts and data seem to have little impact in our increasingly post-truth culture dominated by social media, fake news, tribalism, and identity politics. Recent advances in the study of human cognition, however, offer insights on how to counter these troubling social trends. In this book, psychologist Jon F. Wergin calls upon recent research in learning theory, social psychology, politics, and the arts to show how a deep learning mindset can be developed in both oneself and others. Deep learning is an acceptance that our understanding of the world around us is only temporary and is subject to constant scrutiny. Someone who is committed to learning deeply does not simply react to experiences, but engages fully with that experience, knowing that the inevitable disquietude is what leads to efficacy in the world.

Much has been written about the escalating intolerance of worldviews other than one's own. Reasoned arguments based on facts and data seem to have little impact in our increasingly post-truth culture dominated by social media, fake news, tribalism, and identity politics. Recent advances in the study of human cognition, however, offer insights on how to counter these troubling social trends. In this book, psychologist Jon F. Wergin calls upon recent research in learning theory, social psychology, politics, and the arts to show how a deep learning mindset can be developed in both oneself and others. Deep learning is an acceptance that our understanding of the world around us is only temporary and is subject to constant scrutiny. Someone who is committed to learning deeply does not simply react to experiences, but engages fully with that experience, knowing that the inevitable disquietude is what leads to efficacy in the world.

Machine Learning with Spark and Python Essential Techniques for Predictive Analytics Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves

The leading experts in system change and learning, with their school-based partners around the world, have created this essential companion to their runaway best-seller, Deep Learning: Engage the Worl

A groundbreaking, flexible approach to computer science and data scienceThe Deitels’ Introduction to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and the Cloud o

A project-based guide to the basics of deep learning.This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep