Mastering Tensorflow 1x

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Mastering TensorFlow 1.x

Author : Armando Fandango
Publisher : Packt Publishing Ltd
ISBN 13 : 1788297008
Total Pages : 464 pages
Book Rating : 4.7/5 (882 download)

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Book Synopsis Mastering TensorFlow 1.x by : Armando Fandango

Download or read book Mastering TensorFlow 1.x written by Armando Fandango and published by Packt Publishing Ltd. This book was released on 2018-01-22 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build, scale, and deploy deep neural network models using the star libraries in Python Key Features Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras Build, deploy, and scale end-to-end deep neural network models in a production environment Learn to deploy TensorFlow on mobile, and distributed TensorFlow on GPU, Clusters, and Kubernetes Book Description TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs. This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images. You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF Clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You will see how to call TensorFlow and Keras API within the R statistical software, and learn the required techniques for debugging when the TensorFlow API-based code does not work as expected. The book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of TensorFlow and Keras, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems. What you will learn Master advanced concepts of deep learning such as transfer learning, reinforcement learning, generative models and more, using TensorFlow and Keras Perform supervised (classification and regression) and unsupervised (clustering) learning to solve machine learning tasks Build end-to-end deep learning (CNN, RNN, and Autoencoders) models with TensorFlow Scale and deploy production models with distributed and high-performance computing on GPU and clusters Build TensorFlow models to work with multilayer perceptrons using Keras, TFLearn, and R Learn the functionalities of smart apps by building and deploying TensorFlow models on iOS and Android devices Supercharge TensorFlow with distributed training and deployment on Kubernetes and TensorFlow Clusters Who this book is for This book is for data scientists, machine learning engineers, artificial intelligence engineers, and for all TensorFlow users who wish to upgrade their TensorFlow knowledge and work on various machine learning and deep learning problems. If you are looking for an easy-to-follow guide that underlines the intricacies and complex use cases of machine learning, you will find this book extremely useful. Some basic understanding of TensorFlow is required to get the most out of the book.

Mastering TensorFlow 1. X

Author : Armando Fandango
Publisher :
ISBN 13 : 9781788292061
Total Pages : 474 pages
Book Rating : 4.2/5 (92 download)

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Book Synopsis Mastering TensorFlow 1. X by : Armando Fandango

Download or read book Mastering TensorFlow 1. X written by Armando Fandango and published by . This book was released on 2018-01-22 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build, scale, and deploy deep neural network models using the star libraries in Python Key Features Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras Build, deploy, and scale end-to-end deep neural network models in a production environment Learn to deploy TensorFlow on mobile, and distributed TensorFlow on GPU, Clusters, and Kubernetes Book Description TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs. This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images. You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF Clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You will see how to call TensorFlow and Keras API within the R statistical software, and learn the required techniques for debugging when the TensorFlow API-based code does not work as expected. The book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of TensorFlow and Keras, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems. What you will learn Master advanced concepts of deep learning such as transfer learning, reinforcement learning, generative models and more, using TensorFlow and Keras Perform supervised (classification and regression) and unsupervised (clustering) learning to solve machine learning tasks Build end-to-end deep learning (CNN, RNN, and Autoencoders) models with TensorFlow Scale and deploy production models with distributed and high-performance computing on GPU and clusters Build TensorFlow models to work with multilayer perceptrons using Keras, TFLearn, and R Learn the functionalities of smart apps by building and deploying TensorFlow models on iOS and Android devices Supercharge TensorFlow with distributed training and deployment on Kubernetes and TensorFlow Clusters Who this book is for This book is for data scientists, machine learning engineers, artificial intelligence engineers, and for all TensorFlow users who wish to upgrade their TensorFlow knowledge and work on various machine learning and deep learning problems. If you are looking for an easy-to-follow guide that underlines the intricacies and complex use cases of machine learning, you will find this book extremely useful. Some basic understanding of TensorFlow is required to get the most out of the book.

Mastering TensorFlow 2.x

Author : Rajdeep
Publisher : BPB Publications
ISBN 13 : 9391392229
Total Pages : 353 pages
Book Rating : 4.3/5 (913 download)

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Book Synopsis Mastering TensorFlow 2.x by : Rajdeep

Download or read book Mastering TensorFlow 2.x written by Rajdeep and published by BPB Publications. This book was released on 2022-03-24 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: Work with TensorFlow and Keras for real performance of deep learning KEY FEATURES ● Combines theory and implementation with in-detail use-cases. ● Coverage on both, TensorFlow 1.x and 2.x with elaborated concepts. ● Exposure to Distributed Training, GANs and Reinforcement Learning. DESCRIPTION Mastering TensorFlow 2.x is a must to read and practice if you are interested in building various kinds of neural networks with high level TensorFlow and Keras APIs. The book begins with the basics of TensorFlow and neural network concepts, and goes into specific topics like image classification, object detection, time series forecasting and Generative Adversarial Networks. While we are practicing TensorFlow 2.6 in this book, the version of Tensorflow will change with time; however you can still use this book to witness how Tensorflow outperforms. This book includes the use of a local Jupyter notebook and the use of Google Colab in various use cases including GAN and Image classification tasks. While you explore the performance of TensorFlow, the book also covers various concepts and in-detail explanations around reinforcement learning, model optimization and time series models. WHAT YOU WILL LEARN ● Getting started with Tensorflow 2.x and basic building blocks. ● Get well versed in functional programming with TensorFlow. ● Practice Time Series analysis along with strong understanding of concepts. ● Get introduced to use of TensorFlow in Reinforcement learning and Generative Adversarial Networks. ● Train distributed models and how to optimize them. WHO THIS BOOK IS FOR This book is designed for machine learning engineers, NLP engineers and deep learning practitioners who want to utilize the performance of TensorFlow in their ML and AI projects. Readers are expected to have some familiarity with Tensorflow and the basics of machine learning would be helpful. TABLE OF CONTENTS 1. Getting started with TensorFlow 2.x 2. Machine Learning with TensorFlow 2.x 3. Keras based APIs 4. Convolutional Neural Networks in Tensorflow 5. Text Processing with TensorFlow 2.x 6. Time Series Forecasting with TensorFlow 2.x 7. Distributed Training and DataInput pipelines 8. Reinforcement Learning 9. Model Optimization 10. Generative Adversarial Networks

TensorFlow Machine Learning Projects

Author : Ankit Jain
Publisher : Packt Publishing Ltd
ISBN 13 : 1789132401
Total Pages : 311 pages
Book Rating : 4.7/5 (891 download)

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Book Synopsis TensorFlow Machine Learning Projects by : Ankit Jain

Download or read book TensorFlow Machine Learning Projects written by Ankit Jain and published by Packt Publishing Ltd. This book was released on 2018-11-30 with total page 311 pages. Available in PDF, EPUB and Kindle. Book excerpt: Implement TensorFlow's offerings such as TensorBoard, TensorFlow.js, TensorFlow Probability, and TensorFlow Lite to build smart automation projects Key FeaturesUse machine learning and deep learning principles to build real-world projectsGet to grips with TensorFlow's impressive range of module offeringsImplement projects on GANs, reinforcement learning, and capsule networkBook Description TensorFlow has transformed the way machine learning is perceived. TensorFlow Machine Learning Projects teaches you how to exploit the benefits—simplicity, efficiency, and flexibility—of using TensorFlow in various real-world projects. With the help of this book, you’ll not only learn how to build advanced projects using different datasets but also be able to tackle common challenges using a range of libraries from the TensorFlow ecosystem. To start with, you’ll get to grips with using TensorFlow for machine learning projects; you’ll explore a wide range of projects using TensorForest and TensorBoard for detecting exoplanets, TensorFlow.js for sentiment analysis, and TensorFlow Lite for digit classification. As you make your way through the book, you’ll build projects in various real-world domains, incorporating natural language processing (NLP), the Gaussian process, autoencoders, recommender systems, and Bayesian neural networks, along with trending areas such as Generative Adversarial Networks (GANs), capsule networks, and reinforcement learning. You’ll learn how to use the TensorFlow on Spark API and GPU-accelerated computing with TensorFlow to detect objects, followed by how to train and develop a recurrent neural network (RNN) model to generate book scripts. By the end of this book, you’ll have gained the required expertise to build full-fledged machine learning projects at work. What you will learnUnderstand the TensorFlow ecosystem using various datasets and techniquesCreate recommendation systems for quality product recommendationsBuild projects using CNNs, NLP, and Bayesian neural networksPlay Pac-Man using deep reinforcement learningDeploy scalable TensorFlow-based machine learning systemsGenerate your own book script using RNNsWho this book is for TensorFlow Machine Learning Projects is for you if you are a data analyst, data scientist, machine learning professional, or deep learning enthusiast with basic knowledge of TensorFlow. This book is also for you if you want to build end-to-end projects in the machine learning domain using supervised, unsupervised, and reinforcement learning techniques

Programming with TensorFlow

Author : Kolla Bhanu Prakash
Publisher : Springer Nature
ISBN 13 : 3030570770
Total Pages : 190 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Programming with TensorFlow by : Kolla Bhanu Prakash

Download or read book Programming with TensorFlow written by Kolla Bhanu Prakash and published by Springer Nature. This book was released on 2021-01-22 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for deep learning, Natural Language Processing (NLP), speech recognition, and general predictive analytics. The book provides a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. The authors begin by working through some basic examples in TensorFlow before diving deeper into topics such as CNN, RNN, LSTM, and GNN. The book is written for those who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. The authors demonstrate TensorFlow projects on Single Board Computers (SBCs).

TensorFlow 1.x Deep Learning Cookbook

Author : Antonio Gulli
Publisher : Packt Publishing Ltd
ISBN 13 : 1788291867
Total Pages : 526 pages
Book Rating : 4.7/5 (882 download)

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Book Synopsis TensorFlow 1.x Deep Learning Cookbook by : Antonio Gulli

Download or read book TensorFlow 1.x Deep Learning Cookbook written by Antonio Gulli and published by Packt Publishing Ltd. This book was released on 2017-12-12 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book Skill up and implement tricky neural networks using Google's TensorFlow 1.x An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful. What You Will Learn Install TensorFlow and use it for CPU and GPU operations Implement DNNs and apply them to solve different AI-driven problems. Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code. Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. Use different regression techniques for prediction and classification problems Build single and multilayer perceptrons in TensorFlow Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases. Learn how restricted Boltzmann Machines can be used to recommend movies. Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. Master the different reinforcement learning methods to implement game playing agents. GANs and their implementation using TensorFlow. In Detail Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. With a problem-solution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future. By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more. Style and approach This book consists of hands-on recipes where you'll deal with real-world problems. You'll execute a series of tasks as you walk through data mining challenges using TensorFlow 1.x. Your one-stop solution for common and not-so-common pain points, this is a book that you must have on the shelf.

Mastering Computer Vision with TensorFlow 2.x

Author : Krishnendu Kar
Publisher : Packt Publishing Ltd
ISBN 13 : 1838826939
Total Pages : 419 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Mastering Computer Vision with TensorFlow 2.x by : Krishnendu Kar

Download or read book Mastering Computer Vision with TensorFlow 2.x written by Krishnendu Kar and published by Packt Publishing Ltd. This book was released on 2020-05-15 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: Apply neural network architectures to build state-of-the-art computer vision applications using the Python programming language Key FeaturesGain a fundamental understanding of advanced computer vision and neural network models in use todayCover tasks such as low-level vision, image classification, and object detectionDevelop deep learning models on cloud platforms and optimize them using TensorFlow Lite and the OpenVINO toolkitBook Description Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition, processing, and analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network architectures and understanding how they work rather than using them as a black box. Next, you'll explore architectures such as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. As you advance, you'll learn to use visual search methods using transfer learning. You'll also cover advanced computer vision concepts such as semantic segmentation, image inpainting with GAN's, object tracking, video segmentation, and action recognition. Later, the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You'll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally, you'll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book, you'll have a solid understanding of computer vision and be able to confidently develop models to automate tasks. What you will learnExplore methods of feature extraction and image retrieval and visualize different layers of the neural network modelUse TensorFlow for various visual search methods for real-world scenariosBuild neural networks or adjust parameters to optimize the performance of modelsUnderstand TensorFlow DeepLab to perform semantic segmentation on images and DCGAN for image inpaintingEvaluate your model and optimize and integrate it into your application to operate at scaleGet up to speed with techniques for performing manual and automated image annotationWho this book is for This book is for computer vision professionals, image processing professionals, machine learning engineers and AI developers who have some knowledge of machine learning and deep learning and want to build expert-level computer vision applications. In addition to familiarity with TensorFlow, Python knowledge will be required to get started with this book.

Intelligent Mobile Projects with TensorFlow

Author : Jeff Tang
Publisher : Packt Publishing Ltd
ISBN 13 : 1788628802
Total Pages : 396 pages
Book Rating : 4.7/5 (886 download)

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Book Synopsis Intelligent Mobile Projects with TensorFlow by : Jeff Tang

Download or read book Intelligent Mobile Projects with TensorFlow written by Jeff Tang and published by Packt Publishing Ltd. This book was released on 2018-05-22 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create Deep Learning and Reinforcement Learning apps for multiple platforms with TensorFlow Key Features Build TensorFlow-powered AI applications for mobile and embedded devices Learn modern AI topics such as computer vision, NLP, and deep reinforcement learning Get practical insights and exclusive working code not available in the TensorFlow documentation Book Description As a developer, you always need to keep an eye out and be ready for what will be trending soon, while also focusing on what's trending currently. So, what's better than learning about the integration of the best of both worlds, the present and the future? Artificial Intelligence (AI) is widely regarded as the next big thing after mobile, and Google's TensorFlow is the leading open source machine learning framework, the hottest branch of AI. This book covers more than 10 complete iOS, Android, and Raspberry Pi apps powered by TensorFlow and built from scratch, running all kinds of cool TensorFlow models offline on-device: from computer vision, speech and language processing to generative adversarial networks and AlphaZero-like deep reinforcement learning. You’ll learn how to use or retrain existing TensorFlow models, build your own models, and develop intelligent mobile apps running those TensorFlow models. You'll learn how to quickly build such apps with step-by-step tutorials and how to avoid many pitfalls in the process with lots of hard-earned troubleshooting tips. What you will learn Classify images with transfer learning Detect objects and their locations Transform pictures with amazing art styles Understand simple speech commands Describe images in natural language Recognize drawing with Convolutional Neural Network and Long Short-Term Memory Predict stock price with Recurrent Neural Network in TensorFlow and Keras Generate and enhance images with generative adversarial networks Build AlphaZero-like mobile game app in TensorFlow and Keras Use TensorFlow Lite and Core ML on mobile Develop TensorFlow apps on Raspberry Pi that can move, see, listen, speak, and learn Who this book is for If you're an iOS/Android developer interested in building and retraining others' TensorFlow models and running them in your mobile apps, or if you're a TensorFlow developer and want to run your new and amazing TensorFlow models on mobile devices, this book is for you. You'll also benefit from this book if you're interested in TensorFlow Lite, Core ML, or TensorFlow on Raspberry Pi.

Python Machine Learning

Author : Ryan Turner
Publisher : Publishing Factory
ISBN 13 :
Total Pages : 185 pages
Book Rating : 4./5 ( download)

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Book Synopsis Python Machine Learning by : Ryan Turner

Download or read book Python Machine Learning written by Ryan Turner and published by Publishing Factory . This book was released on 2020-04-18 with total page 185 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you a novice programmer who wants to learn Python Machine Learning? Are you worried about how to translate what you already know into Python? This book will help you overcome those problems! As machines get ever more complex and perform more and more tasks to free up our time, so it is that new ideas are developed to help us continually improve their speed and abilities. One of these is Python and in Python Machine Learning: 3 books in 1 - The Ultimate Beginner's Guide to Learn Python Machine Learning Step by Step using Scikit-Learn and Tensorflow, you will discover information and advice on: Book 1 • What machine learning is • The history of machine learning • Approaches to machine learning • Support vector machines • Machine learning and neural networks • The Internet of Things (IoT) • The future of machine learning • And more… Book 2 • The principles surrounding Python • Different types of networks so you can choose what works best for you • Features of the system • Real world feature engineering • Understanding the techniques of semi-supervised learning • And more… Book 3 • How advanced tensorflow can be used • Neural network models and how to get the most from them • Machine learning with Generative Adversarial Networks • Translating images with cross domain GANs • TF clusters and how to use them • How to debug TF models • And more… This book has been written specifically for beginners and the simple, step by step instructions and plain language make it an ideal place to start for anyone who has a passing interest in this fascinating subject. Python really is an amazing system and can provide you with endless possibilities when you start learning about it. Get a copy of Python Machine Learning today and see where the future lies.

Data Mining

Author : Mehmed Kantardzic
Publisher : John Wiley & Sons
ISBN 13 : 111951598X
Total Pages : 663 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Data Mining by : Mehmed Kantardzic

Download or read book Data Mining written by Mehmed Kantardzic and published by John Wiley & Sons. This book was released on 2019-10-21 with total page 663 pages. Available in PDF, EPUB and Kindle. Book excerpt: Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results from disciplines such as statistics, artificial intelligence, data bases, pattern recognition, and computer visualization. Advances in deep learning technology have opened an entire new spectrum of applications. The author—a noted expert on the topic—explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter.This third edition presents new and expanded information that: • Explores big data and cloud computing • Examines deep learning • Includes information on convolutional neural networks (CNN) • Offers reinforcement learning • Contains semi-supervised learning and S3VM • Reviews model evaluation for unbalanced data Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field.

Python: Advanced Guide to Artificial Intelligence

Author : Giuseppe Bonaccorso
Publisher : Packt Publishing Ltd
ISBN 13 : 1789951720
Total Pages : 748 pages
Book Rating : 4.7/5 (899 download)

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Book Synopsis Python: Advanced Guide to Artificial Intelligence by : Giuseppe Bonaccorso

Download or read book Python: Advanced Guide to Artificial Intelligence written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2018-12-21 with total page 748 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key FeaturesMaster supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and moreBuild, deploy, and scale end-to-end deep neural network models in a production environmentBook Description This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: Mastering Machine Learning Algorithms by Giuseppe BonaccorsoMastering TensorFlow 1.x by Armando FandangoDeep Learning for Computer Vision by Rajalingappaa ShanmugamaniWhat you will learnExplore how an ML model can be trained, optimized, and evaluatedWork with Autoencoders and Generative Adversarial NetworksExplore the most important Reinforcement Learning techniquesBuild end-to-end deep learning (CNN, RNN, and Autoencoders) modelsWho this book is for This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.

Mastering Deep Learning with TensorFlow: From Fundamentals to Real-World Deployment

Author : Peter Jones
Publisher : Walzone Press
ISBN 13 :
Total Pages : 202 pages
Book Rating : 4./5 ( download)

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Book Synopsis Mastering Deep Learning with TensorFlow: From Fundamentals to Real-World Deployment by : Peter Jones

Download or read book Mastering Deep Learning with TensorFlow: From Fundamentals to Real-World Deployment written by Peter Jones and published by Walzone Press. This book was released on 2024-10-11 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the realm of artificial intelligence with "Mastering Deep Learning with TensorFlow: From Fundamentals to Real-World Deployment." This all-encompassing guide provides an in-depth understanding of AI, machine learning, and deep learning, powered by TensorFlow—Google's leading AI framework. Whether you're a beginner starting your AI journey or a professional looking to elevate your expertise in AI model deployment, this book is tailored to meet your needs. Covering crucial topics like neural network design, convolutional and recurrent neural networks, natural language processing, and computer vision, it offers a robust introduction to TensorFlow and its AI applications. Through hands-on examples and a focus on practical solutions, you'll learn how to apply TensorFlow to solve real-world challenges. From theoretical foundations to deployment techniques, "Mastering Deep Learning with TensorFlow" takes you through every step, preparing you to build, fine-tune, and deploy advanced AI models. By the end, you’ll be ready to harness TensorFlow’s full potential, making strides in the rapidly evolving field of artificial intelligence. This book is an indispensable resource for anyone eager to engage with or advance in AI.

Hands-On Deep Learning with TensorFlow

Author : Dan Van Boxel
Publisher : Packt Publishing Ltd
ISBN 13 : 1787125823
Total Pages : 174 pages
Book Rating : 4.7/5 (871 download)

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Book Synopsis Hands-On Deep Learning with TensorFlow by : Dan Van Boxel

Download or read book Hands-On Deep Learning with TensorFlow written by Dan Van Boxel and published by Packt Publishing Ltd. This book was released on 2017-07-31 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is your guide to exploring the possibilities in the field of deep learning, making use of Google's TensorFlow. You will learn about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data. About This Book Explore various possibilities with deep learning and gain amazing insights from data using Google's brainchild-- TensorFlow Want to learn what more can be done with deep learning? Explore various neural networks with the help of this comprehensive guide Rich in concepts, advanced guide on deep learning that will give you background to innovate in your environment Who This Book Is For If you are a data scientist who performs machine learning on a regular basis, are familiar with deep neural networks, and now want to gain expertise in working with convoluted neural networks, then this book is for you. Some familiarity with C++ or Python is assumed. What You Will Learn Set up your computing environment and install TensorFlow Build simple TensorFlow graphs for everyday computations Apply logistic regression for classification with TensorFlow Design and train a multilayer neural network with TensorFlow Intuitively understand convolutional neural networks for image recognition Bootstrap a neural network from simple to more accurate models See how to use TensorFlow with other types of networks Program networks with SciKit-Flow, a high-level interface to TensorFlow In Detail Dan Van Boxel's Deep Learning with TensorFlow is based on Dan's best-selling TensorFlow video course. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel will be your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data. With Dan's guidance, you will dig deeper into the hidden layers of abstraction using raw data. Dan then shows you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. In this book, Dan shares his knowledge across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces. With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond. Style and Approach This book is your go-to guide to becoming a deep learning expert in your organization. Dan helps you evaluate common and not-so-common deep neural networks with the help of insightful examples that you can relate to, and show how they can be exploited in the real world with complex raw data.

Mastering Reinforcement Learning with Python

Author : Enes Bilgin
Publisher : Packt Publishing Ltd
ISBN 13 : 1838648496
Total Pages : 544 pages
Book Rating : 4.8/5 (386 download)

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Book Synopsis Mastering Reinforcement Learning with Python by : Enes Bilgin

Download or read book Mastering Reinforcement Learning with Python written by Enes Bilgin and published by Packt Publishing Ltd. This book was released on 2020-12-18 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practices Key FeaturesUnderstand how large-scale state-of-the-art RL algorithms and approaches workApply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and moreExplore tips and best practices from experts that will enable you to overcome real-world RL challengesBook Description Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems. What you will learnModel and solve complex sequential decision-making problems using RLDevelop a solid understanding of how state-of-the-art RL methods workUse Python and TensorFlow to code RL algorithms from scratchParallelize and scale up your RL implementations using Ray's RLlib packageGet in-depth knowledge of a wide variety of RL topicsUnderstand the trade-offs between different RL approachesDiscover and address the challenges of implementing RL in the real worldWho this book is for This book is for expert machine learning practitioners and researchers looking to focus on hands-on reinforcement learning with Python by implementing advanced deep reinforcement learning concepts in real-world projects. Reinforcement learning experts who want to advance their knowledge to tackle large-scale and complex sequential decision-making problems will also find this book useful. Working knowledge of Python programming and deep learning along with prior experience in reinforcement learning is required.

IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering

Author : Jefferson Luiz Brum Marques
Publisher : Springer Nature
ISBN 13 : 3031494075
Total Pages : 727 pages
Book Rating : 4.0/5 (314 download)

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Book Synopsis IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering by : Jefferson Luiz Brum Marques

Download or read book IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering written by Jefferson Luiz Brum Marques and published by Springer Nature. This book was released on 2024-01-03 with total page 727 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reports on the latest research and developments in Biomedical Engineering, with a special emphasis on topics of interest and findings achieved in Latin America. This third volume of a 4-volume set covers advances in biomechanical analysis and modeling, neural network based methods for medical diagnosis and therapy, and robots and human-machine interface for rehabilitation. Throughout the book, a special emphasis is given to low-cost technologies and to their development for and applications in clinical settings. Based on the IX Latin American Conference on Biomedical Engineering (CLAIB 2022) and the XXVIII Brazilian Congress on Biomedical Engineering (CBEB 2022), held jointly, and virtually on October 24-28, 2022, from Florianópolis, Brazil, this book provides researchers and professionals in the biomedical engineering field with extensive information on new technologies and current challenges for their clinical applications. .

Deep Learning with TensorFlow

Author : Giancarlo Zaccone
Publisher : Packt Publishing Ltd
ISBN 13 : 1786460122
Total Pages : 316 pages
Book Rating : 4.7/5 (864 download)

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Book Synopsis Deep Learning with TensorFlow by : Giancarlo Zaccone

Download or read book Deep Learning with TensorFlow written by Giancarlo Zaccone and published by Packt Publishing Ltd. This book was released on 2017-04-24 with total page 316 pages. Available in PDF, EPUB and Kindle. Book excerpt: Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of this comprehensive TensorFlow guide About This Book Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide Real-world contextualization through some deep learning problems concerning research and application Who This Book Is For The book is intended for a general audience of people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus. What You Will Learn Learn about machine learning landscapes along with the historical development and progress of deep learning Learn about deep machine intelligence and GPU computing with the latest TensorFlow 1.x Access public datasets and utilize them using TensorFlow to load, process, and transform data Use TensorFlow on real-world datasets, including images, text, and more Learn how to evaluate the performance of your deep learning models Using deep learning for scalable object detection and mobile computing Train machines quickly to learn from data by exploring reinforcement learning techniques Explore active areas of deep learning research and applications In Detail Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you'll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you'll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects. Style and approach This step-by-step guide will explore common, and not so common, deep neural networks and show how these can be exploited in the real world with complex raw data. With the help of practical examples, you will learn how to implement different types of neural nets to build smart applications related to text, speech, and image data processing.

Machine Learning and AI Techniques in Interactive Medical Image Analysis

Author : Panigrahi, Lipismita
Publisher : IGI Global
ISBN 13 : 1668446731
Total Pages : 241 pages
Book Rating : 4.6/5 (684 download)

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Book Synopsis Machine Learning and AI Techniques in Interactive Medical Image Analysis by : Panigrahi, Lipismita

Download or read book Machine Learning and AI Techniques in Interactive Medical Image Analysis written by Panigrahi, Lipismita and published by IGI Global. This book was released on 2022-09-16 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: The healthcare industry is predominantly moving towards affordable, accessible, and quality health care. All organizations are striving to build communication compatibility among the wide range of devices that have operated independently. Recent developments in electronic devices have boosted the research in the medical imaging field. It incorporates several medical imaging techniques and achieves an important goal for health improvement all over the world. Despite the significant advances in high-resolution medical instruments, physicians cannot always obtain the full amount of information directly from the equipment outputs, and a large amount of data cannot be easily exploited without a computer. Machine Learning and AI Techniques in Interactive Medical Image Analysis discusses how clinical efficiency can be improved by investigating the different types of intelligent techniques and systems to get more reliable and accurate diagnostic conclusions. This book further introduces segmentation techniques to locate suspicious areas in medical images and increase the segmentation accuracy. Covering topics such as computer-aided detection, intelligent techniques, and machine learning, this premier reference source is a dynamic resource for IT specialists, computer scientists, diagnosticians, imaging specialists, medical professionals, hospital administrators, medical students, medical technicians, librarians, researchers, and academicians.