Tensorflow 2 x (25 Ergebnisse)

Paperback. Zustand: Good. Work through practical recipes to learn how to solve complex machine learning and deep learning problems using Python Key Features Get up and running with artificial intelligence in no time using hands-on problem-solving recipes Explore popular Python libraries and tools to build AI solutions for images, text, sounds, and images Implement NLP, reinforcement learning, deep learning, GANs, Monte-Carlo tree search, and much more Book DescriptionArtificial intelligence (AI) plays an integral role in automating problem-solving. This involves predicting and classifying data and training agents to execute tasks successfully. This book will teach you how to solve complex problems with the help of independent and insightful recipes ranging from the essentials to advanced methods that have just come out of research. Artificial Intelligence with Python Cookbook starts by showing you how to set up your Python environment and taking you through the fundamentals of data exploration. Moving ahead, you?ll be able to implement heuristic search techniques and genetic algorithms. In addition to this, you'll apply probabilistic models, constraint optimization, and reinforcement learning. As you advance through the book, you'll build deep learning models for text, images, video, and audio, and then delve into algorithmic bias, style transfer, music generation, and AI use cases in the healthcare and insurance industries. Throughout the book, you?ll learn about a variety of tools for problem-solving and gain the knowledge needed to effectively approach complex problems. By the end of this book on AI, you will have the skills you need to write AI and machine learning algorithms, test them, and deploy them for production.What you will learn Implement data preprocessing steps and optimize model hyperparameters Delve into representational learning with adversarial autoencoders Use active learning, recommenders, knowledge embedding, and SAT solvers Get to grips with probabilistic modeling with TensorFlow probability Run object detection, text-to-speech conversion, and text and music generation Apply swarm algorithms, multi-agent systems, and graph networks Go from proof of concept to production by deploying models as microservices Understand how to use modern AI in practice Who this book is forThis AI machine learning book is for Python developers, data scientists, machine learning engineers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. You?ll also find this book useful if you?re looking for state-of-the-art solutions to perform different machine learning tasks in various use cases. Basic working knowledge of the Python programming language and machine learning concepts will help you to work with code effectively in this book.…

Paperback. Zustand: Very Good+. First Edition. 7.5 X 1.06 X 9.25 inches; 453 pages; Very Good condition. No noteworthy defects. No markings.; - Your satisfaction is our priority. We offer free returns and respond promptly to all inquiries. Your item will be packaged with care and ship on the same or next business day. Buy with confidence.…

Paperback. Zustand: New. Use TensorFlow 2.x with Google's Colaboratory (Colab) product that offers a free cloud service for Python programmers. Colab is especially well suited as a platform for TensorFlow 2.x deep learning applications. You will learn Colab's default install of the most current TensorFlow 2.x along with Colab's easy access to on-demand GPU hardware acceleration in the cloud for fast execution of deep learning models. This book offers you the opportunity to grasp deep learning in an applied manner with the only requirement being an Internet connection. Everything else-Python, TensorFlow 2.x, GPU support, and Jupyter Notebooks-is provided and ready to go from Colab.  The book begins with an introduction to TensorFlow 2.x and the Google Colab cloud service. You will learn how to provision a workspace on Google Colab and build a simple neural network application. From there you will progress into TensorFlow datasets and building input pipelines in support of modeling and testing. You will find coverage of deep learning classification and regression, with clear code examples showing how to perform each of those functions. Advanced topics covered in the book include convolutional neural networks and recurrent neural networks. This book contains all the applied math and programming you need to master the content. Examples range from simple to relatively complex when necessary to ensure acquisition of appropriate deep learning concepts and constructs. Examples are carefully explained, concise, accurate, and complete to perfectly complement deep learning skill development. Care is taken to walk you through the foundational principles of deep learning through clear examples written in Python that you can try out and experiment with using Google Colab from the comfort of your own home or office.What You Will LearnBe familiar with the basic concepts and constructs of applied deep learningCreate machine learning models with clean and reliable Python codeWork with datasets common to deep learning applicationsPrepare data for TensorFlow consumptionTake advantage of Google Colab's built-in support for deep learningExecute deep learning experiments using a variety of neural network modelsBe able to mount Google Colab directly to your Google Drive accountVisualize training versus test performance to see model fitWho This Book Is ForReaders who want to learn the highly popular TensorFlow 2.x deep learning platform, those who wish to master deep learning fundamentals that are sometimes skipped over in the rush to be productive, and those looking to build competency with a modern cloud service tool such as Google Colab.…

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Paperback. Zustand: New. Use TensorFlow 2.x with Google's Colaboratory (Colab) product that offers a free cloud service for Python programmers. Colab is especially well suited as a platform for TensorFlow 2.x deep learning applications. You will learn Colab's default install of the most current TensorFlow 2.x along with Colab's easy access to on-demand GPU hardware acceleration in the cloud for fast execution of deep learning models. This book offers you the opportunity to grasp deep learning in an applied manner with the only requirement being an Internet connection. Everything else-Python, TensorFlow 2.x, GPU support, and Jupyter Notebooks-is provided and ready to go from Colab.  The book begins with an introduction to TensorFlow 2.x and the Google Colab cloud service. You will learn how to provision a workspace on Google Colab and build a simple neural network application. From there you will progress into TensorFlow datasets and building input pipelines in support of modeling and testing. You will find coverage of deep learning classification and regression, with clear code examples showing how to perform each of those functions. Advanced topics covered in the book include convolutional neural networks and recurrent neural networks. This book contains all the applied math and programming you need to master the content. Examples range from simple to relatively complex when necessary to ensure acquisition of appropriate deep learning concepts and constructs. Examples are carefully explained, concise, accurate, and complete to perfectly complement deep learning skill development. Care is taken to walk you through the foundational principles of deep learning through clear examples written in Python that you can try out and experiment with using Google Colab from the comfort of your own home or office.What You Will LearnBe familiar with the basic concepts and constructs of applied deep learningCreate machine learning models with clean and reliable Python codeWork with datasets common to deep learning applicationsPrepare data for TensorFlow consumptionTake advantage of Google Colab's built-in support for deep learningExecute deep learning experiments using a variety of neural network modelsBe able to mount Google Colab directly to your Google Drive accountVisualize training versus test performance to see model fitWho This Book Is ForReaders who want to learn the highly popular TensorFlow 2.x deep learning platform, those who wish to master deep learning fundamentals that are sometimes skipped over in the rush to be productive, and those looking to build competency with a modern cloud service tool such as Google Colab.…

paperback. Zustand: New. Language:Chinese.Paperback.Pub Date:2022-06-01 Pages:320 Publisher:China Water Resources and Hydropower Press Deep Learning Practice: Computer Vision Development and Application Based on TensorFlow 2.X starts from the construction of the environment and the basic grammar of TensorFlow 2.X . and gradually go deep into the theory and practical application of algorithm models in the field of computer vision. and focus on how to use the TensorFlow 2.X framework to implement various classic deep l.…

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paperback. Zustand: New. Paperback. Pub Date: 524 Language: Chinese Publisher: Electronic Industry Press Based on Tensorflow version 2.1. The contents of the book are divided into 4. Article 1 includes the installation of Tensorflow. This part of the content can make the reader quickly up to TE .

paperback. Zustand: New. Language:Chinese.Paperback. Pub Date: 2022-09-01 Pages: 373 Publisher: Tsinghua University Press TensorFlow2.x Advanced Computer Vision elaborates on the basic solutions related to TensorFlow advanced computer vision. mainly including basic knowledge of computer vision and TensorFlow. Local Binary Patterns and Content Recognition. Face Detection Using OpenCV and CNN. Deep Learning for Images. Neural Network Architectures and Models. Transfer Learning and Visual Search. YOLO and Object Detecti.…

paperback. Zustand: New. Language:Chinese.Paperback. Pub Date: 2022-01-01 Pages: 236 Publisher: Mechanical Industry Press In 2016. an AlphaGo opened a narrow door for the world. and the road to the future was revealed. Through the accumulation of super powerful hardware and a large amount of data. the wave of artificial intelligence is rising for the third time. With the help of artificial intelligence technology. the public security department can easily catch criminals who have fled for many years. AI face changing. …

paperback. Zustand: New. Language:Chinese.Paperback. Pub Date: 2023-12 Publisher: Beijing Institute of Technology Press TensorFlow 2.x Deep Learning from Introduction to Practical Combat is a summary of the author's experience in researching and practicing artificial intelligence algorithms. This book introduces relevant knowledge of the TensorFlow 2.x framework through a combination of charts. cases and sample codes. helping readers lay a solid theoretical foundation for artificial intelligence and put the theory in.…