How will I save time investigating strategic and tactical options and ensuring TensorFlow costs are low? How can I deliver tailored TensorFlow advice instantly with structured going-forward plans?
There's no better guide through these mind-expanding questions than acclaimed best-selling author Gerard Blokdyk. Blokdyk ensures all TensorFlow essentials are covered, from every angle: the TensorFlow self-assessment shows succinctly and clearly that what needs to be clarified to organize the required activities and processes so that TensorFlow outcomes are achieved.
Contains extensive criteria grounded in past and current successful projects and activities by experienced TensorFlow practitioners. Python Machine Learning Projects. As machine learning is increasingly leveraged to find patterns, conduct analysis, and make decisions - sometimes without final input from humans who may be impacted by these findings - it is crucial to invest in bringing more stakeholders into the fold.
This book of Python projects in machine learning tries to do just that: to equip the developers Libelf by Example. This tutorial introduces libelf, a library for reading and writing object code in the Extensible Linking Format ELF file format. This open book is for the beginning level of both architecture and interior design students who learn computer graphic communication software.
Revised for TensorFlow 2. Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques. Starting with an overview of multi-layer perceptrons MLPs , convolutional neural networks CNNs , and recurrent neural networks RNNs , the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders.
What you will learn Use mutual information maximization techniques to perform unsupervised learning Use segmentation to identify the pixel-wise class of each object in an image Identify both the bounding box and class of objects in an image using object detec. The book begins with introducing TensorFlow 2. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.
You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting ways.
If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects in images, understanding text, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples and helps you build knowledge of deep learning foundations from the ground up.
Learn TensorFlow fundamentals, including how to perform basic computation Build simple learning systems to understand their mathematical foundations Dive into fully connected deep networks used in thousands of applications Turn prototypes into high-quality models with hyperparameter optimization Process images with convolutional neural networks Handle natural language datasets with recurrent neural networks Use reinforcement learning to solve games such as tic-tac-toe Train deep networks with hardware including GPUs and tensor processing units.
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. This book shows how this can be exploited in the real wo.
Score: 5. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects.
Learn how AI impacts the healthcare ecosystem through real-life case studies with TensorFlow 2. This book begins by explaining the dynamics of the healthcare market, including the role of stakeholders such as healthcare professionals, patients, and payers. Then it moves into the case studies.
The case studies start with EHR data and how you can account for sub-populations using a multi-task setup when you are working on any downstream task. You also will try to predict ICD-9 codes using the same data. You will study transformer models. And you will be exposed to the challenges of applying modern ML techniques to highly sensitive data in healthcare using federated learning. You will look at semi-supervised approaches that are used in a low training data setting, a case very often observed in specialized domains such as healthcare.
You will be introduced to applications of advanced topics such as the graph convolutional network and how you can develop and optimize image analysis pipelines when using 2D and 3D medical images. And, lastly, after discussing how web and server technologies have come to make scaling and deploying easy, an ML app is deployed for the world to see with Docker using Flask.
By the end of this book, you will have a clear understanding of how the healthcare system works and how to apply ML and deep learning tools and techniques to the healthcare industry. What You Will Learn Get complete, clear, and comprehensive coverage of algorithms and techniques related to case studies Look at different problem areas within the healthcare industry and solve them in a code-first approach Explore and understand advanced topics such as multi-task learning, transformers, and graph convolutional networks Understand the industry and learn ML Who This Book Is For Data scientists and software developers interested in machine learning and its application in the healthcare industry.
This book covers the research area from multiple viewpoints including bibliometric analysis, reviews, empirical analysis, platforms, and future applications.
The centralized training of deep learning and machine learning models not only incurs a high communication cost of data transfer into the cloud systems but also raises the privacy protection concerns of data providers.
This book aims at targeting researchers and practitioners to delve deep into core issues in federated learning research to transform next-generation artificial intelligence applications.
Federated learning enables the distribution of the learning models across the devices and systems which perform initial training and report the updated model attributes to the centralized cloud servers for secure and privacy-preserving attribute aggregation and global model development.
Federated learning benefits in terms of privacy, communication efficiency, data security, and contributors' control of their critical data. Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key Features Introduces and then uses TensorFlow 2 and Keras right from the start Teaches key machine and deep learning techniques Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples Book Description Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow TF and Keras.
TensorFlow is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. What you will learn Build machine learning and deep learning systems with TensorFlow 2 and the Keras API Use Regression analysis, the most popular approach to machine learning Understand ConvNets convolutional neural networks and how they are essential for deep learning systems such as image classifiers Use GANs generative adversarial networks to create new data that fits with existing patterns Discover RNNs recurrent neural networks that can process sequences of input intelligently, using one part of a sequence to correctly interpret another Apply deep learning to natural human language and interpret natural language texts to produce an appropriate response Train your models on the cloud and put TF to work in real environments Explore how Google tools can automate simple ML workflows without the need for complex modeling Who this book is for This book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow.
Whether or not you have done machine learning before, this book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment.
Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Get hands-on with the browser-based JavaScript library for training and deploying machine learning models effectively Key Features Build, train and run machine learning models in the browser using TensorFlow.
With this book, you will learn how to use TensorFlow. Starting with the basics, you'll understand how ML models can be built on the web. Moving on, you will get to grips with the TensorFlow. The book will then guide you through implementing ML techniques and algorithms such as regression, clustering, fast Fourier transform FFT , and dimensionality reduction.
You will later cover the Bellman equation to solve Markov decision process MDP problems and understand how it is related to reinforcement learning. Finally, you will explore techniques for deploying ML-based web applications and training models with TensorFlow Core. Throughout this ML book, you'll discover useful tips and tricks that will build on your knowledge. By the end of this book, you will be equipped with the skills you need to create your own web-based ML applications and fine-tune models to achieve high performance.
This book will also appeal to data scientists, machine learning practitioners, and deep learning enthusiasts who are looking to perform accelerated, browser-based machine learning on Web using TensorFlow. Working knowledge of JavaScript programming language is all you need to get started.
This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.
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