Learn Tensorflow In 24 Hours
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Learn Tensorflow In 24 Hours
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Author : Alex Nordeen
language : en
Publisher: Guru99
Release Date : 2020-10-31
Learn Tensorflow In 24 Hours written by Alex Nordeen and has been published by Guru99 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-31 with Computers categories.
Tensorflow is the most popular Deep Learning Library out there. It has fantastic graph computations feature which helps data scientist to visualize his designed neural network using TensorBoard. This Machine learning library supports both Convolution as well as Recurrent Neural network. It supports parallel processing on CPU as well as GPU. Prominent machine learning algorithms supported by TensorFlow are Deep Learning Classification, wipe & deep, Boston Tree amongst others. The book is very hands-on and gives you industry ready deep learnings practices. Here is what is covered in the book – Table Of Content Chapter 1: What is Deep learning? Chapter 2: Machine Learning vs Deep Learning Chapter 3: What is TensorFlow? Chapter 4: Comparison of Deep Learning Libraries Chapter 5: How to Download and Install TensorFlow Windows and Mac Chapter 6: Jupyter Notebook Tutorial Chapter 7: Tensorflow on AWS Chapter 8: TensorFlow Basics: Tensor, Shape, Type, Graph, Sessions & Operators Chapter 9: Tensorboard: Graph Visualization with Example Chapter 10: NumPy Chapter 11: Pandas Chapter 12: Scikit-Learn Chapter 13: Linear Regression Chapter 14: Linear Regression Case Study Chapter 15: Linear Classifier in TensorFlow Chapter 16: Kernel Methods Chapter 17: TensorFlow ANN (Artificial Neural Network) Chapter 18: ConvNet(Convolutional Neural Network): TensorFlow Image Classification Chapter 19: Autoencoder with TensorFlow Chapter 20: RNN(Recurrent Neural Network) TensorFlow
Machine Learning And Deep Learning Using Python And Tensorflow
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Author : Venkata Reddy Konasani
language : en
Publisher: McGraw Hill Professional
Release Date : 2021-04-29
Machine Learning And Deep Learning Using Python And Tensorflow written by Venkata Reddy Konasani and has been published by McGraw Hill Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-29 with Technology & Engineering categories.
Understand the principles and practices of machine learning and deep learning This hands-on guide lays out machine learning and deep learning techniques and technologies in a style that is approachable, using just the basic math required. Written by a pair of experts in the field, Machine Learning and Deep Learning Using Python and TensorFlow contains case studies in several industries, including banking, insurance, e-commerce, retail, and healthcare. The book shows how to utilize machine learning and deep learning functions in today’s smart devices and apps. You will get download links for datasets, code, and sample projects referred to in the text. Coverage includes: Machine learning and deep learning concepts Python programming and statistics fundamentals Regression and logistic regression Decision trees Model selection and cross-validation Cluster analysis Random forests and boosting Artificial neural networks TensorFlow and Keras Deep learning hyperparameters Convolutional neural networks Recurrent neural networks and long short-term memory
Masterclass Ai Learning Tensorflow Extreme Build Ai Models With Tensorflow In A Professional And Guided Way With Gpt Agent 2025 Edition
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Author : Diego Rodrigues
language : en
Publisher: StudioD21
Release Date : 2025-04-03
Masterclass Ai Learning Tensorflow Extreme Build Ai Models With Tensorflow In A Professional And Guided Way With Gpt Agent 2025 Edition written by Diego Rodrigues and has been published by StudioD21 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-03 with Computers categories.
MASTERCLASS AI LEARNING TENSORFLOW EXTREME – 2025 Edition Build AI Models with TensorFlow in a Professional and Guided Way with GPT Agent How about mastering TensorFlow directly in a hands-on environment, without boring videos or generic texts? The AILearning Masterclass series was created to revolutionize the way you learn essential technologies for the modern market. Here, you study directly on the OpenAI platform with a high-tech GPT Agent — Mr. TensorFlow D21 — who guides your technical journey, answers your questions in real-time, and supports the development of real-world projects using TensorFlow. All through text or voice, 24 hours a day, in 13 languages. No more passive learning. Now it’s active, guided, and applicable learning. You absorb, apply, fix, and evolve. At your own pace. Your own way. In your own language. In this Masterclass, you will learn to: • Build Machine Learning models with TensorFlow — from fundamentals to advanced applications • Work with data, tensors, neural networks, and training pipelines • Apply modern techniques for preprocessing, optimization, and model evaluation • Fix errors with technical support from the AI Agent and apply real AI engineering • Develop real projects with TensorFlow, ready for deployment and scalability With continuous tutoring, automated diagnostics, and applicable projects. AILearning EXPERIENCE – FREE DEMO To sharpen your Machine Learning skills and get you ready to tackle TensorFlow challenges, StudioD21 has prepared a demonstrative experience with AI-assisted tutoring. Explore a complete Masterclass for free and see in practice how our active learning model with artificial intelligence really works. TensorFlow, Machine Learning, Artificial Intelligence, Deep Learning, Neural Networks, TensorFlow Course, Practical Guide, Masterclass with AI, StudioD21, AI Engineering, Applied Machine Learning, Technical Training, Python for AI, ML Frameworks, TensorFlow Projects, GPT Agent Course, Interactive Learning, AI Development, Model Deployment, AI Tutoring, Real Projects with TensorFlow
Anais Do Workshop De Micro Ondas
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Author : Alexandre Maniçoba De Oliveira
language : en
Publisher: Clube de Autores
Release Date : 2025-05-18
Anais Do Workshop De Micro Ondas written by Alexandre Maniçoba De Oliveira and has been published by Clube de Autores this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-18 with Biography & Autobiography categories.
Este livro é a compilação de todos os artigos que foram apresentados no LBX/WMO’24 – XVI Workshop de Micro-ondas do Laboratório Maxwell em outubro de 2024, estando disponíveis para consulta digital pelo endereço https://anais.wmo.labmax.org.
Learn Tensorflow 2 0
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Author : Pramod Singh
language : en
Publisher: Apress
Release Date : 2019-12-17
Learn Tensorflow 2 0 written by Pramod Singh and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-17 with Computers categories.
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0. It also demonstrates how to build models using customer estimators. Further, it explains how to use TensorFlow 2.0 API to build machine learning and deep learning models for image classification using the standard as well as custom parameters. 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. What You'll Learn Review the new features of TensorFlow 2.0 Use TensorFlow 2.0 to build machine learning and deep learning models Perform sequence predictions using TensorFlow 2.0 Deploy TensorFlow 2.0 models with practical examples Who This Book Is For Data scientists, machine and deep learning engineers.
Deep Learning With Jax
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Author : Grigory Sapunov
language : en
Publisher: Simon and Schuster
Release Date : 2024-12-03
Deep Learning With Jax written by Grigory Sapunov and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-03 with Computers categories.
Accelerate deep learning and other number-intensive tasks with JAX, Google’s awesome high-performance numerical computing library. The JAX numerical computing library tackles the core performance challenges at the heart of deep learning and other scientific computing tasks. By combining Google’s Accelerated Linear Algebra platform (XLA) with a hyper-optimized version of NumPy and a variety of other high-performance features, JAX delivers a huge performance boost in low-level computations and transformations. In Deep Learning with JAX you will learn how to: • Use JAX for numerical calculations • Build differentiable models with JAX primitives • Run distributed and parallelized computations with JAX • Use high-level neural network libraries such as Flax • Leverage libraries and modules from the JAX ecosystem Deep Learning with JAX is a hands-on guide to using JAX for deep learning and other mathematically-intensive applications. Google Developer Expert Grigory Sapunov steadily builds your understanding of JAX’s concepts. The engaging examples introduce the fundamental concepts on which JAX relies and then show you how to apply them to real-world tasks. You’ll learn how to use JAX’s ecosystem of high-level libraries and modules, and also how to combine TensorFlow and PyTorch with JAX for data loading and deployment. About the technology Google’s JAX offers a fresh vision for deep learning. This powerful library gives you fine control over low level processes like gradient calculations, delivering fast and efficient model training and inference, especially on large datasets. JAX has transformed how research scientists approach deep learning. Now boasting a robust ecosystem of tools and libraries, JAX makes evolutionary computations, federated learning, and other performance-sensitive tasks approachable for all types of applications. About the book Deep Learning with JAX teaches you to build effective neural networks with JAX. In this example-rich book, you’ll discover how JAX’s unique features help you tackle important deep learning performance challenges, like distributing computations across a cluster of TPUs. You’ll put the library into action as you create an image classification tool, an image filter application, and other realistic projects. The nicely-annotated code listings demonstrate how JAX’s functional programming mindset improves composability and parallelization. What's inside • Use JAX for numerical calculations • Build differentiable models with JAX primitives • Run distributed and parallelized computations with JAX • Use high-level neural network libraries such as Flax About the reader For intermediate Python programmers who are familiar with deep learning. About the author Grigory Sapunov holds a Ph.D. in artificial intelligence and is a Google Developer Expert in Machine Learning. The technical editor on this book was Nicholas McGreivy. Table of Contents Part 1 1 When and why to use JAX 2 Your first program in JAX Part 2 3 Working with arrays 4 Calculating gradients 5 Compiling your code 6 Vectorizing your code 7 Parallelizing your computations 8 Using tensor sharding 9 Random numbers in JAX 10 Working with pytrees Part 3 11 Higher-level neural network libraries 12 Other members of the JAX ecosystem A Installing JAX B Using Google Colab C Using Google Cloud TPUs D Experimental parallelization
Learn Tensorflow
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Author : Diego Rodrigues
language : en
Publisher: StudioD21
Release Date : 2024-12-12
Learn Tensorflow written by Diego Rodrigues and has been published by StudioD21 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-12 with Business & Economics categories.
LEARN TENSORFLOW Master AI Model Development with Scalability and Precision. From Fundamentals to Practical Applications. This comprehensive guide is aimed at developers and students who want to create robust, high-performance, and scalable solutions with TensorFlow. You will learn to apply deep learning efficiently, master data pipelines, build advanced models, and deploy them professionally into production. Includes: • Tensor manipulation and model structuring with Keras • Building and training CNNs, RNNs, Transformers, and GANs • Regularization techniques, hyperparameter tuning, and performance optimization • Practical implementation with tf.data, TensorBoard, and TensorFlow Lite • Deployment with TensorFlow Serving, IoT integration, and use of GPUs and TPUs • Real-world cases in NLP, computer vision, healthcare, and enterprise systems By the end, you'll be fully equipped to develop TensorFlow applications for critical scenarios and scalable environments with technical excellence. tensorflow, keras, deep learning, cnn, rnn, gpu, deployment, iot, scalable models
Tensorflow Machine Learning
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Author : Benjamin Smith
language : en
Publisher:
Release Date : 2020-04-26
Tensorflow Machine Learning written by Benjamin Smith and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-26 with categories.
Are you interested in learning machine learning and deep learning? TensorFlow is the single most popular library available today. Offering some of the very best graph computations, TensorFlow helps data scientists in designing neural networks using a cool feature called TensorBoard. It has support for both recurrent neural networks (RNNs) and convolution, as well as parallel processing support on GPU and CPU. While TensorFlow is an incredibly important machine and deep learning library, we also give you an introduction to three others - NumPy, Pandas, and Scikit Learn. I have produced a hands-on guide, with plenty of code examples for you to follow along withHere's what you will learn: -What deep learning is-The difference between deep learning and machine learning-What TensorFlow is-How to install it on Windows and Mac-The basics of TensorFlow-Using TensorBoard-About NumPy, Scikit Learn, and Pandas-About linear regression-Kernel methods-Building an Artificial Neural Network using TensorFlow-TensorFlow image classification-TensorFlow autoencoders-Much moreIf you are already proficient at programming in Python and are ready to take the next step into machine learning, this guide is for you. Scroll up, hit that Buy Now button, and set off on a brand new machine learning journey.
Hands On Deep Learning With Tensorflow
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Author : Salil Vishnu Kapur
language : en
Publisher:
Release Date : 2018
Hands On Deep Learning With Tensorflow written by Salil Vishnu Kapur and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
"Are you short on time to start from scratch to use deep learning to solve complex problems involving topics like neural networks and reinforcement learning? If yes, then this is the course to help you. This course is designed to help you to overcome various data science problems by using efficient deep learning models built in TensorFlow.The course begins with a quick introduction to TensorFlow essentials. Next, we start with deep neural networks for different problems and then explore the applications of Convolutional Neural Networks on two real datasets. If you're facing time series problem then we will show you how to tackle it using RNN. We will also highlight how autoencoders can be used for efficient data representation. Lastly, we will take you through some of the important techniques to implement generative adversarial networks. All these modules are developed with step by step TensorFlow implementation with the help of real examples.By the end of the course you will be able to develop deep learning based solutions to any kind of problem you have, without any need to learn deep learning models from scratch, rather using TensorFlow and it's enormous power."--Resource description page.
Tensorflow Reinforcement Learning Quick Start Guide
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Author : Kaushik Balakrishnan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-03-30
Tensorflow Reinforcement Learning Quick Start Guide written by Kaushik Balakrishnan and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-03-30 with Computers categories.
Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasks Key FeaturesExplore efficient Reinforcement Learning algorithms and code them using TensorFlow and PythonTrain Reinforcement Learning agents for problems, ranging from computer games to autonomous driving.Formulate and devise selective algorithms and techniques in your applications in no time.Book Description Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator. By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems. What you will learnUnderstand the theory and concepts behind modern Reinforcement Learning algorithmsCode state-of-the-art Reinforcement Learning algorithms with discrete or continuous actionsDevelop Reinforcement Learning algorithms and apply them to training agents to play computer gamesExplore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlowUse A3C to play CartPole and LunarLanderTrain an agent to drive a car autonomously in a simulatorWho this book is for Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. Prior knowledge of machine learning and deep learning concepts (as well as exposure to Python programming) will be useful.