Beginner S Guide To Tensorflow
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Beginners Guide For Tensorflow
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Author : Katie Williams Ph D
language : en
Publisher:
Release Date : 2021-03-29
Beginners Guide For Tensorflow written by Katie Williams Ph D and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-29 with categories.
Machine learning has become ubiquitous in modern society, and its applications include language translation, robotics, handwriting analysis, financial prediction, and image recognition. TensorFlow is Google's preeminent toolset for machine learning, and this hands-on guide makes it easy to understand, even for those without a background in artificial intelligence.Install TensorFlow on your computerLearn the fundamentals of statistical regression and neural networksVisualize the machine learning process with TensorBoardPerform image recognition with convolutional neural networks (CNNs)Analyze sequential data with recurrent neural networks (RNNs)Execute TensorFlow on mobile devices and the Google Cloud Platform (GCP)If you're a manager or software developer looking to use TensorFlow for machine learning, this is the book you'll want to have close by.
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.
Tensorflow Beginners Guide
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Author : Dr Helen Jayden
language : en
Publisher: Independently Published
Release Date : 2021-06-05
Tensorflow Beginners Guide written by Dr Helen Jayden and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-05 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 with. Here'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 more If you are already proficient at programming 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.
Beginner S Guide To Tensorflow
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Author : RASHMI. SHAH
language : en
Publisher: Independently Published
Release Date : 2025-02-09
Beginner S Guide To Tensorflow written by RASHMI. SHAH and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-09 with Computers categories.
This book provides a fast-track guide to mastering TensorFlow for Artificial Intelligence (AI) and Deep Learning. As AI and Deep Learning transform industries, TensorFlow, developed by Google, has emerged as the leading open-source framework for building AI-powered applications efficiently. Designed for beginners, this guide takes you from foundational knowledge to TensorFlow proficiency. You'll learn to understand neural networks, implement deep learning models, and optimize AI applications through practical, hands-on projects. Whether you're new to the field, a data scientist, software developer, or an AI enthusiast, this book equips you with the skills to build your own AI models from scratch. Core areas covered in this book: Introduction to TensorFlow: Understand the framework, installation, and core components. Deep Learning Foundations: Explore neural networks, activation functions, and optimization techniques. Building Your First AI Model: Follow a step-by-step guide to create and train a deep learning model. Data Processing & Feature Engineering: Handle large datasets, preprocessing, and augmentation techniques. Convolutional Neural Networks (CNNs): Implement CNNs for image classification and object detection tasks. Recurrent Neural Networks (RNNs) & LSTMs: Utilize deep learning for time series forecasting and Natural Language Processing (NLP). TensorFlow for Computer Vision & Natural Language Processing (NLP): Apply AI to solve real-world problems in these domains. Model Evaluation & Hyperparameter Tuning: Improve model performance with regularization and tuning strategies. TensorFlow on GPUs & TPUs: Accelerate AI model training using hardware acceleration. Deploying AI Models: Learn to deploy deep learning models in real-world applications and cloud environments. This book provides a practical learning experience: Beginner-Friendly Approach: Master deep learning with step-by-step explanations. Hands-On & Practical: Real-world examples, coding exercises, and TensorFlow implementations are included. Covers Both Theory & Application: Learn AI model building, optimization, and deployment techniques. Industry-Relevant: Learn best practices used by professionals in AI, data science, and machine learning. Updated for the Latest TensorFlow Version: Covers modern AI techniques and deep learning advancements. This book is ideal for: Beginners in AI & Deep Learning Data Scientists & Machine Learning Enthusiasts Software Engineers & Developers Students & Researchers Tech Entrepreneurs & AI Innovators Start building AI models today with this comprehensive TensorFlow guide. Get hands-on experience, learn practical AI techniques, and build real-world AI applications! For additional resources and tutorials, visit QuickTechie.com to further enhance your understanding of TensorFlow.
Python Machine Learning
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Author : Railey Brandon
language : en
Publisher: Roland Bind
Release Date : 2019-04-25
Python Machine Learning written by Railey Brandon and has been published by Roland Bind this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-25 with Computers categories.
★☆Have you come across the terms machine learning and neural networks in most articles you have recently read? Do you also want to learn how to build a machine learning model that will answer your questions within a blink of your eyes?☆★ If you responded yes to any of the above questions, you have come to the right place. Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that. Machine learning and artificial intelligence have been used in different machines and applications to improve the user's experience. One can also use machine learning to make data analysis and predicting the output for some data sets easy. All you need to do is choose the right algorithm, train the model and test the model before you apply it on any real-world tool. It is that simple isn't it? ★★Apart from this, you will also learn more about★★ ♦ The Different Types Of Learning Algorithm That You Can Expect To Encounter ♦ The Numerous Applications Of Machine Learning And Deep Learning ♦ The Best Practices For Picking Up Neural Networks ♦ What Are The Best Languages And Libraries To Work With ♦ The Various Problems That You Can Solve With Machine Learning Algorithms ♦ And much more... Well, you can do it faster if you use Python. This language has made it easy for any user, even an amateur, to build a strong machine learning model since it has numerous directories and libraries that make it easy for one to build a model. Do you want to know how to build a machine learning model and a neural network? So, what are you waiting for? Grab a copy of this book now!
Tensorflow Guide For Beginners
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Author : EDWARD PH.D. DAVID
language : en
Publisher:
Release Date : 2020
Tensorflow Guide For Beginners written by EDWARD PH.D. DAVID and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Python Machine Learning
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Author : Anderson Coen
language : en
Publisher:
Release Date : 2020-05-25
Python Machine Learning written by Anderson Coen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-25 with categories.
Python Machine Learning Would you want to learn how to utilize Python to produce machine learning models, but you think it would be too complicated for you? Or maybe you like to automate simple stuff with your PC, but you do not know how to do it. As a novice, you might think programming is complicated. Understanding artificial intelligence coding could take several months. Not to mention that the chance of giving up before perfecting it could be high. Therefore, you could think of employing a professional developer to shorten the time if you have time to develop. That might look like a great solution, but it is surely very costly. You still have pay for the developer if he doesn't do the proper job you want. You know the best solution for this? The perfect solution is to follow a complete programming manual with hands-on projects as well as practical exercises. This book is structured as a course with six chapters. Inside the book, you will be able to go through a first section in which basic and fundamental notions of deep learning are mention, to get to the next chapters made to help you learn advanced coding insights needed to build training data sets for the development of successful machine learning models. In detail, you will learn: The Fundamentals of Machine Learning Machine-Learning Systems An Overview of Python for Machine Learning Understanding Python Libraries for Machine Learning Introducing Neural Networks and Deep Learning Practical Data Management What makes this book different? The majority of books available on the market take a brief look into machine learning, presenting some of the subjects but never going deep. This book is not one of those. Even if you are totally new to programming in 2020 or you're simply looking to widen your abilities as a programmer, this book is perfect for you! Well, stress no more! Buy this book and also learn all... and DOWNLOAD IT NOW!
Deep Learning For Beginners With Tensorflow
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Author : Mark Smart
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2018-09-13
Deep Learning For Beginners With Tensorflow written by Mark Smart and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-13 with categories.
This book is an exploration of deep learning in Python using TensorFlow. The author guides you on how to create machine learning models using TensorFlow. You will know the initial steps of getting started with TensorFlow in Python. This involves installing TensorFlow and writing your first code. TensorFlow works using the concept of graphs. The author helps you know how expressions are represented into graphs in TensorFlow. Deep learning in Python with TensorFlow simply involves the creation of neural network models. The author helps you understand how to create neural network models with TensorFlow. You are guided on how to train such models with data of various types. Examples of such data include images and text. The process of loading your own data into TensorFlow for training neural network models has also been discussed. You will also know how to use the inbuilt data for training your neural network models. You will learn from this book: Getting started Building a Neural Network Working with Images Importing Data Subjects include: tensorflow python, deep learning with python, tensorflow machine learning, tensor flow, tensorflow deep learning cookbook, tensorflow for deep learning, tensorflow for dummies, tensorflow books, machine learning with tensorflow, tensorflow c++, concept of graphs, neural network, neural networks python, tensorflow with neural network.
Mastering Machine Learning With Tensorflow
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Author : Prime Evolution
language : en
Publisher: Independently Published
Release Date : 2025-06-23
Mastering Machine Learning With Tensorflow written by Prime Evolution and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-23 with Computers categories.
Chapters: Chapter 1: Introduction to Machine Learning Overview of Machine Learning Types of Machine Learning: Supervised, Unsupervised, and Reinforcement Learning Applications and Real-World Examples Chapter 2: Getting Started with TensorFlow Introduction to TensorFlow Setting Up the Environment Basic TensorFlow Concepts and Terminology Chapter 3: Understanding Tensors and Operations What are Tensors? Tensor Operations and Basic Algebra TensorFlow Operations and Functions Chapter 4: Building Your First Neural Network Introduction to Neural Networks Creating a Simple Neural Network in TensorFlow Training and Evaluating Your Model Chapter 5: Data Preparation and Preprocessing Importance of Data Preparation Loading and Handling Data with TensorFlow Data Normalization and Augmentation Techniques Chapter 6: Exploring TensorFlow's High-Level APIs Introduction to Keras API Building Models with Keras Customizing and Compiling Models Chapter 7: Deep Learning Architectures Introduction to Deep Learning Understanding Convolutional Neural Networks (CNNs) Implementing CNNs in TensorFlow Chapter 8: Advanced Neural Networks Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks Sequence-to-Sequence Models Implementing RNNs and LSTMs in TensorFlow Chapter 9: Model Evaluation and Tuning Metrics and Evaluation Techniques Hyperparameter Tuning Cross-Validation and Model Selection Chapter 10: Handling Overfitting and Underfitting Understanding Overfitting and Underfitting Regularization Techniques Strategies for Improving Model Generalization Chapter 11: Transfer Learning and Fine-Tuning What is Transfer Learning? Using Pre-trained Models Fine-Tuning for Specific Tasks Chapter 12: Working with Large Datasets Efficient Data Loading and Processing TensorFlow Data Pipeline Distributed Training and Scaling Models Chapter 13: Deploying TensorFlow Models Exporting and Saving Models TensorFlow Serving for Deployment Using TensorFlow Lite for Mobile and Edge Devices Chapter 14: Integrating TensorFlow with Other Tools TensorFlow and TensorBoard for Visualization TensorFlow and Cloud Services Integration with Other Python Libraries Chapter 15: Future Trends and Next Steps Emerging Trends in Machine Learning Exploring New TensorFlow Features Continuing Your Machine Learning Journey
Reinforcement Learning With Tensorflow
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Author : Sayon Dutta
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-04-24
Reinforcement Learning With Tensorflow written by Sayon Dutta 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 2018-04-24 with Computers categories.
Leverage the power of the Reinforcement Learning techniques to develop self-learning systems using Tensorflow Key Features Learn reinforcement learning concepts and their implementation using TensorFlow Discover different problem-solving methods for Reinforcement Learning Apply reinforcement learning for autonomous driving cars, robobrokers, and more Book Description Reinforcement Learning (RL), allows you to develop smart, quick and self-learning systems in your business surroundings. It is an effective method to train your learning agents and solve a variety of problems in Artificial Intelligence—from games, self-driving cars and robots to enterprise applications that range from datacenter energy saving (cooling data centers) to smart warehousing solutions. The book covers the major advancements and successes achieved in deep reinforcement learning by synergizing deep neural network architectures with reinforcement learning. The book also introduces readers to the concept of Reinforcement Learning, its advantages and why it’s gaining so much popularity. The book also discusses on MDPs, Monte Carlo tree searches, dynamic programming such as policy and value iteration, temporal difference learning such as Q-learning and SARSA. You will use TensorFlow and OpenAI Gym to build simple neural network models that learn from their own actions. You will also see how reinforcement learning algorithms play a role in games, image processing and NLP. By the end of this book, you will have a firm understanding of what reinforcement learning is and how to put your knowledge to practical use by leveraging the power of TensorFlow and OpenAI Gym. What you will learn Implement state-of-the-art Reinforcement Learning algorithms from the basics Discover various techniques of Reinforcement Learning such as MDP, Q Learning and more Learn the applications of Reinforcement Learning in advertisement, image processing, and NLP Teach a Reinforcement Learning model to play a game using TensorFlow and the OpenAI gym Understand how Reinforcement Learning Applications are used in robotics Who this book is for If you want to get started with reinforcement learning using TensorFlow in the most practical way, this book will be a useful resource. The book assumes prior knowledge of machine learning and neural network programming concepts, as well as some understanding of the TensorFlow framework. No previous experience with Reinforcement Learning is required.