Tensorflow Powerful Predictive Analytics With Tensorflow

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TensorFlow: Powerful Predictive Analytics with TensorFlow

Author : Md. Rezaul Karim
Publisher : Packt Publishing Ltd
ISBN 13 : 1789130425
Total Pages : 165 pages
Book Rating : 4.7/5 (891 download)

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Book Synopsis TensorFlow: Powerful Predictive Analytics with TensorFlow by : Md. Rezaul Karim

Download or read book TensorFlow: Powerful Predictive Analytics with TensorFlow written by Md. Rezaul Karim and published by Packt Publishing Ltd. This book was released on 2018-03-14 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to solve real life problems using different methods like logic regression, random forests and SVM’s with TensorFlow. Key Features Understand predictive analytics along with its challenges and best practices Embedded with assessments that will help you revise the concepts you have learned in this book Book Description Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google’s brainchild, is immensely popular and extensively used for predictive analysis. This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features. This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. What you will learn Learn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configuration Explore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analytics Solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics Dig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations Learn several examples of how to apply reinforcement learning algorithms for developing predictive models on real-life datasets Who this book is for This book is aimed at developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow.

TensorFlow: Powerful Predictive Analytics with TensorFlow

Author : Md. Rezaul Karim
Publisher :
ISBN 13 : 9781789136913
Total Pages : 164 pages
Book Rating : 4.1/5 (369 download)

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Book Synopsis TensorFlow: Powerful Predictive Analytics with TensorFlow by : Md. Rezaul Karim

Download or read book TensorFlow: Powerful Predictive Analytics with TensorFlow written by Md. Rezaul Karim and published by . This book was released on 2018-03-12 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to solve real life problems using different methods like logic regression, random forests and SVM's with TensorFlow. Key Features Understand predictive analytics along with its challenges and best practices Embedded with assessments that will help you revise the concepts you have learned in this book Book Description Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google's brainchild, is immensely popular and extensively used for predictive analysis. This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features. This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. What you will learn Learn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configuration Explore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analytics Solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics Dig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations Learn several examples of how to apply reinforcement learning algorithms for developing predictive models on real-life datasets Who this book is for This book is aimed at developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow.

Mastering Predictive Analytics with scikit-learn and TensorFlow

Author : Alvaro Fuentes
Publisher : Packt Publishing Ltd
ISBN 13 : 1789612241
Total Pages : 149 pages
Book Rating : 4.7/5 (896 download)

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Book Synopsis Mastering Predictive Analytics with scikit-learn and TensorFlow by : Alvaro Fuentes

Download or read book Mastering Predictive Analytics with scikit-learn and TensorFlow written by Alvaro Fuentes and published by Packt Publishing Ltd. This book was released on 2018-09-29 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn advanced techniques to improve the performance and quality of your predictive models Key FeaturesUse ensemble methods to improve the performance of predictive analytics modelsImplement feature selection, dimensionality reduction, and cross-validation techniquesDevelop neural network models and master the basics of deep learningBook Description Python is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems. This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics. By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis. What you will learnUse ensemble algorithms to obtain accurate predictionsApply dimensionality reduction techniques to combine features and build better modelsChoose the optimal hyperparameters using cross-validationImplement different techniques to solve current challenges in the predictive analytics domainUnderstand various elements of deep neural network (DNN) modelsImplement neural networks to solve both classification and regression problemsWho this book is for Mastering Predictive Analytics with scikit-learn and TensorFlow is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to progress from basic predictive models to building advanced models and producing more accurate predictions. Prior knowledge of Python and familiarity with predictive analytics concepts are assumed.

Predictive Analytics with TensorFlow

Author : Md. Rezaul Karim
Publisher :
ISBN 13 : 9781788398923
Total Pages : 522 pages
Book Rating : 4.3/5 (989 download)

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Book Synopsis Predictive Analytics with TensorFlow by : Md. Rezaul Karim

Download or read book Predictive Analytics with TensorFlow written by Md. Rezaul Karim and published by . This book was released on 2017-10-26 with total page 522 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accomplish the power of data in your business by building advanced predictive modelling applications with Tensorflow.About This Book* A quick guide to gain hands-on experience with deep learning in different domains such as digit/image classification, and texts* Build your own smart, predictive models with TensorFlow using easy-to-follow approach mentioned in the book* Understand deep learning and predictive analytics along with its challenges and best practicesWho This Book Is ForThis book is intended for anyone who wants to build predictive models with the power of TensorFlow from scratch. If you want to build your own extensive applications which work, and can predict smart decisions in the future then this book is what you need!What You Will Learn* Get a solid and theoretical understanding of linear algebra, statistics, and probability for predictive modeling* Develop predictive models using classification, regression, and clustering algorithms* Develop predictive models for NLP* Learn how to use reinforcement learning for predictive analytics* Factorization Machines for advanced recommendation systems* Get a hands-on understanding of deep learning architectures for advanced predictive analytics* Learn how to use deep Neural Networks for predictive analytics* See how to use recurrent Neural Networks for predictive analytics* Convolutional Neural Networks for emotion recognition, image classification, and sentiment analysisIn DetailPredictive analytics discovers hidden patterns from structured and unstructured data for automated decision-making in business intelligence.This book will help you build, tune, and deploy predictive models with TensorFlow in three main sections. The first section covers linear algebra, statistics, and probability theory for predictive modeling.The second section covers developing predictive models via supervised (classification and regression) and unsupervised (clustering) algorithms. It then explains how to develop predictive models for NLP and covers reinforcement learning algorithms. Lastly, this section covers developing a factorization machines-based recommendation system.The third section covers deep learning architectures for advanced predictive analytics, including deep neural networks and recurrent neural networks for high-dimensional and sequence data. Finally, convolutional neural networks are used for predictive modeling for emotion recognition, image classification, and sentiment analysis.Style and approachTensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation.

Hands-On Predictive Analytics with Python

Author : Alvaro Fuentes
Publisher : Packt Publishing Ltd
ISBN 13 : 1789134544
Total Pages : 320 pages
Book Rating : 4.7/5 (891 download)

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Book Synopsis Hands-On Predictive Analytics with Python by : Alvaro Fuentes

Download or read book Hands-On Predictive Analytics with Python written by Alvaro Fuentes and published by Packt Publishing Ltd. This book was released on 2018-12-28 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step guide to build high performing predictive applications Key FeaturesUse the Python data analytics ecosystem to implement end-to-end predictive analytics projectsExplore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanationsLearn to deploy a predictive model's results as an interactive applicationBook Description Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming. What you will learnGet to grips with the main concepts and principles of predictive analyticsLearn about the stages involved in producing complete predictive analytics solutionsUnderstand how to define a problem, propose a solution, and prepare a datasetUse visualizations to explore relationships and gain insights into the datasetLearn to build regression and classification models using scikit-learnUse Keras to build powerful neural network models that produce accurate predictionsLearn to serve a model's predictions as a web applicationWho this book is for This book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and would like to implement predictive analytics solutions using Python's data stack. People from other backgrounds who would like to enter this exciting field will greatly benefit from reading this book. All you need is to be proficient in Python programming and have a basic understanding of statistics and college-level algebra.

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.

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).

Machine Learning Using TensorFlow Cookbook

Author : Alexia Audevart
Publisher : Packt Publishing Ltd
ISBN 13 : 1800206887
Total Pages : 417 pages
Book Rating : 4.8/5 (2 download)

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Book Synopsis Machine Learning Using TensorFlow Cookbook by : Alexia Audevart

Download or read book Machine Learning Using TensorFlow Cookbook written by Alexia Audevart and published by Packt Publishing Ltd. This book was released on 2021-02-08 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive recipes to give you valuable insights on Transformers, Reinforcement Learning, and more Key FeaturesDeep Learning solutions from Kaggle Masters and Google Developer ExpertsGet to grips with the fundamentals including variables, matrices, and data sourcesLearn advanced techniques to make your algorithms faster and more accurateBook Description The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow. This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression. Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems. With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios. What you will learnTake TensorFlow into productionImplement and fine-tune Transformer models for various NLP tasksApply reinforcement learning algorithms using the TF-Agents frameworkUnderstand linear regression techniques and use Estimators to train linear modelsExecute neural networks and improve predictions on tabular dataMaster convolutional neural networks and recurrent neural networks through practical recipesWho this book is for If you are a data scientist or a machine learning engineer, and you want to skip detailed theoretical explanations in favor of building production-ready machine learning models using TensorFlow, this book is for you. Basic familiarity with Python, linear algebra, statistics, and machine learning is necessary to make the most out of this book.

Deep Learning with TensorFlow 2 and Keras

Author : Antonio Gulli
Publisher : Packt Publishing Ltd
ISBN 13 : 1838827722
Total Pages : 647 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Deep Learning with TensorFlow 2 and Keras by : Antonio Gulli

Download or read book Deep Learning with TensorFlow 2 and Keras written by Antonio Gulli and published by Packt Publishing Ltd. This book was released on 2019-12-27 with total page 647 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices Key FeaturesIntroduces and then uses TensorFlow 2 and Keras right from the startTeaches key machine and deep learning techniquesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesBook Description Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available. 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. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML. What you will learnBuild machine learning and deep learning systems with TensorFlow 2 and the Keras APIUse Regression analysis, the most popular approach to machine learningUnderstand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiersUse GANs (generative adversarial networks) to create new data that fits with existing patternsDiscover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret anotherApply deep learning to natural human language and interpret natural language texts to produce an appropriate responseTrain your models on the cloud and put TF to work in real environmentsExplore how Google tools can automate simple ML workflows without the need for complex modelingWho 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. This book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected.

Machine Learning with TensorFlow, Second Edition

Author : Mattmann A. Chris
Publisher : Manning Publications
ISBN 13 : 1617297712
Total Pages : 454 pages
Book Rating : 4.6/5 (172 download)

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Book Synopsis Machine Learning with TensorFlow, Second Edition by : Mattmann A. Chris

Download or read book Machine Learning with TensorFlow, Second Edition written by Mattmann A. Chris and published by Manning Publications. This book was released on 2021-02-02 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Summary Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need. About the book Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10. What's inside Machine Learning with TensorFlow Choosing the best ML approaches Visualizing algorithms with TensorBoard Sharing results with collaborators Running models in Docker About the reader Requires intermediate Python skills and knowledge of general algebraic concepts like vectors and matrices. Examples use the super-stable 1.15.x branch of TensorFlow and TensorFlow 2.x. About the author Chris Mattmann is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas. Table of Contents PART 1 - YOUR MACHINE-LEARNING RIG 1 A machine-learning odyssey 2 TensorFlow essentials PART 2 - CORE LEARNING ALGORITHMS 3 Linear regression and beyond 4 Using regression for call-center volume prediction 5 A gentle introduction to classification 6 Sentiment classification: Large movie-review dataset 7 Automatically clustering data 8 Inferring user activity from Android accelerometer data 9 Hidden Markov models 10 Part-of-speech tagging and word-sense disambiguation PART 3 - THE NEURAL NETWORK PARADIGM 11 A peek into autoencoders 12 Applying autoencoders: The CIFAR-10 image dataset 13 Reinforcement learning 14 Convolutional neural networks 15 Building a real-world CNN: VGG-Face ad VGG-Face Lite 16 Recurrent neural networks 17 LSTMs and automatic speech recognition 18 Sequence-to-sequence models for chatbots 19 Utility landscape

Deep Learning Pipeline

Author : Hisham El-Amir
Publisher : Apress
ISBN 13 : 1484253493
Total Pages : 563 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Deep Learning Pipeline by : Hisham El-Amir

Download or read book Deep Learning Pipeline written by Hisham El-Amir and published by Apress. This book was released on 2019-12-20 with total page 563 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build your own pipeline based on modern TensorFlow approaches rather than outdated engineering concepts. This book shows you how to build a deep learning pipeline for real-life TensorFlow projects. You'll learn what a pipeline is and how it works so you can build a full application easily and rapidly. Then troubleshoot and overcome basic Tensorflow obstacles to easily create functional apps and deploy well-trained models. Step-by-step and example-oriented instructions help you understand each step of the deep learning pipeline while you apply the most straightforward and effective tools to demonstrative problems and datasets. You'll also develop a deep learning project by preparing data, choosing the model that fits that data, and debugging your model to get the best fit to data all using Tensorflow techniques. Enhance your skills by accessing some of the most powerful recent trends in data science. If you've ever considered building your own image or text-tagging solution or entering a Kaggle contest, Deep Learning Pipeline is for you! What You'll LearnDevelop a deep learning project using dataStudy and apply various models to your dataDebug and troubleshoot the proper model suited for your data Who This Book Is For Developers, analysts, and data scientists looking to add to or enhance their existing skills by accessing some of the most powerful recent trends in data science. Prior experience in Python or other TensorFlow related languages and mathematics would be helpful.

Hands-on Computer Vision with TensorFlow 2

Author : Benjamin Planche
Publisher :
ISBN 13 : 9781788830645
Total Pages : 372 pages
Book Rating : 4.8/5 (36 download)

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Book Synopsis Hands-on Computer Vision with TensorFlow 2 by : Benjamin Planche

Download or read book Hands-on Computer Vision with TensorFlow 2 written by Benjamin Planche and published by . This book was released on 2019 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer vision is achieving a new frontier of capabilities in fields like health, automobile or robotics. This book explores TensorFlow 2, Google's open-source AI framework, and teaches how to leverage deep neural networks for visual tasks. It will help you acquire the insight and skills to be a part of the exciting advances in computer vision.

The TensorFlow Workshop

Author : Matthew Moocarme
Publisher : Packt Publishing Ltd
ISBN 13 : 1800200226
Total Pages : 601 pages
Book Rating : 4.8/5 (2 download)

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Book Synopsis The TensorFlow Workshop by : Matthew Moocarme

Download or read book The TensorFlow Workshop written by Matthew Moocarme and published by Packt Publishing Ltd. This book was released on 2021-12-15 with total page 601 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get started with TensorFlow fundamentals to build and train deep learning models with real-world data, practical exercises, and challenging activities Key FeaturesUnderstand the fundamentals of tensors, neural networks, and deep learningDiscover how to implement and fine-tune deep learning models for real-world datasetsBuild your experience and confidence with hands-on exercises and activitiesBook Description Getting to grips with tensors, deep learning, and neural networks can be intimidating and confusing for anyone, no matter their experience level. The breadth of information out there, often written at a very high level and aimed at advanced practitioners, can make getting started even more challenging. If this sounds familiar to you, The TensorFlow Workshop is here to help. Combining clear explanations, realistic examples, and plenty of hands-on practice, it'll quickly get you up and running. You'll start off with the basics – learning how to load data into TensorFlow, perform tensor operations, and utilize common optimizers and activation functions. As you progress, you'll experiment with different TensorFlow development tools, including TensorBoard, TensorFlow Hub, and Google Colab, before moving on to solve regression and classification problems with sequential models. Building on this solid foundation, you'll learn how to tune models and work with different types of neural network, getting hands-on with real-world deep learning applications such as text encoding, temperature forecasting, image augmentation, and audio processing. By the end of this deep learning book, you'll have the skills, knowledge, and confidence to tackle your own ambitious deep learning projects with TensorFlow. What you will learnGet to grips with TensorFlow's mathematical operationsPre-process a wide variety of tabular, sequential, and image dataUnderstand the purpose and usage of different deep learning layersPerform hyperparameter-tuning to prevent overfitting of training dataUse pre-trained models to speed up the development of learning modelsGenerate new data based on existing patterns using generative modelsWho this book is for This TensorFlow book is for anyone who wants to develop their understanding of deep learning and get started building neural networks with TensorFlow. Basic knowledge of Python programming and its libraries, as well as a general understanding of the fundamentals of data science and machine learning, will help you grasp the topics covered in this book more easily.

Pro Deep Learning with TensorFlow

Author : Santanu Pattanayak
Publisher : Apress
ISBN 13 : 1484230965
Total Pages : 412 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Pro Deep Learning with TensorFlow by : Santanu Pattanayak

Download or read book Pro Deep Learning with TensorFlow written by Santanu Pattanayak and published by Apress. This book was released on 2017-12-06 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with TensorFlow provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures. All of the practical aspects of deep learning that are relevant in any industry are emphasized in this book. You will be able to use the prototypes demonstrated to build new deep learning applications. The code presented in the book is available in the form of iPython notebooks and scripts which allow you to try out examples and extend them in interesting ways. You will be equipped with the mathematical foundation and scientific knowledge to pursue research in this field and give back to the community. What You'll Learn Understand full stack deep learning using TensorFlow and gain a solid mathematical foundation for deep learning Deploy complex deep learning solutions in production using TensorFlow Carry out research on deep learning and perform experiments using TensorFlow Who This Book Is For Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts

TensorFlow for Machine Intelligence

Author : Sam Abrahams
Publisher :
ISBN 13 : 9781939902450
Total Pages : pages
Book Rating : 4.9/5 (24 download)

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Book Synopsis TensorFlow for Machine Intelligence by : Sam Abrahams

Download or read book TensorFlow for Machine Intelligence written by Sam Abrahams and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

TensorFlow for Deep Learning

Author : Bharath Ramsundar
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491980400
Total Pages : 247 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis TensorFlow for Deep Learning by : Bharath Ramsundar

Download or read book TensorFlow for Deep Learning written by Bharath Ramsundar and published by "O'Reilly Media, Inc.". This book was released on 2018-03-01 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. 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. It’s ideal for practicing developers with experience designing software systems, and useful for scientists and other professionals familiar with scripting but not necessarily with designing learning algorithms. 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