Best TensorFlow Courses 2020

Best TensorFlow Books 2020

Best TensorFlow tutorials 2020

Complete Guide to TensorFlow for Deep Learning with Python by Jose Portilla. This TensorFlow course is for Python developers who want to learn the latest Deep Learning techniques with TensorFlow.

You will learn:

Neural Network Basics

TensorFlow Basics

Artificial Neural Networks

Densely Connected Networks

Convolutional Neural Networks

Recurrent Neural Networks

AutoEncoders

Reinforcement Learning

OpenAI Gym

You will understand how Neural Networks work and build your own Neural Network from scratch with Python. This TensorFlow tutorial will teach you to use TensorFlow for Classification and Regression Tasks. You will make further use of TensorFlow for Image Classification with Convolutional Neural Networks, Time Series Analysis with Recurrent Neural Networks and solving Unsupervised Learning Problems with AutoEncoders. This TensorFlow and Python course will help you conduct Reinforcement Learning with OpenAI Gym. You will be able to create Generative Adversarial Networks using TensorFlow.

This is one of the best TensorFlow tutorial in 2020.

by 365 Careers will help you build Deep Learning algorithms with TensorFlow. This TensorFlow tutorial will teach you to create Deep Learning algorithms from scratch in Python, using NumPy and TensorFlow. You will begin with NumPy and transfer to TensorFlow, to see the Machine Learning process from different angles.

This TensorFlow tutorial will move onto more complex topics including underfitting and overfitting, training, validation, n-fold cross-validation, testing, early stopping, initialization. You will understand optimization techniques like the stochastic gradient descent, batching, momentum, and learning rate schedules. This TensorFlow course will teach you to carry out preprocessing – standardization, normalization, and one-hot encoding.

You will:

TensorFlow and NumPy, two tools essential for creating and understanding Deep Learning algorithms.

Explore layers, their building blocks and activations – sigmoid, tanh, ReLu, softmax, etc.

Backpropagation process, intuitively and mathematically.

Spot and prevent overfitting.

State-of-the-art initialization methods.

Build deep neural networks using real data, implemented by real companies in the real world.

Create your very own Deep Learning Algorithm.

Gain hands-on TensorFlow experience.

This is one of the best TensorFlow course in 2020.

by Sefik Ilkin Serengil will help you learn how to build Deep Learning models for different business domains in TensorFlow. This TensorFlow course is for anyone interested in Machine Learning, Data Science or AI, you should check out this TensorFlow course. You will be able to distinguish classification and regression problems, apply supervised learning, and develop solutions. This TensorFlow tutorial will teach you to apply segmentation analysis to unsupervised learning and clustering. You will use Keras. By the end of the course, you will know how to tune Machine Learning models to produce more successful results.

TensorFlow and the Google Cloud ML Engine for Deep Learning by Loony Corn is a comprehensive TensorFlow tutorial. This TensorFlow tutorial starts at TensorFlow basics. You will learn to build and execute machine learning models using TensorFlow. This TensorFlow course will teach you to implement Deep Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks. You will understand and implement unsupervised learning models such as Clustering and Autoencoders.

You will learn:

Deep learning basics including what a neuron is, how neural networks connect neurons to ‘learn’ complex functions and how TF makes it easy to build neural network models.

Using Deep Learning for the famous ML problems inclduing regression, classification, clustering and autoencoding.

Convolutional Neural Networks(CNNs) including Kernel functions, feature maps and CNNs v DNNs.

Recurrent Neural Networks(RNNs) including LSTMs, Back-propagation through time and dealing with vanishing/exploding gradients.

Unsupervised learning techniques including Autoencoding, K-means clustering, PCA as autoencoding .

Working with images, documents and word embeddings

Google Cloud ML Engine including Distributed training and prediction of TF models on the cloud.

Working with TensorFlow estimators.

This is one of the best TensorFlow tutorial for Deep Learning.

by Minerva Singh will help you understand TensorFlow for Machine Learning & Deep Learning. You will use Anaconda/iPython for Data Science. This TensorFlow tutorial will help you learn how to install and use Tensorflow with Anaconda. You will implement statistical and Machine Learning, Neural Network Modelling, Deep Learning based unsupervised learning and Deep Learning based supervised learning.

This TensorFlow tutorial covers:

Introduction to Python Data Science

Introduction to Anaconda

Jupyter notebooks for implementing data science techniques in Python

Comprehensive guide to Tensorflow installation

Introduction to Python data science packages

Introduction to Pandas and Numpy

Basics of the Tensorflow syntax and graphing environment

Statistical modelling

Machine Learning, Supervised Learning, Unsupervised Learning in the Tensorflow framework

Create artificial neural networks and deep learning structures

This is one of the best TensorFlow bootcamps in 2020.

Best TensorFlow books 2020

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron will help you learndeep learning with TensorFlow and Scikit-Learn. This TensorFlow book will teach you a range of techniques, starting with simple linear regression and progressing to deep neural networks. You will learn from exercises, examples and minimal theory.

Through a series of recent breakthroughs, deep learning has stimulated the whole field of machine learning. Now even programmers who know almost nothing about this technology can use simple and effective tools to implement programs that can learn from data. This practical book shows you how. Using concrete examples, minimal theory and two production-ready Python structures, Scikit-Learn and TensorFlow helps you gain an intuitive knowledge of the concepts and tools of intelligent system building. You will:

Explore machine learning, including neural nets

Use scikit-learn to track an example machine-learning project

Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods

Use the TensorFlow library to build and train neural nets

Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning

Apply practical code examples without acquiring excessive machine learning theory or algorithm details

This is one of the best TensorFlow book in 2020.

Deep Learning with TensorFlow 2 and Keras, the second edition teaches nervous networks and deep learning techniques along with Tensorflow (TF) and Keras. You will learn how to write deep learning applications on the most powerful, popular and scalable machine learning stacks. Presentation and then teaching key machine and deep learning techniques using TensorFlow 2 and Keras from scratch. Understand the basics of deep learning and machine learning through clear explanations and numerous code examples.

TensorFlow is the machine learning library of choice for professional applications, while Keras TensorFlow provides a simple and powerful Python API for access. TensorFlow 2 provides complete Keras integration, making advanced machine learning easier and more convenient than ever before. The book also introduces neural networks, including TensorFlow, through the core applications (regression, convents (CNN), GAN, RNN, NLP), two examples of applications, followed by TF, WM and TenMo in production. You are about to learn:

Build machine learning and deep learning systems with TensorFlow 2 and Keras APIRegression analysis, use the most popular method of machine learningUnderstand Convents (Conventional Neural Networks) and how it is necessary for deep learning methods such as image classificationUse GANs (opposite generator networks) to create new data that matches existing modelsDiscover RNNs (repetitive neural networks) that can intelligently process input sequences, using one part of a sequence to correctly interpret anotherApply deep learning in natural human language and interpret the text in natural language to create an appropriate responseTrain your models in the cloud and keep the TL to work in a real environmentLearn how Google tools can automate simple ML workflows without the need for complex modeling

This book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow for. Some knowledge of machine learning can be expected. Best Tensorflow 2 book

Advanced Deep Learning with TensorFlow 2 and Keras, the second edition is a fully updated version of the best-selling guide to advanced deep learning strategies available today. Revised to TensorFlow 2.x, this version introduces you to the practical aspects of in-depth learning in semantics (FCN and PSPinet), with new chapters of observable learning using interactive information, object detection (SSD) and segmentation, allowing you to create your own cutting-AI projects. The book presents hands-on projects using Keras as an open source deep learning library that shows you how to create more efficient AI with the latest techniques.

Updated and revised the second edition of the successful guide to advanced deep learning with the help of TensorFlow 2 and Keras. Discover the most advanced deep learning techniques that generate modern AI results.New coverage of interactive deep learning. Information, object identification and semantic segmentation.Completely updated to TensorFlow 2.x.

Starting with an overview of multilayer perceptors (MLPs), conventional neural networks (CNNs) and recursive neural networks (RNNs), the book explores network architectures as well as introduces more advanced techniques. Deep neurons. Next, you will learn about GANS and how they can unlock new levels of AI performance.

Next, you will discover how to implement a variational autoencoder (VAE), and how GANs and VAE have the ability to synthesize generation data that can be highly trusted by humans. You will also learn how to apply DRL, such as DR Q-learning and policy grading methods, which are essential for many modern AI results. You are about to learn:

Use reciprocal information maximization techniques to facilitate unconventional learningUse sections to identify the pixel class of each object in an imageIdentify both the selection frame and the class of objects in an image using object detectionLearn the basics of advanced techniques – MLPS, CNN and RNNUnderstanding deep neural networks – including Resnet and DensnetUnderstand and create autoregressive models – Automatic Encoder, VAE and GNDiscover and apply deep reinforcement learning methods

This is not an early book, so Python requires skill. The reader should be familiar with certain machine learning methods and practical experience with DL will also be helpful. Knowledge of Keras or Tensorflow 2.0 is not required but it is recommended.

Learning TensorFlow: A Guide to Building Deep Learning Systems

Learning TensorFlow: A Guide to Building Deep Learning Systems by Tom Hope, Yehezkel S. Resheff and Itay Lieder gives a hands-on approach to TensorFlow fundamentals. Inspired by the human brain, deep neural networks made up of huge amounts of data can solve complex tasks with unprecedented precision. This book provides an end-to-end guide to TensorFlow’s top open source software library that helps you build and train computer perspectives, automated natural language processing (NLP), neural networks for recognition, vocal and general predictive analysis.

Authors Tom Hope, Ezekiel Risheff, and Itte have proposed a hands-on approach to the basics of tensorflow to a wide range of technological audiences, from scientists and data engineers to students and researchers. Before delving further into topics such as neural network architecture, tensorboard visualization, tensorflow abstraction libraries, and multithreaded input pipelines, you will begin to study some basic examples in tensorflow. By the end of this book, you will know how to create and set up a production-ready deep learning system in TensorFlow.

Machine Learning with TensorFlow

Sale Machine Learning with TensorFlow Shukla, Nishant (Author)

English (Publication Language)

272 Pages - 02/12/2018 (Publication Date) - Manning Publications (Publisher)

Machine Learning with TensorFlow by Nishant Shukla will give you a solid foundation in machine-learning concepts with hands-on experience coding TensorFlow with Python. This TensorFlow book will teach you how to use TensorFlow for machine-learning and building deep-learning applications. Machine learning with TensorFlow gives readers a solid foundation in machine learning concepts as well as coding experience with TensorFlow with Python Hands TensorFlow, Google’s library for larger scale machine learning Makes.

Machine learning with TensorFlow gives readers a solid foundation on machine learning concepts as well as coding experience with TensorFlow with Python hands You will learn the basics by working with classic predictions, classification and clustering algorithms. Next, you’ll move on to the chapters on finance: explore deep learning concepts such as auto-encoder, repetitive neural networks, and reinforcement learning. Digest this book and you are ready to use TensorFlow for your own machine learning and deep learning applications.

Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python by Santanu Pattanayak gives you a practical and hands-on guide to learn deep learning from scratch with TensorFlow. This TensorFlow book will get you up to speed quickly using TensorFlow and teach you to optimize different deep learning architectures.

Install deep learning solutions in production very easily using TensorFlow. You will also develop mathematical understanding and intuition to discover new architecture and deep learning solutions on your own. Pro Deep Learning with Tensorflow provides practical and hands-on skills so you can learn deep learning from scratch and set up meaningful deep learning solutions. This book will allow you to quickly familiarize yourself with Tensorflow and adapt to various in-depth learning architectures.

All the practical aspects of deep learning relevant to any industry are highlighted in this book. You will be able to use the prototypes displayed to create new deep learning applications. The code presented in the book is available in the form of an iPot notebook and scripts that allow you to try out examples and expand them in interesting ways. You will have the mathematical foundation and scientific knowledge to continue research in this area and give back to the community. You are about to learn:

Learn full stack full using tensorflow and gain a solid math foundation for deep learningInstall complex deep learning solutions in production using tensorflowConduct and experiment with in-depth learning research using tensorflow

Data scientists and machine learning professionals, software developers, undergraduate students and open source enthusiasts

Find out how to build, train, and serve your own deep neural network with TensorFlow 2 and Keras, a practical guide to building high performance systems for object detection, segmentation, video processing, smartphone applications, and more. Apply modern solutions to a wide range of applications such as object detection and video analysis. How to run your models on mobile devices and web pages and improve their functionality.Computer vision solutions are becoming more common and are gaining ground in areas such as healthcare, automobiles, social media and robotics. This book will help you explore TensorFlow 2, a new version of Google’s open source framework for machine learning. You will understand how to take advantage of the use of Convulsive Neural Networks (CNN) for visual work.

The practical computer approach with TensorFlow 2 begins with the basics of computer vision and deep learning, teaching you how to build neural networks from scratch. You will learn the features that make TensorFlock the most used AI library, as well as its intuitive Keras interface, and move on to CNN’s effective creation, training, and deployment. Complete with concrete code examples, the book shows how images can be categorized with modern solutions such as Inception and Resnet and extract specific content using U Only Look (YOLO), Mask R-CNN and U-Net. You can create Generator Anti-Networks (GAN) and Variable Automatic Encoder (VAE) to create and edit images and LSTM for video analysis. During this process, you will gain advanced insights into learning, migration, domain adaptation, and mobile and web deployment, among other key concepts.

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