Best TensorFlow Courses 2020
Best TensorFlow Books 2020
Best TensorFlow tutorials 2020
This course will walk you through using Google’s TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google’s TensorFlow framework in an easy to understand way. Other courses and tutorials tend to stay away from the flow of pure tension and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning!
This course is designed to balance theory and practical implementation, with comprehensive jupyter notebook code guides and easy-to-navigate slides and notes. We also have plenty of exercises to test your new skills along the way!
This course covers a variety of topics, including
Neural Network Basics
Artificial Neural Networks
Densely Connected Networks
Convolutional Neural Networks
Recurrent Neural Networks
TensorFlow is an open source software library for numerical computation using data flow graphs. The nodes of the graph represent mathematical operations, while the edges of the graph represent the arrays of multidimensional data (tensors) communicated between them. The flexible architecture allows you to deploy compute to one or more processors or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working as part of the Google Brain team within Google’s Machine Intelligence research organization with the goal of conducting research on machine learning and networking. of deep neurons, but the system is general enough to be applicable in a wide variety of other fields as well. It is used by large companies around the world including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel and of course Google!
This is the best TensorFlow tutorial in 2020.
A Complete Guide on TensorFlow 2.0 using Keras API
TensorFlow 2.0 has just been released and introduced many features that simplify the model development and maintenance processes. On the educational side, it improves people’s understanding by simplifying many complex concepts. From an industry perspective, models are much easier to understand, maintain, and develop.
Deep Learning is one of the fastest growing areas of artificial intelligence. Over the past few years, we have proven that even the simplest Deep Learning models can solve very difficult and complex tasks. Now that the Deep Learning buzzword has partially passed, people are unleashing its power and potential to improve their products.
The course is structured to cover all topics of neural network modeling and training to put it into production. In the first part of the course, you will learn about the technology stack that we will be using throughout the course (section 1) and the basics and syntax of the TensorFlow 2.0 library (section 2). In the second part of the course, we will delve into the exciting world of deep learning. During this part of the course, you will implement several types of neural networks (fully connected neural network (section 3), convolutional neural network (section 4), recurrent neural network (section 5)). By the end of this part, section 6, you will learn and build their own transfer learning application that will achieve cutting edge results (SOTA) on the Dogs vs Cats dataset.
After successfully completing part 2 of the course and finally learning how to implement neural networks, in part 3 of the course you will learn how to create your own stock trading bot using Reinforcement Learning, in particular the Deep-Q network. . about TensorFlow Extended (TFX). In this part of the course, you will learn how to work with data and how to create your own data pipelines for production. In section 8 we will check if the dataset has anomalies using the TensorFlow Data Validation library and after learning how to check a dataset for anomalies in section 9 we will create our own pipeline data preprocessing using the TensorFlow Transform library.
In Section 10 of the course, you will learn and build your own Fashion API using the Flask Python library and a pre-trained model. Throughout this section you will have a better idea of how to send a request to a model on the internet. However, at this point, the architecture around the model is not scalable for millions of requests. Enter section 11. In this section of the course, you will learn how to improve the solution from the previous section using the TensorFlow Serving library. In a very easy way, you will learn and create your own image classification API which can support millions of requests per day! Nowadays, it is more and more popular to have a Deep Learning model in an Android or iOS app, but neural networks require a lot of power and resources! This is where the TensorFlow Lite library comes in. In Section 12 of the course, you will learn how to optimize and convert any neural network to make it suitable for a mobile device.
To conclude with the learning process and part 5 of the course, in section 13 you will learn how to distribute the training of any neural network to multiple GPUs or even to servers using the TensorFlow 2.0 library.
You will learn:
How to use Tensorflow 2.0 in Data Science
Important differences between Tensorflow 1.x and Tensorflow 2.0
How to implement artificial neural networks in Tensorflow 2.0
How to implement convolutional neural networks in Tensorflow 2.0
How to implement recurrent neural networks in Tensorflow 2.0
How to Create Your Own Transfer Learning App in Tensorflow 2.0
How to Create a Stock Trading Bot Using Reinforcement Learning (Deep-Q Network)
How to create a machine learning pipeline in Tensorflow 2.0
How to perform data validation and preprocessing of datasets using TensorFlow data validation and TensorFlow transformation.
Production of a TensorFlow 2.0 model
How to create a Fashion API with Flask and TensorFlow 2.0
How to serve a TensorFlow model with the RESTful API
Tensorflow 2.0: Deep Learning and Artificial Intelligence
It’s been almost 4 years since Tensorflow came out, and the library has evolved into its second official version. Tensorflow is Google’s library for deep learning and artificial intelligence. Deep Learning has recently been responsible for some amazing achievements, such as:
Generate beautiful photorealistic images of people and objects that never existed (GAN)
Defeat World Champions in Go Strategy Game and Complex Video Games like CS: GO and Dota 2 (Deep Reinforcement Learning)
Autonomous cars (computer vision)
Speech recognition (e.g. Siri) and machine translation (natural language processing)
Even create videos of people doing and saying things they’ve never done (DeepFakes – a potentially harmful deep learning app)
Tensorflow is the world’s most popular deep learning library, and it was built by Google, whose parent company Alphabet recently became the world’s most cash-rich company (days before writing this). It’s the library of choice for many companies doing AI and machine learning. In other words, if you want to do deep learning, you have to know Tensorflow.
We’ll start with some very basic machine learning models and move on to some cutting edge concepts.
Along the way, you’ll learn about all of the major deep learning architectures, such as deep neural networks, convolutional neural networks (image processing), and recurrent neural networks (sequence data).
Current projects include:
Natural language processing (NLP)
Learning transfer for computer vision
Generative Adversary Networks (GANs)
Deep Reinforcement Learning Stock Trading Bot
Learn how to convert your previous code to use Tensorflow 2.0, and there are some brand new never-before-seen projects in this course, such as time forecast for sets and how to do stock forecast. This course is designed for students who want to learn quickly, but there are also “deeper” sections in case you want to dig a little deeper into the theory (like what a loss function is and what the different types of gradient descent are. ).
Advanced topics in Tensorflow include:
Deploy a model with Tensorflow Serving (Tensorflow in the cloud)
Deploy a model with Tensorflow Lite (mobile and embedded applications)
Distributed Tensorflow training with distribution strategies
Write your own custom Tensorflow model
Converting Tensorflow 1.x code to Tensorflow 2.0
Constants, variables and tensors
This course focuses on breadth rather than depth, with less theory in favor of building more interesting things. If you are looking for a more theoretical course, this is not it. Generally speaking, for each of these subjects (recommender systems, natural language processing, reinforcement learning, computer vision, GAN, etc.), I already have courses specifically focused on these subjects.
You will learn:
Artificial Neural Networks (ANN) / Deep Neural Networks (DNN)
Predict stock returns
Time series forecasts
How to Create a Deep Reinforcement Learning Stock Trading Bot
GANs (Generative Adversarial Networks)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Use Tensorflow Serving to serve your model using a RESTful API
Use Tensorflow Lite to export your model for mobile (Android, iOS) and embedded devices
Use Tensorflow’s distribution strategies to parallelize learning
Low-level tensorflow, gradient ribbon and how to create your own custom models
Natural Language Processing (NLP) with Deep Learning
Demonstrate Moore’s Law Using Code
Transfer learning to create state-of-the-art image classifiers
Complete Tensorflow 2 and Keras Deep Learning Bootcamp
This course will guide you in using Google’s latest TensorFlow 2 framework to create artificial neural networks for deep learning! This course aims to give you an easy-to-understand guide to the complexities of Google’s TensorFlow 2 framework in an easy-to-understand manner. We will focus on understanding the latest updates to TensorFlow and using the Keras API (the official API of TensorFlow 2.0) to quickly and easily build models. In this course, we will create models to predict future prices, classify medical images, predict future sales data, artificially generate complete new text, and much more!This course is designed to balance theory and practical implementation, with comprehensive jupyter notebook code guides and easy-to-navigate slides and notes. We also have plenty of exercises to test your new skills along the way!
This course covers a variety of topics:
Intensive NumPy course
Introductory course in Pandas data analysis
Introductory course in data visualization
Neural Network Basics
Basics of Keras syntax
Artificial neural networks
Densely connected networks
Convolutional neural networks
Recurrent neural networks
GAN – Generative Conflict Networks
Deployment of TensorFlow in production
Keras, a user-friendly API standard for machine learning, will be the central high-level API used to build and train models. The Keras API makes it easy to get started with TensorFlow 2. It is important to note that Keras provides several templating APIs (sequential, functional, and subclassification), so that you can choose the right level of abstraction for your project. The TensorFlow implementation contains enhancements including fast execution, for immediate iteration and intuitive debugging, and tf.data, for building scalable input pipelines. TensorFlow 2 makes it easy to take new ideas from concept to code, and from model to release. TensorFlow 2.0 incorporates a number of features that allow advanced model definition and training without sacrificing speed or performance
You will learn:
Learn how to use TensorFlow 2.0 for Deep Learning
Leverage the Keras API to quickly build models that run on Tensorflow 2
Perform image classification with convolutional neural networks
Using Deep Learning for Medical Imaging
Predicting time series data with recurrent neural networks
Use generative conflicting networks (GANs) to generate images
Use deep learning for style transfer
Generate text with RNNs and natural language processing
Serve Tensorflow models via an API
Use GPUs for accelerated deep learning
Best TensorFlow books 2020
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems 2nd Edition
- Géron, Aurélien (Author)
- English (Publication Language)
- 856 Pages - 10/15/2019 (Publication Date) - O'Reilly Media (Publisher)
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.
- 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.
Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more, 2nd Edition
- Atienza, Rowel (Author)
- English (Publication Language)
- 512 Pages - 02/28/2020 (Publication Date) - Packt Publishing (Publisher)
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.
Learning TensorFlow: A Guide to Building Deep Learning Systems
- Hope, Tom (Author)
- English (Publication Language)
- 242 Pages - 09/05/2017 (Publication Date) - O'Reilly Media (Publisher)
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
- 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.