Tensorflow: Artificial Intelligence With Tensorflow: 3-In-1

Begin your journey to build next-generation AI models from scratch with TensorFlow and create your own machine learning

What you’ll learn
Build custom reusable components for your mobile app and develop native apps for both iOS and Android
Perform animations in your applications using the animation APIs
Test and deploy your application for a production-ready environment
Grasp the concepts of Redux state management to build scalable apps
Add navigation to your App to build UX components for your React Native App
Integrate with Firebase as a data store and learn how to authenticate a user

Requirements
Knowledge of Data Science

Description
Google’s TensorFlow framework is the current leading software for implementing and experimenting with the algorithms that power AI and machine learning. Google deploys TensorFlow for many of its products, such as Translate and Maps.

TensorFlow is one of the most used frameworks for Deep Learning and AI. This course will be your guide to understand and learn the concepts of Artificial intelligence by applying them in a real-world project with TensorFlow.

This comprehensive 3-in-1 course is a practical approach to deep learning and deep reinforcement learning for building real-world applications using TensorFlow. Learn how models are made in production settings, and how to best structure your TensorFlow programs. Build models to solve problems in Computer vision, Natural Language Processing, Reinforcement Learning, Finance, and more!

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Learn Artificial Intelligence with TensorFlow, covers creating your own machine learning solutions. You’ll embark on this journey by quickly wrapping up some important fundamental concepts, followed by a focus on TensorFlow to complete tasks in computer vision and natural language processing. You will be introduced to some important tips and tricks necessary for enhancing the efficiency of our models. We will highlight how TensorFlow is used in an advanced environment and brush through some of the unique concepts at the cutting edge of practical AI.

The second course, Hands-on Artificial Intelligence with TensorFlow, covers a practical approach to deep learning and deep reinforcement learning for building real-world applications using TensorFlow. This course will take you through all the relevant AI domains, tools, and algorithms required to build optimal solutions and will show you how to implement them hands-on. You’ll then be taken through techniques such as reinforcement learning, heuristic searches, neural networks, Computer Vision, OpenAI Gym, and more in different stages of your application. You’ll learn how TensorFlow can be used to analyze a variety of data sets and will learn to optimize various AI algorithms. By the end of the course, you will have learned to build intelligent apps by leveraging the full potential of Artificial Intelligence with TensorFlow..

The third course, TensorFlow 1.x Deep Learning Recipes for Artificial Intelligence Applications, covers recipes for Computer vision, Natural Language Processing, Reinforcement Learning, Finance, and more! Build models to solve problems in different domains such as Computer vision, Natural Language Processing, Reinforcement Learning, Finance, and more. Taking a Cookbook approach, this course presents you with easy-to-follow recipes to show the use of advanced Deep Learning techniques and their implementation in TensorFlow. After taking this tutorial you will be able to start building advanced Deep Learning models with TensorFlow for applications with a wide range of fields.

By the end of the course, you’ll begin your journey to build next-generation AI models from scratch with TensorFlow and create your own machine learning solutions.

About the Authors
Brandon McKinzie is an NLP engineer/researcher and lover of all things associated with machine learning, with a particular interest in deep learning for natural language processing. The author is extremely passionate about contributing to research and learning in general, and in his free time he’s either working through textbooks, personal projects, or browsing blogs related to ML/AI.

Saikat Basak is currently working as a machine learning engineer at Kepler Lab, the research & development wing of SapientRazorfish, India. His work at Kepler involves problem-solving using machine learning, researching and building deep learning models. Saikat is extremely passionate about Artificial intelligence becoming a reality and hopes to be one of the architects of the future of AI.

Alvaro Fuentes is a Data Scientist with an M.S. in Quantitative Economics and a M.S. in Applied Mathematics with more than 10 years’ experience in analytical roles. He worked in the Central Bank of Guatemala as an Economic Analyst, building models for economic and financial data. He founded Quant Company to provide consulting and training services in Data Science topics and has been a consultant for many projects in fields such as: Business, Education, Psychology and Mass Media. He also has taught many (online and on-site) courses to students from around the World in topics such as Data Science, Mathematics, Statistics, R programming, and Python. Alvaro Fuentes is a big Python fan; he has been working with Python for about 4 years and uses it routinely to analyze data and make predictions. He also has used it in a couple of software projects. He is also a big R fan, and doesn’t like the controversy between what is the “best” R or Python; he uses them both. He is also very interested in the Spark approach to big data, and likes the way it simplifies complicated topics. He is not a software engineer or a developer but is generally interested in web technologies. He also has technical skills in R programming, Spark, SQL (PostgreSQL), MS Excel, machine learning, statistical analysis, econometrics, and mathematical modeling. Predictive Analytics is a topic in which he has both professional and teaching experience. He has solved practical problems in his consulting practice using Python tools for predictive analytics and the topics of predictive analytics are part of a more general course on Data Science with Python that he teaches online.

Overview
Section 1: Learn Artificial Intelligence with TensorFlow

Lecture 1 The Course Overview

Lecture 2 Machine Learning Basics

Lecture 3 TensorFlow Basics Part 1: Tensors and Variables

Lecture 4 TensorFlow Basics Part 2: Graphs and Sessions

Lecture 5 TensorFlow Basics Part 3: Training, Saving, and Loading

Lecture 6 Convolutional Neural Networks

Lecture 7 Preprocessing, Pooling, and Batch Normalization

Lecture 8 Training a CNN on CIFAR-10 – Part 1

Lecture 9 Training a CNN on CIFAR-10 – Part 2

Lecture 10 Embeddings

Lecture 11 Recurrent Neural Networks

Lecture 12 Bidirectionality and Stacking RNNs

Lecture 13 Models for Text Classification – Part 1

Lecture 14 Models for Text Classification – Part 2

Lecture 15 TensorBoard

Lecture 16 Working with Estimators

Lecture 17 Training Tips

Lecture 18 Debugging Strategies

Lecture 19 Requirements for ML at Scale

Lecture 20 TensorFlow with C++

Lecture 21 TensorFlow Serving

Lecture 22 TensorFlow Lite

Lecture 23 TPUs

Lecture 24 AutoML

Lecture 25 TensorFlow Eager

Lecture 26 Course Summary and Next Steps

Section 2: Hands-on Artificial Intelligence with TensorFlow

Lecture 27 The Course Overview

Lecture 28 The Current State of Artificial Intelligence

Lecture 29 Setting Up the Environment for Deep Learning

Lecture 30 Deep Learning in Fashion

Lecture 31 An Intro to Transfer Learning: Skin Cancer Classification

Lecture 32 Fundamentals of Object Localization and Detection

Lecture 33 YOLO(You Only Look Once): Single Shot Object Detection

Lecture 34 Unravelling Adversarial Learning and Generative Adversarial Nets

Lecture 35 Generating Handwritten Digits Using GANs

Lecture 36 Generating New Pokemons Using a DCGAN

Lecture 37 Super-Resolution Generative Adversarial Networks

Lecture 38 Setting Up OpenAI Gym

Lecture 39 Introduction to Reinforcement Learning

Lecture 40 Simple Q-Learning: Building Our First Video Game Bot

Lecture 41 Deep Q-Learning: Building a Game Bot That Plays the Classic Atari Games

Lecture 42 Deep Reinforcement Learning with Policy Gradient – AI that Plays Pong

Section 3: TensorFlow 1.x Deep Learning Recipes for Artificial Intelligence Applications

Lecture 43 The Course Overview

Lecture 44 Installation and Setup

Lecture 45 Defining Layers for Image Recognition

Lecture 46 Building an Image Classifier with CNNs

Lecture 47 Building Better CNNs with Regularization

Lecture 48 Transfer Learning

Lecture 49 The Intuition Behind RNNs

Lecture 50 Time Series Forecasting with RNN

Lecture 51 Producing Word Embeddings for NLP Tasks

Lecture 52 Processing Text Sequences with LSTM Networks

Lecture 53 Guessing Correlations from Scatter Plots

Lecture 54 Introduction to Generative Adversarial Networks

Lecture 55 Creating Images with GANs

Lecture 56 Sequence to Sequence Models

Lecture 57 Building a Language Translator

Lecture 58 Key Concepts in Reinforcement Learning

Lecture 59 A Simple Environment and Basic Policies

Lecture 60 Training a Neural Network Policy

Lecture 61 Using an Intelligent Agent

Data science enthusiast looking to achieve the power of Artificial Intelligence for developing machine learning solutions using TensorFlow, then this course is what you need.,Developers and aspiring Data Science professionals who would like to develop their AI techniques to create smart and robust applications.,Data Analysts, Data Scientists, Data Engineers, Software Engineers, and anyone working with Python and data who wants to perform Machine Learning on a regular basis and use TensorFlow to build Deep Learning models.

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