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

    Posted By: ELK1nG
    Tensorflow: Artificial Intelligence With Tensorflow: 3-In-1

    Tensorflow: Artificial Intelligence With Tensorflow: 3-In-1
    Last updated 7/2018
    MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
    Language: English | Size: 3.73 GB | Duration: 9h 3m

    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.