Tensorflow Artificial Intelligence With Tensorflow 3-In-1

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.


Homepage

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