TensorFlow- Artificial Intelligence with TensorFlow- 3-in-1

TensorFlow: Artificial Intelligence with TensorFlow: 3-in-1, available at $49.99, has an average rating of 3.7, with 61 lectures, 3 quizzes, based on 18 reviews, and has 149 subscribers.

You will learn about 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 This course is ideal for individuals who are 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. or Developers and aspiring Data Science professionals who would like to develop their AI techniques to create smart and robust applications. or 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. It is particularly useful for 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. or Developers and aspiring Data Science professionals who would like to develop their AI techniques to create smart and robust applications. or 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.

Enroll now: TensorFlow: Artificial Intelligence with TensorFlow: 3-in-1

Summary

Title: TensorFlow: Artificial Intelligence with TensorFlow: 3-in-1

Price: $49.99

Average Rating: 3.7

Number of Lectures: 61

Number of Quizzes: 3

Number of Published Lectures: 61

Number of Published Quizzes: 3

Number of Curriculum Items: 64

Number of Published Curriculum Objects: 64

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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.

Target Audiences

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.

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 McKinzieis 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.

Course Curriculum

Chapter 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

Chapter 2: Hands-on Artificial Intelligence with TensorFlow

Lecture 1: The Course Overview

Lecture 2: The Current State of Artificial Intelligence

Lecture 3: Setting Up the Environment for Deep Learning

Lecture 4: Deep Learning in Fashion

Lecture 5: An Intro to Transfer Learning: Skin Cancer Classification

Lecture 6: Fundamentals of Object Localization and Detection

Lecture 7: YOLO(You Only Look Once): Single Shot Object Detection

Lecture 8: Unravelling Adversarial Learning and Generative Adversarial Nets

Lecture 9: Generating Handwritten Digits Using GANs

Lecture 10: Generating New Pokemons Using a DCGAN

Lecture 11: Super-Resolution Generative Adversarial Networks

Lecture 12: Setting Up OpenAI Gym

Lecture 13: Introduction to Reinforcement Learning

Lecture 14: Simple Q-Learning: Building Our First Video Game Bot

Lecture 15: Deep Q-Learning: Building a Game Bot That Plays the Classic Atari Games

Lecture 16: Deep Reinforcement Learning with Policy Gradient – AI that Plays Pong

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

Lecture 1: The Course Overview

Lecture 2: Installation and Setup

Lecture 3: Defining Layers for Image Recognition

Lecture 4: Building an Image Classifier with CNNs

Lecture 5: Building Better CNNs with Regularization

Lecture 6: Transfer Learning

Lecture 7: The Intuition Behind RNNs

Lecture 8: Time Series Forecasting with RNN

Lecture 9: Producing Word Embeddings for NLP Tasks

Lecture 10: Processing Text Sequences with LSTM Networks

Lecture 11: Guessing Correlations from Scatter Plots

Lecture 12: Introduction to Generative Adversarial Networks

Lecture 13: Creating Images with GANs

Lecture 14: Sequence to Sequence Models

Lecture 15: Building a Language Translator

Lecture 16: Key Concepts in Reinforcement Learning

Lecture 17: A Simple Environment and Basic Policies

Lecture 18: Training a Neural Network Policy

Lecture 19: Using an Intelligent Agent

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 3 votes

2 stars: 0 votes

3 stars: 3 votes

4 stars: 6 votes

5 stars: 6 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!