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TensorFlow: Application Development Using TensorFlow: 2-in-1

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TensorFlow: Application Development Using TensorFlow: 2-in-1, available at $19.99, has an average rating of 3.25, with 46 lectures, based on 4 reviews, and has 67 subscribers.

You will learn about Develop AI systems using different machine learning models Deploy TensorFlow models on iOS and Android platforms Design solutions to real-life computer vision problems to tackle typical challenges when developing real-life applications Explore generative models and how they generate information from random noise. Optimize machine learning models for better performance and accuracy Understand different deep learning models for computer vision This course is ideal for individuals who are Developers and aspiring Data Science professionals who would like to develop their AI techniques to create smart and robust applications. or Mobile developers who want to make their mobile applications smart with TensorFlow to solve machine learning, computer vision or deep learning problems such as data prediction, visual or audio recognition, and more. It is particularly useful for Developers and aspiring Data Science professionals who would like to develop their AI techniques to create smart and robust applications. or Mobile developers who want to make their mobile applications smart with TensorFlow to solve machine learning, computer vision or deep learning problems such as data prediction, visual or audio recognition, and more.

Enroll now: TensorFlow: Application Development Using TensorFlow: 2-in-1

Summary

Title: TensorFlow: Application Development Using TensorFlow: 2-in-1

Price: $19.99

Average Rating: 3.25

Number of Lectures: 46

Number of Published Lectures: 46

Number of Curriculum Items: 46

Number of Published Curriculum Objects: 46

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Develop AI systems using different machine learning models
  • Deploy TensorFlow models on iOS and Android platforms
  • Design solutions to real-life computer vision problems to tackle typical challenges when developing real-life applications
  • Explore generative models and how they generate information from random noise.
  • Optimize machine learning models for better performance and accuracy
  • Understand different deep learning models for computer vision

Who Should Attend

  • Developers and aspiring Data Science professionals who would like to develop their AI techniques to create smart and robust applications.
  • Mobile developers who want to make their mobile applications smart with TensorFlow to solve machine learning, computer vision or deep learning problems such as data prediction, visual or audio recognition, and more.

Target Audiences

  • Developers and aspiring Data Science professionals who would like to develop their AI techniques to create smart and robust applications.
  • Mobile developers who want to make their mobile applications smart with TensorFlow to solve machine learning, computer vision or deep learning problems such as data prediction, visual or audio recognition, and more.

The flexible architecture of TensorFlow allows you to create and deploy deep learning and deep reinforcement learning models for building intelligent, real-world applications. TensorFlow facilitates AI to build and train systems, in particular, neural networks.

This comprehensive 2-in-1 course is a hands-on approach to problem-solving. Gain practical knowledge by coding TensorFlow models to solve real-life problems such as gesture or voice recognition. You’ll also learn to deploy TensorFlow models on mobile devices.

Contents and Overview

This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.

The first 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 teach you how to combine the power of Artificial Intelligence and TensorFlow to develop some exciting applications for the real world. You will 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.

The second course, Hands-on TensorFlow Lite for Intelligent Mobile Apps, covers application of Machine Learning models in real-time in mobile devices with the new and powerful TensorFlow Lite. This course will teach you how to solve real-life problems related to Artificial Intelligence—such as image, text, and voice recognition—by developing models in TensorFlow to make your applications really smart. You will understand what Machine Learning can do for you and your mobile applications in the most efficient way. With the capabilities of TensorFlow Lite you will learn to improve the performance of your mobile application and make it smart.

By the end of the course, you’ll be able to implement AI in your mobile applications as well as build intelligent apps by leveraging the full potential of Artificial Intelligence with TensorFlow.

About the Authors

  • SaikatBasakis 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.
  • Juan Miguel Valverde Martinez is a Deep Learning, Computer Vision and TensorFlow enthusiast, with an MSc in IT and Cognition from the University of Copenhagen. His main interests are Computer Vision and Medical Image Analysis, and he has recently been more interested in Adversarial Training and Natural Language Processing. In his free time, he likes to read papers and research. In addition to Computer Science, he also enjoys learning languages and cooking, especially Mediterranean and Asian dishes.

Course Curriculum

Chapter 1: 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 2: Hands-on TensorFlow Lite for Intelligent Mobile Apps

Lecture 1: The Course Overview

Lecture 2: Deep Learning

Lecture 3: Deep Learning Components

Lecture 4: TensorFlow

Lecture 5: TensorFlow Lite

Lecture 6: Hello World in TensorFlow

Lecture 7: Debugging Our Model

Lecture 8: Parameter Study

Lecture 9: Overfitting

Lecture 10: Deployment in iOS with TensorFlow Lite

Lecture 11: Introduction to the Problem and Dataset

Lecture 12: Developing the Handwriting Recognition Model

Lecture 13: Parameter Study

Lecture 14: Testing the Model

Lecture 15: Deployment in Android with TensorFlow Lite

Lecture 16: Data Augmentation

Lecture 17: Developing the Pattern Recognition Model

Lecture 18: Parameter Study and Data Augmentation

Lecture 19: Testing the Model

Lecture 20: Deployment in Android with TensorFlow Lite

Lecture 21: Introduction

Lecture 22: Developing the Gesture Recognition Model

Lecture 23: Parameter Study and Data Augmentation

Lecture 24: Adapting and Debugging the Model

Lecture 25: Deployment in Android with TensorFlow Lite

Lecture 26: Introduction

Lecture 27: Developing the Voice Recognition Model

Lecture 28: Dropout and Dataset Generation

Lecture 29: Deployment in Android with TensorFlow Lite

Lecture 30: Course Summary

Instructors

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  • 5 stars: 1 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!

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