TensorFlow: Application Development Using TensorFlow: 2-in-1
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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Packt Publishing
Tech Knowledge in Motion
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- 3 stars: 1 votes
- 4 stars: 1 votes
- 5 stars: 1 votes
Frequently Asked Questions
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