Hands-On Transfer Learning with TensorFlow 2.0

Hands-On Transfer Learning with TensorFlow 2.0, available at $54.99, has an average rating of 4.7, with 13 lectures, 4 quizzes, based on 33 reviews, and has 160 subscribers.

You will learn about Build your own image classification application using Convolutional Neural Networks and TensorFlow 2.0 Improve any image classification system by leveraging the power of transfer learning on Convolutional Neural Networks, in only a few lines of code Discover how users feel about IMDB movies by building a Sentiment Analysis system utilizing the power of Recurrent Neural Networks and the TensorFlow 2.0 high-level API Learn how to perform transfer learning on Recurrent Neural Networks and powerfully improve any text-based system Learn how to use TensorFlow Hub and TensorFlow Lite to make transfer learning much easier This course is ideal for individuals who are If you want to take deep learning to the next level and master transfer-learning concepts, then this course is what you need. It is particularly useful for If you want to take deep learning to the next level and master transfer-learning concepts, then this course is what you need.

Enroll now: Hands-On Transfer Learning with TensorFlow 2.0

Summary

Title: Hands-On Transfer Learning with TensorFlow 2.0

Price: $54.99

Average Rating: 4.7

Number of Lectures: 13

Number of Quizzes: 4

Number of Published Lectures: 13

Number of Published Quizzes: 4

Number of Curriculum Items: 17

Number of Published Curriculum Objects: 17

Original Price: $109.99

Quality Status: approved

Status: Live

What You Will Learn

Build your own image classification application using Convolutional Neural Networks and TensorFlow 2.0

Improve any image classification system by leveraging the power of transfer learning on Convolutional Neural Networks, in only a few lines of code

Discover how users feel about IMDB movies by building a Sentiment Analysis system utilizing the power of Recurrent Neural Networks and the TensorFlow 2.0 high-level API

Learn how to perform transfer learning on Recurrent Neural Networks and powerfully improve any text-based system

Learn how to use TensorFlow Hub and TensorFlow Lite to make transfer learning much easier

Who Should Attend

If you want to take deep learning to the next level and master transfer-learning concepts, then this course is what you need.

Target Audiences

If you want to take deep learning to the next level and master transfer-learning concepts, then this course is what you need.

Transfer learning involves using a pre-trained model on a new problem. It is currently very popular in the field of Deep Learning because it enables you to train Deep Neural Networks with comparatively little data. In Transfer learning, knowledge of an already trained Machine Learning model is applied to a different but related problem.

The general idea is to use knowledge, which a model has learned from a task where a lot of labeled training data is available, in a new task where we don’t have a lot of data. Instead of starting the learning process from scratch, you start from patterns that have been learned by solving a related task.

In this course, learn how to implement transfer learning to solve a different set of machine learning problems by reusing pre-trained models to train other models. Hands-on examples with transfer learning will get you started, and allow you to master how and why it is extensively used in different deep learning domains.

You will implement practical use cases of transfer learning in CNN and RNN such as using image classifiers, text classification, sentimental analysis, and much more. You’ll be shown how to train models and how a pre-trained model is used to train similar untrained models in order to apply the transfer learning process even further. Allowing you to implement advanced use cases and learn how transfer learning is gaining momentum when it comes to solving real-world problems in deep learning.

By the end of this course, you will not only be able to build machine learning models, but have mastered transferring with tf.keras, TensorFlow Hub, and TensorFlow Lite tools.

About the Author

Margaret Maynard-Reid is a Google Developer Expert (GDE) for Machine Learning, contributor to the open-source ML framework TensorFlow and an author of the official TensorFlow blog. She writes tutorials and speaks at conferences about on-device ML, deep learning, computer vision, TensorFlow, and Android.

Margaret leads the Google Developer Group (GDG) Seattle and Seattle Data/Analytics/ML and is passionate about helping others get started with AI/ML. She has taught in the University of Washington Professional and Continuing Education program. For several years, she has been working with TensorFlow, and has contributed to the success of TensorFlow 2.0 by testing and organizing the Global Docs Sprint project.

Course Curriculum

Chapter 1: Image Classifier from Scratch with TensorFlow 2.0

Lecture 1: Course Overview

Lecture 2: TensorFlow 2.0

Lecture 3: Google Colab Basics

Lecture 4: Image Classifier with tf.keras

Chapter 2: Transfer Learning with tf.keras

Lecture 1: Transfer Learning Overview

Lecture 2: Pre-trained ConvNets

Lecture 3: Transfer Learning – Feature Extractor

Lecture 4: Transfer Learning – Fine Tuning

Chapter 3: Transfer Learning with TensorFlow Hub

Lecture 1: TensorFlow Hub Overview

Lecture 2: Image Classifier with TensorFlow Hub

Lecture 3: Text Classification with TensorFlow Hub

Chapter 4: TFLite Model Maker

Lecture 1: TensorFlow Lite Model Maker

Lecture 2: On-Device Training

Instructors

Packt Publishing
Tech Knowledge in Motion

Rating Distribution

1 stars: 0 votes

2 stars: 0 votes

3 stars: 4 votes

4 stars: 10 votes

5 stars: 19 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!