Packt+ | Advance your knowledge in tech

You're reading from TensorFlow Machine Learning Projects Build 13 real-world projects with advanced numerical computations using the Python ecosystem

Product type Paperback

Published in Nov 2018

Publisher Packt

ISBN-13 9781789132212

Length 322 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Authors (2):

Ankit Jain

Dr. Amita Kapoor

View More author details

Table of Contents (17) Chapters

Preface

1. Overview of TensorFlow and Machine Learning FREE CHAPTER

2. Using Machine Learning to Detect Exoplanets in Outer Space

3. Sentiment Analysis in Your Browser Using TensorFlow.js

4. Digit Classification Using TensorFlow Lite

5. Speech to Text and Topic Extraction Using NLP

6. Predicting Stock Prices using Gaussian Process Regression

7. Credit Card Fraud Detection using Autoencoders

8. Generating Uncertainty in Traffic Signs Classifier Using Bayesian Neural Networks

9. Generating Matching Shoe Bags from Shoe Images Using DiscoGANs

10. Classifying Clothing Images using Capsule Networks

11. Making Quality Product Recommendations Using TensorFlow

12. Object Detection at a Large Scale with TensorFlow

13. Generating Book Scripts Using LSTMs

14. Playing Pacman Using Deep Reinforcement Learning

15. What is Next?

16. Other Books You May Enjoy

Leave a review - let other readers know what you think

Object detection using TensorFlowOnSpark and Sparkdl

Apache Spark has a higher level API Sparkdl for scalable deep learning in Python. In this section, we'll use the Sparkdl API. In this section, you will learn how to build a model over the pre-trained Inception v3 model to detect cars and buses. This technique of using a pre-trained model is called transfer learning.

Transfer learning

Learning in humans is a continuous process—whatever we learn today is built upon the learning we have had in the past. For example, if you know how to drive a bicycle, you can extend the same knowledge to drive a motorcycle, or drive a car. The driving rule remains the same—the only thing that changes is the control panel and actuators. However, in deep learning, we often start afresh. Is it possible to use the knowledge the model has gained in solving a problem in one domain, to solve the problem in another related domain?

Yes, it's indeed possible, and it's called transfer learning. Though a lot of research...