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Hands-On Transfer Learning with Python

You're reading from   Hands-On Transfer Learning with Python Implement advanced deep learning and neural network models using TensorFlow and Keras

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781788831307
Length 438 pages
Edition 1st Edition
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Authors (4):
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Nitin Panwar Nitin Panwar
Author Profile Icon Nitin Panwar
Nitin Panwar
Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
Tamoghna Ghosh Tamoghna Ghosh
Author Profile Icon Tamoghna Ghosh
Tamoghna Ghosh
Dipanjan Sarkar Dipanjan Sarkar
Author Profile Icon Dipanjan Sarkar
Dipanjan Sarkar
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Toc

Table of Contents (14) Chapters Close

Preface 1. Machine Learning Fundamentals FREE CHAPTER 2. Deep Learning Essentials 3. Understanding Deep Learning Architectures 4. Transfer Learning Fundamentals 5. Unleashing the Power of Transfer Learning 6. Image Recognition and Classification 7. Text Document Categorization 8. Audio Event Identification and Classification 9. DeepDream 10. Style Transfer 11. Automated Image Caption Generator 12. Image Colorization 13. Other Books You May Enjoy

Image Recognition and Classification

An investment in knowledge always pays the best interest.
– Benjamin Franklin

Image recognition is an active interdisciplinary field of study under the umbrella of computer vision. Image or object recognition, as the name suggests, is the task of identifying objects in an image or video sequence. Traditionally, this field has leveraged advancements in mathematical and computer-aided modeling, and the design of objects. Several hand-annotated datasets have been developed over the years to test and evaluate image recognition systems. Traditional techniques, as we now call them, were dominating the scene and iteratively improving upon the task until recently. In 2012, deep learning arrived at the ImageNet competition and opened the floodgates for rapid improvements and advancements in computer vision and deep learning techniques.

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