Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Hands-On Computer Vision with TensorFlow 2
  • Toc
  • feedback
Hands-On Computer Vision with TensorFlow 2

Hands-On Computer Vision with TensorFlow 2

By : Benjamin Planche, Eliot Andres
3.3 (12)
close
Hands-On Computer Vision with TensorFlow 2

Hands-On Computer Vision with TensorFlow 2

3.3 (12)
By: Benjamin Planche, Eliot Andres

Overview of this book

Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. This book will help you explore TensorFlow 2, the brand new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. Hands-On Computer Vision with TensorFlow 2 starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the book demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. By the end of the book, you will have both the theoretical understanding and practical skills to solve advanced computer vision problems with TensorFlow 2.0.
Table of Contents (16 chapters)
close
Free Chapter
1
Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision
2
Computer Vision and Neural Networks
5
Section 2: State-of-the-Art Solutions for Classic Recognition Problems
6
Influential Classification Tools
chevron up
9
Section 3: Advanced Concepts and New Frontiers of Computer Vision
10
Training on Complex and Scarce Datasets

Human inspiration

Like many developments in machine learning, transfer learning is inspired by our own human way of tackling complex tasks and gathering knowledge.

As mentioned in the introduction of this section, the first inspiration is our ability as a species to transfer knowledge from one individual to another. Experts can efficiently transfer the precious knowledge they have gathered over the years to a large number of students through oral or written teaching. By harnessing the knowledge that has been accumulated and distilled generation after generation, human civilizations have been able to continuously refine and extend their technical abilities. Phenomena that took millennia for our ancestors to understand— such as human biology, the solar system, and more—became common knowledge.

Furthermore, as individuals, we also have the ability to transfer some expertise from one task to another. For example, people mastering one foreign language have an easier time...

bookmark search playlist download font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete