Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying The Deep Learning Workshop
  • Table Of Contents Toc
The Deep Learning Workshop

The Deep Learning Workshop

By : Mirza Rahim Baig , Thomas Joseph, Nipun Sadvilkar , Mohan Kumar Silaparasetty , Anthony So , Akshay Chauhan, Nagendra Nagaraj, Robert Ridley
4.5 (4)
close
close
The Deep Learning Workshop

The Deep Learning Workshop

4.5 (4)
By: Mirza Rahim Baig , Thomas Joseph, Nipun Sadvilkar , Mohan Kumar Silaparasetty , Anthony So , Akshay Chauhan, Nagendra Nagaraj, Robert Ridley

Overview of this book

Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You’ll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you’ll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you’ll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you’ll have learned the skills essential for building deep learning models with TensorFlow and Keras.
Table of Contents (9 chapters)
close
close
Preface

Image Processing

Now that we know how a digital image is represented, let's discuss how computers can use this information to find patterns that will be used to classify an image or localize objects. So, in order to get any useful or actionable information from an image, a computer has to resolve an image into a recognizable or known pattern. As for any machine learning algorithm, computer vision needs some features in order to learn patterns.

Unlike structured data, where each feature is well defined in advance and stored in separate columns, images don't follow any specific pattern. It is impossible to say, for instance, that the third line will always contain the eye of an animal or that the bottom left corner will always represent a red, round-shaped object. Images can be of anything and don't follow any structure. This is why they are considered to be unstructured data.

However, images do contain features. They contain different shapes (lines, circles, rectangles...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
The Deep Learning Workshop
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon