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Deep Learning By Example

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  • Understand the fundamentals of deep learning and how it is different from machine learning
  • Get familiarized with Tensorflow, one of the most popular libraries for advanced machine learning
  • Increase the predictive power of your model using feature engineering
  • Understand the basics of deep learning by solving a digit classification problem of MNIST
  • Demonstrate face generation based on the CelebA database, a promising application of generative models
  • Apply deep learning to other domains like language modeling, sentiment analysis, and machine translation

Deep learning is a popular subset of machine learning, and it allows you to build complex models that are faster and give more accurate predictions. This book is your companion to take your first steps into the world of deep learning, with hands-on examples to boost your understanding of the topic.

This book starts with a quick overview of the essential concepts of data science and machine learning which are required to get started with deep learning. It introduces you to Tensorflow, the most widely used machine learning library for training deep learning models. You will then work on your first deep learning problem by training a deep feed-forward neural network for digit classification, and move on to tackle other real-world problems in computer vision, language processing, sentiment analysis, and more. Advanced deep learning models such as generative adversarial networks and their applications are also covered in this book.

By the end of this book, you will have a solid understanding of all the essential concepts in deep learning. With the help of the examples and code provided in this book, you will be equipped to train your own deep learning models with more confidence.

  • Get a first-hand experience of the deep learning concepts and techniques with this easy-to-follow guide
  • Train different types of neural networks using Tensorflow for real-world problems in language processing, computer vision, transfer learning, and more
  • Designed for those who believe in the concept of 'learn by doing', this book is a perfect blend of theory and code examples
Page Count 450
Course Length 13 hours 30 minutes
Date Of Publication 28 Feb 2018
Capacity of a single neuron
Activation functions
Feed-forward neural network
The need for multilayer networks
TensorFlow terminologies – recap
Linear regression model – building and training
Logistic regression model – building and training
Hidden units and architecture design
MNIST dataset analysis
Digit classification – model building and training
The convolution operation
Different layers of CNNs
CNN basic example – MNIST digit classification
Object detection
CIFAR-10 – modeling, building, and training
Transfer learning
CIFAR-10 object detection – revisited
General sentiment analysis architecture
Sentiment analysis – model implementation


Ahmed Menshawy

Ahmed Menshawy is a Research Engineer at the Trinity College Dublin, Ireland. He has more than 5 years of working experience in the area of ML and NLP. He holds an MSc in Advanced Computer Science. He started his Career as a Teaching Assistant at the Department of Computer Science, Helwan University, Cairo, Egypt. He taught several advanced ML and NLP courses such as ML, Image Processing, and so on. He was involved in implementing the state-of-the-art system for Arabic Text to Speech. He was the main ML specialist at the Industrial research and development lab at IST Networks, based in Egypt.