Deep Learning with Applications Using Python: Chatbots and Face, Object, and Speech Recognition with TensorFlow and Keras

  • 1h 28m
  • Navin Kumar Manaswi
  • Apress
  • 2018

Explore deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning.

This book covers convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn.

What You Will Learn

  • Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn.
  • Use face recognition and face detection capabilities
  • Create speech-to-text and text-to-speech functionality
  • Engage with chatbots using deep learning

Who This Book Is For

Data scientists and developers who want to adapt and build deep learning applications.

About the Author

Navin K Manaswi has been developing AI solutions/products with the use of cutting edge technologies and sciences related to artificial intelligence for many years. Having worked for consulting companies in Malaysia, Singapore and the Dubai Smart City project, he has developed a rare skill of delivering end-to-end data science solutions. He has been building solutions for video intelligence, document intelligence and human-like chatbots in his own company.

In this Book

  • Basics of TensorFlow
  • Understanding and Working with Keras
  • Multilayer Perceptron
  • Regression to MLP in TensorFlow
  • Regression to MLP in Keras
  • Convolutional Neural Networks
  • CNN in TensorFlow
  • CNN in Keras
  • RNN and LSTM
  • Speech to Text and Vice Versa
  • Developing Chatbots
  • Face Detection and Recognition
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