Video Analytics Using Deep Learning: Building Applications with Tensorflow, Keras, and Yolo
Paul, Debjyoti, Puvvala, Charan
Build analytics for video using TensorFlow, Keras, and YOLO. This book guides you through the field of deep learning starting with neural networks, taking a deep dive into convolutional neural networks, recurrent neural networks, and long short-term memory (LSTM) networks. Video Analytics Using Deep Learning closes with practical examples of building image filters and video masking using generative models.
The examples within the book cover topics from domains such as traffic recognition for self-driving cars; face recognition and emotion analysis for retail analytics; object and tamper detection for safety and security; and image filters and video masking for social networks and web applications. To enable you to make a smooth transition into deep learning, the book covers mathematical pre-requisites and includes an introduction to deep learning. You'll also cover topics such as storage of large video content for processing on the cloud and working with the connectors involved. All the code and samples in the book are provided as iPython.
What You Will Learn
- Master TensorFlow, Keras, and YOLO
- Work with face recognition, age detection, and gender identification
- Apply CNN, RNN and generative models in deep learning
- Use emotion analysis and gesture detection
- Carry out traffic recognition in real-time
Who This Book Is For
Data scientists and machine learning developers looking to build applications based on video in finance, healthcare, automotive, transport, safety/security, and home automation.
Charan Puvvala is a team builder and is technology agnostic. He is a speaker and lead for Hyderabad Machine Learning Group. Currently, he's working on problems related to machine learning and deep learning, natural language understanding, conversational bots, video analytics, big data, data pipelines, and analytics. He has trained over 400 data engineers, data scientists, and software developers in corporate skill upgrade programs. His core competencies are deep learning/artificial neural networks, supervised learning, unsupervised learning, enterprise search architecture, large scale crawling, and natural language processing.