Video Analytics Using Deep Learning

Beschreibung

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 Learningcloses 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. 
You will:
• 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
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 LearnMaster TensorFlow, Keras, and YOLOWork with face recognition, age detection, and gender identificationApply CNN, RNN and generative models in deep learningUse emotion analysis and gesture detectionCarry out traffic recognition in real-timeWho This Book Is ForData scientists and machine learning developers looking to build applications based on video in finance, healthcare, automotive, transport, safety/security, and home automation.
Includes examples such as live face, age, gender, and emotion detection, and real-time object detection, fraud detection, and traffic vision for smart cars

Focuses on cutting-edge deep learning models such as  convolutional neural networks, recurrent neural networks and generative adversarial networks
Covers examples from the healthcare, finance, automotive, and retail domains

Autor*in

Debjyoti Paul

Themen in »Video Analytics Using Deep Learning«

Alexnet Convolutional Neural Network Deep Learning Generative Models GoogLenet Image Captioning Keras Recurrent neural networks TensorFlow Video Analytics

Stimmen zu »Video Analytics Using Deep Learning«

Details

Link teilen