Deep Learning for Computer Vision
- Train efficient deep learning models to solve different problems in Computer Vision with the help of this comprehensive guide
- Perform object detection, image classification and more, by combining the power of Python, Keras and Tensorflow
- Contains practical examples using real-world datasets to apply the concepts of deep learning to various computer vision algorithms
This book will not teach you what you already know - it directly jumps on to readying the environment required to train efficient deep learning models for a plethora of computer vision tasks such as object recognition, image classification and feature detection. In the process, you will leverage the power of Python, popular Deep Learning frameworks such as Keras and Tensorflow. You will implement the common architectures of deep learning such as convolutional neural networks, recurrent neural networks to work on your image data, with this book.
By the end of the book, you will be confident to develop and train your own deep learning models and use them to solve your Computer Vision problems.
What you will learn
- Setup up the environment for keras and tensorflow
- Train a pet classification problem while training the first deep learning model
- Use a pre-trained model for image retrieval problem by understanding the deeper layers of a model
- Learn about the solutions available of object detection and train a pedestrian detection to understand the nuances
- Learn about losses for similarity learning and a train a model for face recognition
- Train a model that can caption images by training image along with text
- Advance the knowledge by learning Generative Adversarial Networks and train a model that can generate images
- Explore video classification problem and relate video to images
- Learn how to deploy the trained models across platforms