Explore the potential of deep learning techniques in computer vision applications using Python ecosystemAbout This Book* Grasp the fundamental concepts using deep learning to perform image processing and computer vision tasks. * Develop your skills by in-depth understanding and work with python libraries such as NumPy, Matplotlib, TensorFlow and Keras to build neural network models to carry out computer vision tasks. * Covers multiple facets of deep learning using the easy-to-follow practical approach mentioned. Who This Book Is ForThis book is for machine learning developers, deep learning researchers and AI enthusiasts who want to build efficient neural network models to perform efficient image processing tasks.
If you want to build powerful computer vision applications using OpenCV and Python frameworks, this book is what you need!
Knowledge of Python programming language and working knowledge of machine learning concepts is expected to get the most out of the book. What You Will Learn* Explore machine learning and deep learning landscape for computer vision and image processing applications. * Develop an understanding of deep learning based algorithms to build deep neural network models. * Explore building, training, and validating of various models to carry out tasks such as Image recognition, Image tagging, Object Detection, tracking and Segmentation using Python, OpenCV, Tensorflow, and Keras. * Learn techniques for training and scaling deep neural networks such as CNN, RNN and Capsule networks. * Understand popular research domains such as reinforcement learning and GANs to implement deep learning principles in next-generation computer vision applications. * Deploy the trained model applications in production ready environmentIn DetailDeep Learning is the buzzword and the emerging field in the world of IT bringing machine learning and artificial intelligence to reality.
Deep Learning with Python and OpenCV will bring the flavor of deep learning in computer vision and image processing applications explaining the required concepts such as back-propagation, perceptrons, and neural networks to build a foundation with the practical approach mentioned. The book will first introduce you to the concept of Deep Learning and its trends and applications in computer vision and image processing.
You will learn to implement supervised, unsupervised and reinforcement learning algorithms using OpenCV and Python frameworks such as TensorFlow and Keras with real-world examples.
The book will then teach you how to create your first Deep Neural Network and also explore different Optimization techniques like AdaGrad, RMSProp, Adam and their impact on the performance of the neural network.
Later you will delve into different types of neural networks such as CNN and RNN with easy-to-follow code.
You will be introduced to reinforcement learning and will learn to develop projects with OpenAI gym.
The book will then teach you how Capsule networks work and how are they superior to CNN.
The book will then explain a completely different side of deep learning which is Generative Adversarial Networks and how they are used in building powerful computer vision applications. By the end of this book, you will have all the required knowledge to cover intermediate-to-expert level image processing tasks.
You will be able to deploy the trained models for the production-ready environment.
