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Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.
Relying on years of industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use.
Whether you're a software engineer aspiring to enter the world of deep learning, a veteran data scientist, or a hobbyist with a simple dream of making the next viral AI app, you might have wondered where to begin. This step-by-step guide teaches you how to build practical deep learning applications
Discusses Open AI and Open AI Gym with relevance to Reinforcement LearningApplication of TensorFlow and Keras to Reinforcement Learning Swarm Intelligence with Python in terms of Reinforcement LearningDiscusses Google's DeepMind and the future of Reinforcement Learning
Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze
Chapter 1- Perceptrons- Introduction to Perceptrons- Working of a Perceptron- Program to understand the working of a PerceptronChapter 2: Neural Networks- Introduction to Neural Networks- Types of Neural Networks- How each neural network works- Program to understand the working of Neural