3 Review(s)

The book, to the best of the author’s knowledge, is the first text of its kind that presents both the traditional and the modern aspects of “AI, Artificial Neural Networks, Pytorch, Reinforcement Learning, and Deep Learning” in a clear, insightful, and highly comprehensive writing style. It provides an in-depth analysis of the mathematical models, and algorithms and demonstrates their applications in real-world problems of significant complexity. With the reinvigoration of Neural Networks in the 2000s, Deep Learning has become an extremely active area of research that is paving the way for modern Machine Learning. This book uses exposition and examples to help you understand major concepts in this complicated field. Large companies such as Microsoft, Google, and Facebook have taken notice and are actively growing in-house deep learning teams. Deep Learning is still a pretty complex and difficult subject to grasp. By the end of the book, we hope that our readers will be left with an intuition for how to approach problems using deep learning, the historical context for modern deep learning approaches, and a familiarity with implementing deep learning algorithms using the Tensor Flow open-source library Research papers are filled to the online tutorials do little to help build a strong intuition for why and how deep learning practitioners approach problems. Our goal is to bridge this gap.