Python Deep Learning: Exploring deep learning techniques, neural network architectures and GANs with PyTorch, Keras and TensorFlow 2nd Edition Pdf is written by Ivan Vasilev, Daniel Slater, Gianmario Spacagna, Peter Roelants, Valentino Zocca and you can download for free in pdf format. Exploring an innovative state of the art deep learning models and its software using Popular python libraries such as Keras, Tensorflow, and Pytorch. Key comes with a powerful foundation on neural networks and deep learning with Python libraries. Explore advanced deep learning techniques and their software across computer vision and NLP. Discover how a computer may browse in complex environments with reinforcement learning.With the surge of Artificial Intelligence in each application catering to both business and consumer needs, Deep Learning becomes the prime requirement of now and future market demands. This publication explores deep learning and builds a strong deep learning mindset so as to place them to use in their smart artificial intelligence jobs.
This second edition builds strong grounds of deep learning, profound neural networks and how to train them with high performance algorithms and popular python frameworks. You will uncover different neural networks architectures like convolutional networks, recurrent networks, long short term memory (LSTM) and solve issues across image recognition, natural language processing, and time-series prediction. You will also research the newly evolved area of reinforcement learning and it will help you to comprehend the state-of-the-art algorithms which are the main engines behind popular game Go, Atari, and Dota. From the end of the book, you will be well versed with sensible deep learning knowledge and its own real-world applications
What you will learn
• Grasp mathematical concept behind neural networks and deep learning procedure.
• Investigate and resolve computer vision difficulties using convolutional networks and tablet computer networks.
• Explore Reinforcement Learning and comprehend how agents behave in a intricate environment.
Who This Book Is For: This book is for Data Science professionals, Machine Learning Engineers and Deep learning aspirants who have a basic foundation of Machine Learning concepts and some programming experience with Python. A mathematical background with a conceptual Comprehension of calculus and statistics can also be desired
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