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Hands-On Meta Learning with Python: Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow: Sudharsan Ravichandiran: 9781789534207: Amazon.com: Books

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Sudharsan Ravichandiran is a data scientist, researcher, artificial intelligence enthusiast, and YouTuber (search for Sudharsan reinforcement learning). He completed his bachelors in information technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, which includes natural language processing and computer vision. He is an open source contributor and loves answering questions on Stack Overflow. He also authored a best seller, Hands-On Reinforcement Learning with Python, published by Packt Publishing.


Embarking on a Python journey? Then 'Hands-on Machine Learning' is a must read

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Writing an all-encompassing book on Python machine learning is difficult, given how expansive the field is. But reviewing one is not an easy feat either, especially when it's a highly acclaimed title such as Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 2nd Edition. The book is a best-seller on Amazon, and the author, Aurélien Géron, is arguably one of the most talented writers on Python machine learning. And after reading Hands-on Machine Learning, I must say that Geron does not disappoint, and the second edition is an excellent resource for Python machine learning. Geron has managed to cover more topics than you'll find in most other general books on Python machine learning, including a comprehensive section on deep learning.


9 Books on Generative Adversarial Networks (GANs)

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Generative Adversarial Networks, or GANs for short, were first described in the 2014 paper by Ian Goodfellow, et al. titled "Generative Adversarial Networks." Since then, GANs have seen a lot of attention given that they are perhaps one of the most effective techniques for generating large, high-quality synthetic images. As such, a number of books have been written about GANs, mostly focusing on how to develop and use the models in practice. In this post, you will discover books written on Generative Adversarial Networks. Discover how to develop DCGANs, conditional GANs, Pix2Pix, CycleGANs, and more with Keras in my new GANs book, with 29 step-by-step tutorials and full source code.


Python: Artificial Intelligence with Python: 3-in-1

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Artificial Intelligence is one of the hottest fields in computer science right now and has taken the world by storm as a major field of research and development. Python has surfaced as a dominant language in AI/ML programming because of its simplicity and flexibility, as well as its great support for open source libraries such as Scikit-learn, Keras, spaCy and TensorFlow. This comprehensive 3-in-1 course is designed to teach you the fundamentals of Deep Learning and use them to build intelligent systems. You'll solve real-world problems such as face detection, handwriting recognition, and more. You'll get an exposure to hands-on projects that simplify your first steps in the world of Artificial Intelligence with Python.


A Complete Guide on TensorFlow 2.0 using Keras API

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Online Courses Udemy - A Complete Guide on TensorFlow 2.0 using Keras API, Build Amazing Applications of Deep Learning and Artificial Intelligence in TensorFlow 2.0 Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team, Luka Anicin English [Auto] Students also bought Machine Learning A-Z: Hands-On Python & R In Data Science Data Analysis & Visualization Bootcamp - 2020 BESTSELLER R Programming A-Z: R For Data Science With Real Exercises! Practical Machine Learning by Example in Python Python for Statistical Analysis Preview this course GET COUPON CODE Description Welcome to Tensorflow 2.0! TensorFlow 2.0 has just been released, and it introduced many features that simplify the model development and maintenance processes. From the educational side, it boosts people's understanding by simplifying many complex concepts. From the industry point of view, models are much easier to understand, maintain, and develop.