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Best Deep Learning courses 2020

 

Deep Learning A-Z: Hands-On Artificial Neural Networks

Deep Learning A-Z: Hands-On Artificial Neural Networks by Kirill Eremenko and Hadelin de Ponteves will teach you Deep Learning with Artificial Neural Networks. You will work with Tensorflow and Pytorch to build several different types of Neural Networks. Learn Deep Learning from the best Deep Learning tutorial in 2020.

Data Science: Deep Learning in Python

Data Science: Deep Learning in Python by Lazy Programmer Inc. will teach you build Neural Networks from scratch in Python, numpy & TensorFlow. You will learn about the various types and terms associated to neural networks.This is one of the best Deep Learning and Python tutorials in 2020.

Best Deep Learning tutorials 2020

Natural Language Processing with Deep Learning in Python

Natural Language Processing with Deep Learning in Python by Lazy Programmer Inc. will teach you everything about deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets. You will learn how to use different types neural networks.Learn Deep Learning from the best Deep Learning course in 2020.

Zero to Deep Learning with Python and Keras

Zero to Deep Learning with Python and Keras by Jose Portilla and Francesco Mosconi will help you understand and build Deep Learning using Python and Keras. This course is a great complete introduction to Deep Learning and how to use it. This is one of the best Deep Learning and Python Keras tutorials in 2020.

Deep Learning: Convolutional Neural Networks in Python

Deep Learning: Convolutional Neural Networks in Python by Lazy Programmer Inc will help you understand Computer Vision, Data Science and Machine Learning using Theano and TensorFlow. You will learn, understand and implement convolutional neural networks.

Best Deep Learning books 2020

Deep Learning (Adaptive Computation and Machine Learning series)

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Deep Learning (Adaptive Computation and Machine Learning series)
  • The MIT Press
  • Hardcover Book
  • Goodfellow, Ian (Author)
  • English (Publication Language)
  • 800 Pages - 11/18/2016 (Publication Date) - The MIT Press (Publisher)

Deep learning is a form of machine learning that allows computers to learn from experience and understand the world in terms of the hierarchy of ideas. Since computers gather knowledge from experience, it is not necessary for any IT operator to formally specify all the knowledge required of a computer. Classification of concepts allows computers to learn complex concepts from simple concepts to building; A graph of this classification will have different levels of depth. This book presents various topics of deep learning.

The text provides the mathematical and conceptual context, covering the relevant concepts of linear algebra, probability theory and data theory, number calculation and machine learning. It describes in-depth learning strategies used by industry practitioners, including in-depth feedback networks, regularization, optimization algorithms, convincing networks, sequence modeling and practical methods; And it examines applications like natural language processing, speech recognition, computer vision, online suggestion systems, bioinformatics and video games. Finally, the book provides research perspectives on theoretical topics such as linear factor models, auto-encoders, presentation training, structural potential models, Monte Carlo methods, partition functions, etc., approximate estimates and deep productive models.

Deep learning undergraduate or graduate students plan careers in art or research and can be used by software engineers who want to start using in-depth learning in their products or platforms. A website provides additional material for readers and trainers. This is the best Deep Learning book in 2020.

Deep Learning with Python

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Deep Learning with Python
  • Chollet, François (Author)
  • English (Publication Language)
  • 384 Pages - 12/22/2017 (Publication Date) - Manning Publications (Publisher)

Deep Learning with Python introduces the field of in-depth learning using the Python language and powerful Keras library. Writes Franইois Cholett, creator of Keras and Google AI researcher, this book reinforces your understanding through intuitive explanations and practical examples.

Machine learning has made significant progress in recent years. We have moved from almost useless speech and image recognition to almost human perfection. We were left with machines that couldn’t beat a serious go-to player to beat any world champion. Behind this advancement is a combination of deep learning – engineering, best practice and the advancement of theory that enables a multitude of previously intelligent applications.

Deep Learning with Python introduces the field of in-depth learning using the Python language and powerful Keras library. Writes Franইois Cholett, creator of Keras and Google AI researcher, this book reinforces your understanding through intuitive explanations and practical examples. You will find difficult ideas and practices, including applications in computer vision, natural language processing and generator models. After you finish your work, you will have the knowledge and practical skills to apply deep learning to your own projects. You will:

The first principle is deep knowledge
Establish your own deep learning environment
Image classification model
Deep learning for text and hierarchy
Neural style transfer, text production and image generation

Readers need intermediate Python skills. No previous experience is required with Keras, Tensorflow or Machine Learning.

Deep Learning (MIT Press Essential Knowledge series)

Deep Learning (MIT Press Essential Knowledge series)
  • Kelleher, John D. (Author)
  • English (Publication Language)
  • 296 Pages - 09/10/2019 (Publication Date) - The MIT Press (Publisher)

Artificial intelligence is an accessible role of technology that enables computer vision, voice recognition, machine translation and driverless vehicles. Deep learning is an artificial intelligence technology that enables computer vision, voice recognition on mobile phones, machine translation, AI games, driverless cars and other applications. When we use consumer products from Google, Microsoft, Facebook, Apple or Baidu, we often approach deep learning approaches. In this volume of the MIT Press Necessary Knowledge series, computer scientist John Keleh offers an accessible and concise but comprehensive introduction to basic technology at the center of the artificial intelligence revolution.

Keleher explains that deep learning enables data-driven decisions by identifying and extracting models from large data sets; Learning skills from complex data makes deep learning ideal for taking advantage of big data and the rapid growth of computing power. Keleh also explained some of the basic concepts of deep learning, presented a history of progress in the field and discussed the current state of the industry. It describes recent developments and most important deep learning architectures such as auto-encoders, recurrent neural networks and short-term long-term networks as well as anti-generator networks and capsules. It also provides a complete (and understandable) introduction to two basic algorithms of deep learning: gradient descent and backprogression. Finally, Keleh envisioned the future of deep learning – big trends, potential developments and big challenges.

Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence (Addison-Wesley Data & Analytics Series)

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Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence (Addison-Wesley Data & Analytics Series)
  • Krohn, Jon (Author)
  • English (Publication Language)
  • 416 Pages - 09/28/2019 (Publication Date) - Addison-Wesley Professional (Publisher)

Transforms deep learning software, supports new powerful artificial intelligence capabilities and generates unprecedented algorithm performance. Deep Learning Illustrated is particularly intuitive and provides a complete introduction to disciplinary techniques. Equipped with colorful statistics and easy-to-follow code, it removes the complexity of creating deep learning models, making the subject accessible and fun.

World-class instructor and practitioner Jon Crohan – with the spectacular content of Grant Bieleveld and the beautiful illustrations of Agla Bassens – offers simple analogies to explain what deep study is, why it is so popular and how it relates to other machine learning methods. Crohn has created a practical reference and tutorial for developers, information scientists, researchers, analysts and those who want to start applying it. This clarifies the theory with practical Python code in notebooks with Jupiter. To help you make rapid progress, he focuses on the versatile Keras Deep Learning Library to effectively delay tensorflow models; The main alternative library is also covered by Pieterch.

You will be able to gain a practical understanding of all the basic deep learning methods and their uses, from machine vision and natural language processing to image generation and game algorithms.

Find out what makes deep learning systems unique and their effects for physicians
Explore new tools that make it easier to create, use and improve deep learning models
Master the Necessary Theory: Artificial Neurons, Training, Optimization, Convolutionary Networks, Repetitive Networks, Generator Advertising Networks (GN), Deep Rehabilitation Learning, and more
Find interactive deep learning applications and move forward with your own artificial intelligence projects.

Deep Learning from Scratch: Building with Python from First Principles

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Deep Learning from Scratch: Building with Python from First Principles
  • Weidman, Seth (Author)
  • English (Publication Language)
  • 252 Pages - 09/24/2019 (Publication Date) - O'Reilly Media (Publisher)

With the resurgence of neural networks in the 2010s, in-depth learning became essential for machine learning practitioners and even for many software engineers. This book provides a wide range of role for data scientists and software engineers with experience in machine learning. You will start with the basics of deep learning and quickly move on to the details of important advanced architecture by implementing everything from the beginning.

Author Seth Wedman shows you how neural networks work with the first principles approach. You will learn how to apply Multilayer Neural Networks, Conversational Neural Networks, and Recursive Neural Networks from scratch. With a deep understanding of how neural networks work on a mathematical, computer, and conceptual level, you will be ready to succeed in all future in-depth learning projects.

This book provides:

To understand neural networks – including examples of work codes and mathematical explanations – very clear and in-depth psychological models
Methods for implementing multilayer neural networks from scratch using simple to understand object oriented frameworks
Work implementation and clear explanation of corrective and repetitive neural networks
Application of these neural network concepts using the popular Pieterch framework

Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition

Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition
  • Gulli, Antonio (Author)
  • English (Publication Language)
  • 646 Pages - 12/27/2019 (Publication Date) - Packt Publishing (Publisher)