Learn Deep Learning 2021 – Best Deep Learning courses & Best Deep Learning books

Best Deep Learning Courses 2021

 

Best Deep Learning Books 2021

 

Best Deep Learning tutorials 2021

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

As you can see there are many different tools in the Deep Learning space and in this course we make sure to show you the most important and progressive ones so that when you are done with Deep Learning AZ ™ your skills are activated the cutting edge of today’s technology. If you are just new to Deep Learning, you will find this course extremely useful. Deep Learning A-Z ™ is structured around special coding plan approaches, which means you won’t get bogged down in unnecessary programming or mathematical complexities and will apply Deep Learning techniques from the start of the course. You will develop your knowledge from scratch and you will see how with each tutorial you become more and more confident.

If you have any previous experience with Deep Learning, you will find this course refreshing, inspiring and very practical. Inside Deep Learning AZ ™ you will master some of the most advanced Deep Learning algorithms and techniques (some of which didn’t even exist a year ago) and through this course you will gain immense hands-on experience with real world business challenges. Plus, inside you’ll find inspiration to explore new deep learning skills and applications. In this course, we will work on real world data sets, to solve real business problems. (Certainly not the boring iris or the number classification data sets we see in every course). In this course, we’ll solve six real-world challenges:

Artificial neural networks to solve a customer churn problem
Convolutional neural networks for image recognition
Recurrent neural networks to predict stock prices
Self-organized cards to investigate fraud
Boltzmann machines to create a recommendation system
Auto-Encoders Stacked * To Meet The Million Dollar Netflix Prize Challenge

You will learn:

Understanding the intuition behind artificial neural networks
Applying artificial neural networks in practice
Understanding the intuition behind convolutional neural networks
Applying convolutional neural networks in practice
Understanding the intuition behind recurrent neural networks
Applying recurrent neural networks in practice
Understand the intuition behind self-organizing maps
Applying self-organized maps in practice
Understanding the intuition behind Boltzmann machines
Applying Boltzmann machines in practice
Understand the intuition behind AutoEncoders
Apply AutoEncoders in practice

Tensorflow and Pytorch are the two most popular open source libraries for Deep Learning. In this course, you will learn both! TensorFlow was developed by Google and is used in their speech recognition system, in the new product google photos, gmail, google search and much more. Companies using Tensorflow include AirBnb, Airbus, Ebay, Intel, Uber, and dozens more. PyTorch is just as powerful and is developed by researchers from Nvidia and major universities: Stanford, Oxford, ParisTech. Companies using PyTorch include Twitter, Saleforce, and Facebook.

So what is better and for what?

Well, in this course you will have the opportunity to work with both and understand when Tensorflow is best and when PyTorch is the way to go. Throughout the tutorials, we compare the two and give you tips and ideas on which ones might work best under certain circumstances. The interesting thing is that these two libraries are barely over a year old. This is what we mean when we say that in this course we teach you the most advanced deep learning graphical models and techniques.

Complete Guide to TensorFlow for Deep Learning with Python

This course will walk you through using Google’s TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google’s TensorFlow framework in an easy to understand way. Other courses and tutorials tend to stay away from the flow of pure tension and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning! This course is designed to balance theory and practical implementation, with comprehensive jupyter notebook code guides and easy-to-navigate slides and notes. We also have plenty of exercises to test your new skills along the way! This course covers a variety of advanced topics, including

Neural Network Basics
TensorFlow Basics
Artificial neural networks
Densely connected networks
Convolutional neural networks
Recurrent neural networks
Automatic encoders
Reinforcement learning
OpenAI Gymnasium
and much more!
There are many deep learning frameworks out there, so why use TensorFlow?

TensorFlow is an open source software library for numerical computation using data flow graphs. The nodes of the graph represent mathematical operations, while the edges of the graph represent the arrays of multidimensional data (tensors) communicated between them. The flexible architecture allows you to deploy compute to one or more processors or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working as part of the Google Brain team within Google’s Machine Intelligence research organization with the goal of conducting research on machine learning and networking. of deep neurons, but the system is general enough to be applicable in a wide variety of other fields as well.

It is used by large companies around the world including Airbnb, Ebay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, Intel and of course Google!

You will learn

Understand how neural networks work
Create your own neural network from scratch with Python
Use TensorFlow for classification and regression tasks
Use TensorFlow for image classification with convolutional neural networks
Use TensorFlow for time series analysis with recurrent neural networks
Use TensorFlow to Solve Unsupervised Learning Problems with Auto Encoders
Learn how to conduct reinforcement learning with OpenAI Gym
Create generative conflicting networks with TensorFlow
Become a Deep Learning Guru!

Data Science: Deep Learning in Python

This course will get you started building your FIRST artificial neural network using deep learning techniques. Continuing from my previous course on logistic regression, we take this basic building block and build complete nonlinear neural networks right out of the gate using Python and Numpy. All material for this course is FREE.

We extend the previous binary classification model to several classes using the softmax function, and we derive the very important training method called “backpropagation” using first principles. I show you how to code backpropagation in Numpy, first “the slow way”, then “the fast way” using the features of Numpy.

Next, we implement a neural network using Google’s new TensorFlow library.

You should take this course if you want to start your journey to becoming a master of deep learning, or if you are interested in machine learning and data science in general. We go beyond basic models like logistic regression and linear regression and show you something that automatically learns functionality.

This course provides you with lots of practical examples so you can really see how deep learning can be used on anything. Throughout the course we will be doing a course project, which will show you how to predict user actions on a website based on user data, such as whether or not that user is on a mobile device, the number of products that ‘they viewed, how long they stayed on your site, whether or not they were a repeat visitor, and what time of day they visited.

You will learn

Learn how Deep Learning REALLY works (not just a few diagrams and magic black box code)
Find out how a neural network is built from basic building blocks (the neuron)
Code a neural network from scratch in Python and numpy
Code a neural network using Google’s TensorFlow
Describe the different types of neural networks and the different types of problems for which they are used
Derive the backpropagation rule from first principles
Create a neural network with an output that has K> 2 classes using softmax
Describe the various terms related to neural networks, such as “activation”, “backpropagation” and “feedforward”
Install TensorFlow

Best Deep Learning books 2021

Deep Learning (Adaptive Computation and Machine Learning series)


Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville offers mathematical and conceptual training, covering relevant deep learning concepts in linear algebra, probability theory and information theory, numerical calculus, and machine learning. Describes deep learning techniques used by industry professionals, including deep feedback networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and studies applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and video games. Finally, the book offers research perspectives, covering theoretical topics such as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. This is the best Deep Learning book in 2021.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems


Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron helps you gain an intuitive understanding of smart systems building concepts and tools. You will learn a variety of deep learning technique, starting with a simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what he has learned, all you need is a programming experience to get started.

Explore the machine learning landscape, especially neural networks
Use Scikit-Learn to follow a sample machine learning project from start to finish
Explore multiple training models, including support vector machines, decision trees, random forests, and ensemble methods
Use the TensorFlow library to build and train neural networks
Immerse yourself in neural network architectures, including convolutional networks, recurring networks, and deep reinforcement learning
Learn techniques for training and scaling deep neural networks

Neural Networks and Deep Learning: A Textbook


Neural Networks and Deep Learning by Charu C. Aggarwal covers classic and modern deep learning models. The main focus is deep learning theory and algorithms. Neural network theory and algorithms are particularly important for understanding important fundamental concepts in order to understand the important design concepts of neural architectures in different applications. Why do neural networks work? When do they work better than machine learning business models? When is depth useful? Why is it so difficult to train neural networks? What are the pitfalls? The book is also rich in discussions of different applications in order to give the practitioner an idea of ​​how neural architectures are designed for different types of problems. Applications associated with many different fields are covered, such as recommender systems, machine translation, image captions, image classification, reinforcement learning-based games, and text analysis.

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition by Sebastian Raschka and Vahid Mirjalili is a comprehensive guide to machine learning and deep learning with Python. It acts as a step-by-step tutorial and a reference you will come back to when building your machine learning systems. Packed with clear explanations, visualizations, and practical examples, the book covers all the essential machine learning techniques in depth. While some books only teach you to follow directions, with this machine learning book, Raschka and Mirjalili teach the principles of machine learning, allowing you to create models and applications yourself.

Updated for TensorFlow 2.0, this new 3rd edition introduces readers to its new features in the Keras API, as well as the latest additions to scikit-learn. It has also been expanded to cover advanced reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, which helps you learn to use machine learning algorithms to classify documents. You will be:

Mastering the frameworks, models, and techniques that allow machines to “ learn ” from data
Use scikit-learn for machine learning and TensorFlow for deep learning
Apply machine learning to image classification, sentiment analysis, smart web apps, and more.
Create and train neural networks, GAN, and other models.
Discover best practices for evaluating and fitting models
Predict ongoing target outcomes using regression analysis
Dig deep into text and social data using sentiment analysis

Deep Learning with Python


Deep Learning with Python by François Chollet introduces the field of deep learning using the Python language and the powerful Keras library. Written by François Chollet, creator of Keras and Google’s artificial intelligence researcher, this book reinforces understanding of it with intuitive explanations and practical examples. You will explore challenging concepts and practices with applications of computer vision, natural language processing, and generative modeling. When you are done, you will have the knowledge and practical skills to apply deep learning in your own projects. The book contains:

Learn deeply from first principles
Set up your own deep learning environment
Image classification models
Deep learning for text and sequences
Neural style transfer, text generation, and image generation

Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more, 2nd Edition


Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more by Maxim Lapan is an updated and expanded version of the successful guide to the latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the basics of LR, as well as the hands-on ability to code smart learning agents to perform a variety of practical tasks. With six new chapters dedicated to a variety of next-generation RL developments, including discrete optimization (Rubik’s cube resolution), multi-agent methods, Microsoft’s TextWorld environment, advanced exploration techniques, and more, you’ll come out of this. book with a deep understanding of the latest innovations in this emerging field.

In addition, he will gain practical knowledge in areas such as deep Q networks, policy gradient methods, continuous control problems, and highly scalable non-gradient methods. He will also learn how to build a real RL-trained hardware robot for less than $ 100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization. You will learn:

Understand the deep learning context of RL and implement complex deep learning models
Evaluate RL methods including cross entropy, DQN, actor-critical, TRPO, PPO, DDPG, D4PG, and others
Build a hands-on hardware robot trained in RL methods for less than $ 100
Discover Microsoft’s TextWorld environment, an interactive fictional gaming platform
Use discrete optimization in RL to solve a Rubik’s cube
Teach your agent how to play Connect 4 with AlphaGo Zero
Explore the latest in-depth research from RL on topics like AI chatbots
Discover advanced exploration techniques, including noisy networks and network distillation techniques

Deep Learning (MIT Press Essential Knowledge series)

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Deep Learning (The 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.

John D. Kelleher 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...
  • Krohn, Jon (Author)
  • English (Publication Language)
  • 416 Pages - 09/18/2019 (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. 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 - 10/01/2019 (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. 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