Best Deep Learning Courses 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 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 topics, including
Neural Network Basics
Artificial neural networks
Densely connected networks
Convolutional neural networks
Recurrent neural networks
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”
Best Deep Learning books 2021
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. This is the best Deep Learning book in 2021.
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. 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
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)
- 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
- 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. 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