Toggle navigation. Help Preferences Sign up Log in. View by Category Toggle navigation. Products Sold on our sister site CrystalGraphics. Title: Deep Learning. Tags: deep learning tron. Latest Highest Rated. Can we find learning methods that scale? Can we find learning methods that solve really complex problems end-to-end, such as vision, natural language, speech?
How can we learn the structure of the world? How can we learn internal representations that capture the relevant information and eliminates irrelevant variabilities? How can a human or a machine learn internal representations by just looking at the world? Example Support Vector Machines require a good input representation, and a good kernel function. The next frontier is to learn the features The question how can a machine learn good internal representations In language, good representations are paramount.
What makes the words cat and dog semantically similar? How can different sentences with the same meaning be mapped to the same internal representation? How can we leverage unlabeled data which is plentiful? From the image of an airplane, how do we extract a representation that is invariant to pose, illumination, background, clutter, object instance How can a human or a machine learn those representations by just looking at the world?
How can we learn visual categories from just a few examples? The recognition of common objects is essentially feed forward. But not all of vision is feed forward. Much of the visual system all of it? If the visual system is deep and learned, what is the learning algorithm? What learning algorithm can train neural nets as deep as the visual system 10 layers?On the exercises and problems.
Using neural nets to recognize handwritten digits Perceptrons Sigmoid neurons The architecture of neural networks A simple network to classify handwritten digits Learning with gradient descent Implementing our network to classify digits Toward deep learning. Backpropagation: the big picture.
Improving the way neural networks learn The cross-entropy cost function Overfitting and regularization Weight initialization Handwriting recognition revisited: the code How to choose a neural network's hyper-parameters? Other techniques. A visual proof that neural nets can compute any function Two caveats Universality with one input and one output Many input variables Extension beyond sigmoid neurons Fixing up the step functions Conclusion.
Why are deep neural networks hard to train? The vanishing gradient problem What's causing the vanishing gradient problem? Unstable gradients in deep neural nets Unstable gradients in more complex networks Other obstacles to deep learning. Deep learning Introducing convolutional networks Convolutional neural networks in practice The code for our convolutional networks Recent progress in image recognition Other approaches to deep neural nets On the future of neural networks.
Appendix: Is there a simple algorithm for intelligence? If you benefit from the book, please make a small donation. Thanks to all the supporters who made the book possible, with especial thanks to Pavel Dudrenov. Thanks also to all the contributors to the Bugfinder Hall of Fame. Code repository. Michael Nielsen's project announcement mailing list. Neural Networks and Deep Learning is a free online book. The book will teach you about: Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data Deep learning, a powerful set of techniques for learning in neural networks Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing.
This book will teach you many of the core concepts behind neural networks and deep learning. For more details about the approach taken in the book, see here. Or you can jump directly to Chapter 1 and get started.
An Introduction to Neural Network and Deep Learning For Beginners
Neural Networks and Deep Learning What this book is about On the exercises and problems Using neural nets to recognize handwritten digits Perceptrons Sigmoid neurons The architecture of neural networks A simple network to classify handwritten digits Learning with gradient descent Implementing our network to classify digits Toward deep learning. The cross-entropy cost function Overfitting and regularization Weight initialization Handwriting recognition revisited: the code How to choose a neural network's hyper-parameters?
Two caveats Universality with one input and one output Many input variables Extension beyond sigmoid neurons Fixing up the step functions Conclusion. Introducing convolutional networks Convolutional neural networks in practice The code for our convolutional networks Recent progress in image recognition Other approaches to deep neural nets On the future of neural networks.
Deep Learning Workstations, Servers, and Laptops. In academic work, please cite this book as: Michael A. This means you're free to copy, share, and build on this book, but not to sell it.
If you're interested in commercial use, please contact me. Last update: Thu Dec 26 Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions.
Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. Deep learning is one of the foundations of artificial intelligence AIand the current interest in deep learning is due in part to the buzz surrounding AI. Deep learning techniques have improved the ability to classify, recognize, detect and describe — in one word, understand.
For example, deep learning is used to classify images, recognize speech, detect objects and describe content. Systems such as Siri and Cortana are powered, in part, by deep learning.
14 Most Popular Presentations On Artificial Intelligence And Machine Learning On SlideShare
At the same time, human-to-machine interfaces have evolved greatly as well. The mouse and the keyboard are being replaced with gesture, swipe, touch and natural language, ushering in a renewed interest in AI and deep learning.
In this deep learning example, the computer program is learning to interpret animal tracks to help with animal conservation. A lot of computational power is needed to solve deep learning problems because of the iterative nature of deep learning algorithms, their complexity as the number of layers increase, and the large volumes of data needed to train the networks.
The dynamic nature of deep learning methods — their ability to continuously improve and adapt to changes in the underlying information pattern — presents a great opportunity to introduce more dynamic behavior into analytics. Greater personalization of customer analytics is one possibility. Another great opportunity is to improve accuracy and performance in applications where neural networks have been used for a long time. Through better algorithms and more computing power, we can add greater depth.
While the current market focus of deep learning techniques is in applications of cognitive computing, there is also great potential in more traditional analytics applications, for example, time series analysis. Another opportunity is to simply be more efficient and streamlined in existing analytical operations.
Recently, SAS experimented with deep neural networks in speech-to-text transcription problems. Compared to the standard techniques, the word-error-rate decreased by more than 10 percent when deep neural networks were applied. They also eliminated about 10 steps of data preprocessing, feature engineering and modeling. The impressive performance gains and the time savings when compared to feature engineering signify a paradigm shift.
Learn more about what people are saying. Why is deep learning unequaled among machine learning techniques? Learn more about how deep learning works and why it's not overhyped. Read the blog post.Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis.
The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing.
This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives. The course will contain guided hands on lab that will lead the students in their first steps in deep learning frameworks.
A summary can be found on this site by Prof. Jordi Torres. Students will work in teams to develop a machine learning research project that will be presented both in an oral presentation and as a poster during the final session open to the general public. Pacman with Deep Learning Creative Writing. Deep Learning for Multimedia. Insight Dublin City University Amaia Salvador and Santiago Pascual.
Persontyle LaSalle URL. May Stanford University, Spring University of Toronto, Winter MIT, Winter University of Illinois at Urbana-Champaign. Spring Berkeley University.
Georgia Tech GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Instructor: Andrew Ng Community: deeplearning.
Data Augmentation | How to use Deep Learning when you have Limited Data — Part 2
I created this repository post completing the Deep Learning Specialization on coursera. Its includes solutions to the quizzes and programming assignments which are required for successful completion of the courses.
Note: Coursera Honor Code advise against plagiarism. Readers are requested to use this repo only for insights and reference. If you are undertaking these courses at coursera, please submit you original work only. The constitution of the repository as per course modules, quizzes and programming assignments is as follows:.
Here are some references of lecture notes and reviews drawn by some communities, authors and editors. Deep Learning Specialization offered by Andrew Ng is an excellent blend of content for deep learning enthusiasts. I thoroughly enjoyed the course and earned the certificate. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Sign up. A deep learning specialization series of 5 courses offered by Andrew Ng at Coursera. Jupyter Notebook. Jupyter Notebook Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Jan 5, Jan 11, Deep learning is a type of machine learning ML and artificial intelligence AI that imitates the way humans gain certain types of knowledge.
Deep learning is an important element of data science, which includes statistics and predictive modeling. It is extremely beneficial to data scientists who are tasked with collecting, analyzing and interpreting large amounts of data; deep learning makes this process faster and easier. At its simplest, deep learning can be thought of as a way to automate predictive analytics.
While traditional machine learning algorithms are linear, deep learning algorithms are stacked in a hierarchy of increasing complexity and abstraction. To understand deep learning, imagine a toddler whose first word is dog. The toddler learns what a dog is -- and is not -- by pointing to objects and saying the word dog. The parent says, "Yes, that is a dog," or, "No, that is not a dog. What the toddler does, without knowing it, is clarify a complex abstraction -- the concept of dog -- by building a hierarchy in which each level of abstraction is created with knowledge that was gained from the preceding layer of the hierarchy.
Computer programs that use deep learning go through much the same process as the toddler learning to identify the dog. Each algorithm in the hierarchy applies a nonlinear transformation to its input and uses what it learns to create a statistical model as output.
Iterations continue until the output has reached an acceptable level of accuracy. The number of processing layers through which data must pass is what inspired the label deep. In traditional machine learning, the learning process is supervised, and the programmer has to be extremely specific when telling the computer what types of things it should be looking for to decide if an image contains a dog or does not contain a dog. This is a laborious process called feature extraction, and the computer's success rate depends entirely upon the programmer's ability to accurately define a feature set for "dog.
Unsupervised learning is not only faster, but it is usually more accurate. Initially, the computer program might be provided with training data -- a set of images for which a human has labeled each image "dog" or "not dog" with meta tags. The program uses the information it receives from the training data to create a feature set for "dog" and build a predictive model. In this case, the model the computer first creates might predict that anything in an image that has four legs and a tail should be labeled "dog.
With each iteration, the predictive model becomes more complex and more accurate. Unlike the toddler, who will take weeks or even months to understand the concept of "dog," a computer program that uses deep learning algorithms can be shown a training set and sort through millions of images, accurately identifying which images have dogs in them within a few minutes. To achieve an acceptable level of accuracy, deep learning programs require access to immense amounts of training data and processing power, neither of which were easily available to programmers until the era of big data and cloud computing.
Because deep learning programming can create complex statistical models directly from its own iterative output, it is able to create accurate predictive models from large quantities of unlabeled, unstructured data. This is important as the internet of things IoT continues to become more pervasive, because most of the data humans and machines create is unstructured and is not labeled.
A type of advanced machine learning algorithm, known as artificial neural networksunderpins most deep learning models. As a result, deep learning may sometimes be referred to as deep neural learning or deep neural networking. Neural networks come in several different forms, including recurrent neural networks, convolutional neural networks, artificial neural networks and feedforward neural networks -- and each has benefits for specific use cases.
However, they all function in somewhat similar ways, by feeding data in and letting the model figure out for itself whether it has made the right interpretation or decision about a given data element. Neural networks involve a trial-and-error process, so they need massive amounts of data on which to train. It's no coincidence neural networks became popular only after most enterprises embraced big data analytics and accumulated large stores of data.
Because the model's first few iterations involve somewhat-educated guesses on the contents of an image or parts of speech, the data used during the training stage must be labeled so the model can see if its guess was accurate. This means, though many enterprises that use big data have large amounts of data, unstructured data is less helpful. Unstructured data can only be analyzed by a deep learning model once it has been trained and reaches an acceptable level of accuracy, but deep learning models can't train on unstructured data.
Various different methods can be used to create strong deep learning models. These techniques include learning rate decay, transfer learning, training from scratch and dropout. Learning rate decay. The learning rate is a hyperparameter -- a factor that defines the system or sets conditions for its operation prior to the learning process -- that controls how much change the model experiences in response to the estimated error every time the model weights are altered.
Learning rates that are too high may result in unstable training processes or the learning of a suboptimal set of weights.For a quick overview of a subject or a breakdown of concepts, SlideShare serves as a go-to platform for many. The recapitulations found in many of the presentations are both concise and informative. The most popular presentations are the ones that have received the most number of likes and have been viewed more than the other presentations in a particular category.
Deep Learning and everything else in between. People who are not aware of what artificial intelligence is will find the topic presented in a very simple manner here. Along with the explanation of what AI is, the two major approaches towards AI are discussed— logic and rules-based approach, and machine learning approach.
Special emphasis on machine learning approach can be seen in the slides devoted to its detailed examination. The examination goes beyond the rudimentary explanation of what machine learning is and presents examples of proxies that seem like machine learning but are not.
The presentation lists examples of AI in the field of law and identifies some of the limitations of AI technology. For the uninitiated, this presentation offers an ideal rundown of AI. The question of AI being a threat is raised at the very beginning. However, as the presentation progresses, it discusses the basics necessary for understanding AI. The most basic question of what is artificial intelligence is answered. A brief history of AI and the discussion on recent advances in the field of AI is also found.
The various areas where AI currently sees practical application have been listed. Fascinating uses that AI can be put to in the future are also found in the presentation. The two approaches of achieving AI, machine learning and deep learning, is touched upon. All in all, this presentation serves as a simple introduction to AI. An exciting application of AI can be found in chatbots. Here, the limitless scope of chatbots is explored. The evolution of chatbots and its absorption of more AI in the future is also looked into.
E-Commerce is touted as the biggest beneficiary of the advancement in chatbots and that bot technology will owe its rise to services and commerce. Two tech giants, Facebook and Google, have been pitted against each other based on their ongoing developments in this area and the question of who will emerge as the best is raised. In order to derive a better understanding of this presentation, it is advisable to first watch the original talk.
During the course of the presentation, many examples of how machines can learn and perform any human task that is repetitive in nature are cited. Other possibilities suggested include the creation of new unheard jobs for human beings as a result of aggressive use of AI and other allied technologies.
Qualities that are characteristic only of human beings, may be the basis on which these jobs will be created is also suggested.