- Top /r/MachineLearning Posts, September: Open Images Dataset; Whopping Deep Learning Grant; Advanced ML Courseware - Oct 7, 2016.
Google Research announces the Open Images dataset; Canadian Government Deep Learning Research grant; DeepMind: WaveNet - A Generative Model for Raw Audio; Machine Learning in a Year - From total noob to using it at work; Phd-level machine learning courses; xkcd: Linear Regression
Canada, Courses, Deep Learning, Generative Models, Geoff Hinton, Machine Learning, Reddit, xkcd
- Deep Learning Reading Group: SqueezeNet - Sep 29, 2016.
This paper introduces a small CNN architecture called “SqueezeNet” that achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters.
Compression, Deep Learning, Lab41, Machine Learning, Neural Networks
- Data Science for Internet of Things (IoT): Ten Differences From Traditional Data Science - Sep 26, 2016.
The connected devices (The Internet of Things) generate more than 2.5 quintillion bytes of data daily. All this data will significantly impact business processes and the Data Science for IoT will take increasingly central role. Here we outline 10 main differences between Data Science for IoT and traditional Data Science.
Data Science, Deep Learning, IoT, Privacy, Robots
- Deep Learning Reading Group: Deep Residual Learning for Image Recognition - Sep 22, 2016.
Published in 2015, today's paper offers a new architecture for Convolution Networks, one which has since become a staple in neural network implementation. Read all about it here.
Academics, Convolutional Neural Networks, Deep Learning, Image Recognition, Lab41, Machine Learning, Neural Networks
- 9 Key Deep Learning Papers, Explained - Sep 20, 2016.
If you are interested in understanding the current state of deep learning, this post outlines and thoroughly summarizes 9 of the most influential contemporary papers in the field.
Pages: 1 2 3
Academics, Deep Learning, Explained, Neural Networks
- Urban Sound Classification with Neural Networks in Tensorflow - Sep 12, 2016.
This post discuss techniques of feature extraction from sound in Python using open source library Librosa and implements a Neural Network in Tensorflow to categories urban sounds, including car horns, children playing, dogs bark, and more.
Pages: 1 2
Deep Learning, Feature Extraction, Machine Learning, Neural Networks, TensorFlow
- Deep Learning Reading Group: Deep Networks with Stochastic Depth - Sep 8, 2016.
An concise overview of a recent paper which introduces a new way to perturb networks during training in order to improve their performance, stochastic depth networks.
Academics, Deep Learning, Lab41, Neural Networks
- A Beginner’s Guide To Understanding Convolutional Neural Networks Part 2 - Sep 8, 2016.
This is the second part of a thorough introductory treatment of convolutional neural networks. Have a look after reading the first part.
Pages: 1 2
Beginners, Convolutional Neural Networks, Deep Learning, Neural Networks
- KDnuggets™ News 16:n32, Sep 7: Cartoon: Data Scientist was sexiest job until…; Up to Speed on Deep Learning - Sep 7, 2016.
Cartoon: Data Scientist - the sexiest job of the 21st century until...; Up to Speed on Deep Learning: July Update; How Convolutional Neural Networks Work; Learning from Imbalanced Classes; What is the Role of the Activation Function in a Neural Network?
Balancing Classes, Convolutional Neural Networks, Data Scientist, Deep Learning, Neural Networks
- A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1 - Sep 6, 2016.
Interested in better understanding convolutional neural networks? Check out this first part of a very comprehensive overview of the topic.
Pages: 1 2
Beginners, Convolutional Neural Networks, Deep Learning, Neural Networks
- The Gentlest Introduction to Tensorflow – Part 2 - Aug 19, 2016.
Check out the second and final part of this introductory tutorial to TensorFlow.
Pages: 1 2
Beginners, Deep Learning, Gradient Descent, Machine Learning, TensorFlow
- Top Machine Learning Projects for Julia - Aug 19, 2016.
Julia is gaining traction as a legitimate alternative programming language for analytics tasks. Learn more about these 5 machine learning related projects.
Deep Learning, Julia, Machine Learning, Open Source, scikit-learn
- 7 Steps to Understanding Computer Vision - Aug 9, 2016.
A starting point for Computer Vision and how to get going deeper. Dive into this post for some overview of the right resources and a little bit of advice.
7 Steps, Computer Vision, Deep Learning, Neural Networks, Python
- Yann LeCun Quora Session Overview - Aug 1, 2016.
Here is a quick oversight, with excerpts, of the Yann LeCun Quora Session which took place on Thursday July 28, 2016.
Deep Learning, Generative Adversarial Network, Quora, Yann LeCun
- In Deep Learning, Architecture Engineering is the New Feature Engineering - Jul 19, 2016.
A discussion of architecture engineering in deep neural networks, and its relationship with feature engineering.
Architecture, Deep Learning, Feature Engineering, Neural Networks
- How to Start Learning Deep Learning - Jul 14, 2016.
Want to get started learning deep learning? Sure you do! Check out this great overview, advice, and list of resources.
Andrej Karpathy, Coursera, Deep Learning, edX, Geoff Hinton, Neural Networks
- 5 Deep Learning Projects You Can No Longer Overlook - Jul 12, 2016.
There are a number of "mainstream" deep learning projects out there, but many more niche projects flying under the radar. Have a look at 5 such projects worth checking out.
C++, Deep Learning, Javascript, Machine Learning, Neural Networks, Overlook, Python
- Top Machine Learning MOOCs and Online Lectures: A Comprehensive Survey - Jul 11, 2016.
This post reviews Machine Learning MOOCs and online lectures for both the novice and expert audience.
Andrew Ng, Coursera, Deep Learning, edX, Machine Learning, MOOC, Nando de Freitas, Tom Mitchell, Udacity
- Deep Residual Networks for Image Classification with Python + NumPy - Jul 7, 2016.
This post outlines the results of an innovative Deep Residual Network implementation for Image Classification using Python and NumPy.
Deep Learning, Neural Networks, numpy, Python
- Top Machine Learning Libraries for Javascript - Jun 24, 2016.
Javascript may not be the conventional choice for machine learning, but there is no reason it cannot be used for such tasks. Here are the top libraries to facilitate machine learning in Javascript.
Andrej Karpathy, Convolutional Neural Networks, Deep Learning, Javascript, Machine Learning, Neural Networks
- What is the Difference Between Deep Learning and “Regular” Machine Learning? - Jun 3, 2016.
Another concise explanation of a machine learning concept by Sebastian Raschka. This time, Sebastian explains the difference between Deep Learning and "regular" machine learning.
Convolutional Neural Networks, Deep Learning
- Let Me Hear Your Voice and I’ll Tell You How You Feel - May 24, 2016.
This post provides an overview of a voice tone analyzer implemented as part of a cohesive emotion detection system, directly from the researcher and architect.
Artificial Intelligence, Deep Learning, Emotion
- 5 Machine Learning Projects You Can No Longer Overlook - May 19, 2016.
We all know the big machine learning projects out there: Scikit-learn, TensorFlow, Theano, etc. But what about the smaller niche projects that are actively developed, providing useful services to users? Here are 5 such projects.
Data Cleaning, Deep Learning, Machine Learning, Open Source, Overlook, Pandas, Python, scikit-learn, Theano
- Troubleshooting Neural Networks: What is Wrong When My Error Increases? - May 13, 2016.
An overview of some of the things that could lead to an increased error rate in neural network implementations.
Deep Learning, Neural Networks, Overfitting
- Are Deep Neural Networks Creative? - May 12, 2016.
Deep neural networks routinely generate images and synthesize text. But does this amount to creativity? Can we reasonably claim that deep learning produces art?
Artificial Intelligence, Deep Learning, Generative Adversarial Network, Generative Models, Recurrent Neural Networks, Reinforcement Learning, Zachary Lipton
- Deep Learning and Neuromorphic Chips - May 12, 2016.
The 3 main ingredients to creating artificial intelligence are hardware, software, and data, and while we have focused historically on improving software and data, what if, instead, the hardware was drastically changed?
AI, Brain, Deep Learning, Neural Networks
- Machine Learning for Artists – Video lectures and notes - Apr 28, 2016.
Art has always been deep for those who appreciate it... but now, more than ever, deep learning is making a real impact on the art world. Check out this graduate course, and its freely-available resources, focusing on this very topic.
Art, Convolutional Neural Networks, Deep Learning, Machine Learning, Recurrent Neural Networks
- When Does Deep Learning Work Better Than SVMs or Random Forests®? - Apr 22, 2016.
Some advice on when a deep neural network may or may not outperform Support Vector Machines or Random Forests.
Advice, Deep Learning, random forests algorithm, Support Vector Machines, SVM
- Top 10 IPython Notebook Tutorials for Data Science and Machine Learning - Apr 22, 2016.
A list of 10 useful Github repositories made up of IPython (Jupyter) notebooks, focused on teaching data science and machine learning. Python is the clear target here, but general principles are transferable.
Data Science, Deep Learning, GitHub, IPython, Machine Learning, Python, Sebastian Raschka, TensorFlow
- Deep Learning for Chatbots, Part 1 – Introduction - Apr 19, 2016.
The first in a series of tutorial posts on using Deep Learning for chatbots, this covers some of the techniques being used to build conversational agents, and goes from the current state of affairs through to what is and is not possible.
Chatbot, Deep Learning, Siri
- Top 15 Frameworks for Machine Learning Experts - Apr 19, 2016.
Either you are a researcher, start-up or big organization who wants to use machine learning, you will need the right tools to make it happen. Here is a list of the most popular frameworks for machine learning.
Data Science Tools, Deep Learning, Devendra Desale, Machine Learning, MLlib
- New Deep Learning Book Finished, Finalized Online Version Available - Apr 12, 2016.
What will likely become known as the seminal book on deep learning is finally finished, with the online version finalized and freely-accessible to all those interested in mastering deep neural networks.
Aaron Courville, Book, Deep Learning, Free ebook, Ian Goodfellow, Yoshua Bengio
- Deep Learning for Internet of Things Using H2O - Apr 6, 2016.
H2O is feature-rich open source machine learning platform known for its R and Spark integration and it’s ease of use. This is an overview of using H2O deep learning for data science with the Internet of Things.
Deep Learning, H2O, Internet of Things, IoT, R
- 100 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning - Mar 29, 2016.
Stay on top of your data science skills game! Here’s a list of about 100 most active and interesting blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence.
Pages: 1 2
Big Data, Blogs, Data Science, Deep Learning, Hadoop, Machine Learning
- Must Know Tips for Deep Learning Neural Networks - Mar 22, 2016.
Deep learning is white hot research topic. Add some solid deep learning neural network tips and tricks from a PhD researcher.
Pages: 1 2
Convolutional Neural Networks, Deep Learning
- The Data Science Puzzle, Explained - Mar 10, 2016.
The puzzle of data science is examined through the relationship between several key concepts in the data science realm. As we will see, far from being concrete concepts etched in stone, divergent opinions are inevitable; this is but another opinion to consider.
Pages: 1 2
Artificial Intelligence, Data Mining, Data Science, Deep Learning, Explained, Machine Learning
- Distributed TensorFlow Has Arrived - Mar 1, 2016.
Google has open sourced its distributed version of TensorFlow. Get the info on it here, and catch up on some other TensorFlow news at the same time.
Deep Learning, Distributed Systems, Google, Matthew Mayo, TensorFlow
- Opening Up Deep Learning For Everyone - Feb 19, 2016.
Opening deep learning up to everyone is a noble goal. But is it achievable? Should non-programmers and even non-technical people be able to implement deep neural models?
Caffe, Deep Learning, Feature Engineering, Open Source, TensorFlow
- The ICLR Experiment: Deep Learning Pioneers Take on Scientific Publishing - Feb 15, 2016.
Deep learning pioneers Yann LeCun and Yoshua Bengio have undertaken a grand experiment in academic publishing. Embracing a radical level of transparency and unprecedented public participation, they've created an opportunity not only to find and vet the best papers, but also to gather data about the publication process itself.
Academics, arXiv, Deep Learning, ICLR, Neural Networks, Yann LeCun, Yoshua Bengio, Zachary Lipton
- Scikit Flow: Easy Deep Learning with TensorFlow and Scikit-learn - Feb 12, 2016.
Scikit Learn is a new easy-to-use interface for TensorFlow from Google based on the Scikit-learn fit/predict model. Does it succeed in making deep learning more accessible?
Deep Learning, Google, Matthew Mayo, Python, scikit-learn, TensorFlow
- Cartoon: Deeper Deep Learning - Feb 1, 2016.
New KDnuggets Cartoon looks at a creative new way of achieving even better results and breaking through Machine Learning barriers with even "deeper" Deep Learning approach.
Cartoon, Deep Learning
- Is Deep Learning Overhyped? - Jan 29, 2016.
With all of the success that deep learning is experiencing, the detractors and cheerleaders can be seen coming out of the woodwork. What is the real validity of deep learning, and is it simply hype?
Deep Learning, Hype, Matthew Mayo, Quora, Yoshua Bengio
- Deep Learning with Spark and TensorFlow - Jan 28, 2016.
The integration of TensorFlow with Spark leverages the distributed framework for hyperparameter tuning and model deployment at scale. Both time savings and improved error rates are demonstrated.
Apache Spark, Deep Learning, Distributed Systems, TensorFlow
- Anthony Goldbloom gives you the Secret to winning Kaggle competitions - Jan 20, 2016.
Kaggle CEO shares insights on best approaches to win Kaggle competitions, along with a brief explanation of how Kaggle competitions work.
Anthony Goldbloom, Competition, Deep Learning, Feature Engineering, Kaggle, Neural Networks, Success
- Research Leaders on Data Mining, Data Science and Big Data key advances, top trends - Jan 18, 2016.
Research Leaders in Data Science and Big Data reflect on the most important research advances in 2015 and the key trends expected to dominate throughout 2016.
Pages: 1 2
Bing Liu, Charu Aggarwal, Deep Learning, Ingo Mierswa, Internet of Things, IoT, Michael Berthold, Mohammed Zaki, Neural Networks, Padhraic Smyth, Pedro Domingos, Research, Trends
- Top 10 Deep Learning Projects on Github - Jan 13, 2016.
The top 10 deep learning projects on Github include a number of libraries, frameworks, and education resources. Have a look at the tools others are using, and the resources they are learning from.
Caffe, Deep Learning, GitHub, Open Source, Top 10, Tutorials
- Attention and Memory in Deep Learning and NLP - Jan 12, 2016.
An overview of attention mechanisms and memory in deep neural networks and why they work, including some specific applications in natural language processing and beyond.
Pages: 1 2
Deep Learning, Machine Translation, NLP, Recurrent Neural Networks
- 7 Steps to Understanding Deep Learning - Jan 11, 2016.
There are many deep learning resources freely available online, but it can be confusing knowing where to begin. Go from vague understanding of deep neural networks to knowledgeable practitioner in 7 steps!
Pages: 1 2
7 Steps, Caffe, Convolutional Neural Networks, Deep Learning, Matthew Mayo, Recurrent Neural Networks, TensorFlow, Theano
- DeepLearningKit – Open Source Deep Learning Framework for Apple iOS, OS X - Dec 30, 2015.
We are introducing you to the new deep learning framework “DeepLearningKit”, for the Apple based OS which is developed in Metal and Swift.
Apple, Deep Learning, iOS
- 50 Deep Learning Software Tools and Platforms, Updated - Dec 15, 2015.
We present the popular software & toolkit resources for Deep Learning, including Caffe, Cuda-convnet, Deeplearning4j, Pylearn2, Theano, and Torch. Explore the new list!
Caffe, Deep Learning, Pylearn2, Theano, Tools
- Deep Learning Transcends the Bag of Words - Dec 7, 2015.
Generative RNNs are now widely popular, many modeling text at the character level and typically using unsupervised approach. Here we show how to generate contextually relevant sentences and explain recent work that does it successfully.
Beer, Deep Learning, Generative Models, Recurrent Neural Networks, Zachary Lipton
- Spark + Deep Learning: Distributed Deep Neural Network Training with SparkNet - Dec 4, 2015.
Training deep neural nets can take precious time and resources. By leveraging an existing distributed batch processing framework, SparkNet can train neural nets quickly and efficiently.
Pages: 1 2
Apache Spark, Caffe, Deep Learning, Distributed Systems, H2O, Matthew Mayo, Neural Networks
- Amazon Top 20 Books in Neural Networks - Nov 30, 2015.
These are the most popular neural networks books on Amazon. Perhaps there is something of interest to you here.
Amazon, Book, Deep Learning, Matthew Mayo, Neural Networks
- Top KDnuggets tweets, Nov 16-22: Dilbert discovers the perfect chart; TensorFlow Disappoints – Google Deep Learning falls shallow - Nov 23, 2015.
A standard #graph for any occasion! #Dilbert discovers the perfect chart; TensorFlow Disappoints - Google #DeepLearning falls shallow; All the #BigData tools and how to use them; KDnuggets #DataScience #Cartoon Caption Contest.
Cartoon, Deep Learning, Dilbert, James Bond, TensorFlow, Tesla
- Deep Learning for Visual Question Answering - Nov 19, 2015.
Here we discuss about the Visual Question Answering problem, and I’ll also present neural network based approaches for same.
Pages: 1 2
Deep Learning, Question answering, Turing Test
- 7 Steps to Mastering Machine Learning With Python - Nov 19, 2015.
There are many Python machine learning resources freely available online. Where to begin? How to proceed? Go from zero to Python machine learning hero in 7 steps!
Pages: 1 2
7 Steps, Anaconda, Caffe, Deep Learning, Machine Learning, Matthew Mayo, Python, scikit-learn, Theano
- A Statistical View of Deep Learning - Nov 13, 2015.
A statistical overview of deep learning, with a focus on testing wide-held beliefs, highlighting statistical connections, and the unseen implications of deep learning. The post links to 6 articles covering a number of related topics.
Deep Learning, Recurrent Neural Networks, Statistical Learning
- Understanding Convolutional Neural Networks for NLP - Nov 11, 2015.
Dive into the world of Convolution Neural Networks (CNN), learn how they work, how to apply them for NLP, and how to tune CNN hyperparameters for best performance.
Pages: 1 2 3
Convolutional Neural Networks, Deep Learning, Neural Networks, NLP
- Why Deep Learning Works – Key Insights and Saddle Points - Nov 3, 2015.
A quality discussion on the theoretical motivations for deep learning, including distributed representation, deep architecture, and the easily escapable saddle point.
Deep Learning, Distributed Representation, Matthew Mayo, Yoshua Bengio
- 6 crazy things Deep Learning and Topological Data Analysis can do with your data - Nov 2, 2015.
Want to analyze a high dimensional dataset and you are running out of options? Find out how Deep Learning combined with Topological Data Analysis can do exactly that and more.
Clustering, Data Visualization, Deep Learning, Netflix, Topological Data Analysis
- MetaMind Mastermind Richard Socher: Uncut Interview - Oct 20, 2015.
In a wide-ranging interview, Richard Socher opens up about MetaMind, deep learning, the nature of corporate research, and the future of machine learning.
Convolutional Neural Networks, Deep Learning, Image Recognition, MetaMind, Recurrent Neural Networks, Richard Socher, Zachary Lipton
- Does Deep Learning Come from the Devil? - Oct 9, 2015.
Deep learning has revolutionized computer vision and natural language processing. Yet the mathematics explaining its success remains elusive. At the Yandex conference on machine learning prospects and applications, Vladimir Vapnik offered a critical perspective.
Berlin, Deep Learning, Machine Learning, Support Vector Machines, SVM, Vladimir Vapnik, Yandex, Zachary Lipton
- Recurrent Neural Networks Tutorial, Introduction - Oct 7, 2015.
Recurrent Neural Networks (RNNs) are popular models that have shown great promise in NLP and many other Machine Learning tasks. Here is a much-needed guide to key RNN models and a few brilliant research papers.
Pages: 1 2
Deep Learning, Neural Networks, NLP, Recurrent Neural Networks
- Top /r/MachineLearning Posts, September: Implement a neural network from scratch in C++ - Oct 6, 2015.
Neural network in C++ for beginners, Chinese character handwriting recognition beats humans, a handy machine learning algorithm cheat sheet, neural nets versus functional programming, and a neural nets paper repository.
C++, Deep Learning, Matthew Mayo, Neural Networks, Python, R, Reddit
- Recycling Deep Learning Models with Transfer Learning - Aug 14, 2015.
Deep learning exploits gigantic datasets to produce powerful models. But what can we do when our datasets are comparatively small? Transfer learning by fine-tuning deep nets offers a way to leverage existing datasets to perform well on new tasks.
Deep Learning, Image Recognition, ImageNet, Machine Learning, Neural Networks, Transfer Learning, Zachary Lipton
- Deep Learning Adversarial Examples – Clarifying Misconceptions - Jul 15, 2015.
Google scientist clarifies misconceptions and myths around Deep Learning Adversarial Examples, including: they do not occur in practice, Deep Learning is more vulnerable to them, they can be easily solved, and human brains make similar mistakes.
Adversarial, Deep Learning, Ian Goodfellow, Myths, Regularization
- Can Deep Learning Help you Find the Perfect Girl? – Part 2 - Jul 13, 2015.
Using Deep Learning to find the perfect match, PhD student Harm de Vries describes the process of data collection and analysis. Finally, the results from matching algorithm are compared to human assessment for identifying an individual's dating preferences.
Deep Learning, Love, OkCupid, Online Dating, Predictive Analytics
- Can deep learning help find the perfect date? - Jul 10, 2015.
When a Machine Learning PhD student at University of Montreal starts using Tinder, he soon realises that something is missing in the dating app - the ability to predict to which girls he is attracted. Harm de Vries applies Deep Learning to assist in the pursuit of the perfect match.
Deep Learning, ICML, Love, Machine Learning, Online Dating, Predictive Analytics
- Deep Learning and the Triumph of Empiricism - Jul 7, 2015.
Theoretical guarantees are clearly desirable. And yet many of today's best-performing supervised learning algorithms offer none. What explains the gap between theoretical soundness and empirical success?
Big Data, Data Science, Deep Learning, Mathematics, Statistics, Zachary Lipton
- Top 10 Machine Learning Videos on YouTube - Jun 23, 2015.
The top machine learning videos on YouTube include lecture series from Stanford and Caltech, Google Tech Talks on deep learning, using machine learning to play Mario and Hearthstone, and detecting NHL goals from live streams.
Andrew Ng, Computer Vision, Deep Learning, Geoff Hinton, Google, Grant Marshall, Machine Learning, Neural Networks, Robots, Video Games, Youtube
- Popular Deep Learning Tools – a review - Jun 18, 2015.
Deep Learning is the hottest trend now in AI and Machine Learning. We review the popular software for Deep Learning, including Caffe, Cuda-convnet, Deeplearning4j, Pylearn2, Theano, and Torch.
Convolutional Neural Networks, CUDA, Deep Learning, GPU, Pylearn2, Python, Ran Bi, Theano, Torch
- Dark Knowledge Distilled from Neural Network - May 26, 2015.
Geoff Hinton never stopped generating new ideas. This post is a review of his research on “dark knowledge”. What’s that supposed to mean?
Dark Knowledge, Deep Learning, Geoff Hinton, Neural Networks, Ran Bi
- Most Viewed Big Data Videos on YouTube - May 9, 2015.
The top Big Data YouTube videos by those like Hortonworks and Kirk D. Borne cover diverse topics including Hadoop, Big Data Trends, Deep Learning, and Big Data Leadership.
Big Data, Cloudera, Deep Learning, Google, Grant Marshall, Hadoop, IBM, Kirk D. Borne, TED, Youtube
- The Myth of Model Interpretability - Apr 27, 2015.
Deep networks are widely regarded as black boxes. But are they truly uninterpretable in any way that logistic regression is not?
Deep Learning, Deep Neural Network, Interpretability, Support Vector Machines, Zachary Lipton
- Talking Machine – 3 Deep Learning Gurus Talk about History and Future of Machine Learning, part 1 - Mar 25, 2015.
An recent interview from the talking machine podcast with three deep learning experts. They talked about the neural network winter and its renewal.
convnet, Deep Learning, Geoff Hinton, Neural Networks, Ran Bi, Yann LeCun, Yoshua Bengio
- Top KDnuggets tweets, Mar 16-18: 87 Studies shown that accurate numbers aren’t more useful than the ones you make up (Dilbert) - Mar 19, 2015.
Also Sirius - a free, open-source version of Siri; #PI art: the first 13,689 digits of pi; Great tutorial + #Python code: 1-Layer Neural Networks.
Cartoon, Data Preparation, Deep Learning, Dilbert, Excel, Neural Networks, pi, Python, Siri
- Small Data requires Specialized Deep Learning and Yann LeCun response - Mar 19, 2015.
For industries that have relatively small data sets (less than a petabyte), a Specialized Deep Learning approach based on unsupervised learning and domain knowledge is needed.
Pages: 1 2
Big Data, Deep Learning, Small Data, Yann LeCun
- Deep Learning for Text Understanding from Scratch - Mar 13, 2015.
Forget about the meaning of words, forget about grammar, forget about syntax, forget even the very concept of a word. Now let the machine learn everything by itself.
convnet, Deep Learning, Francois Petitjean, Text Classification, Torch, Yann LeCun
- Deep Learning, The Curse of Dimensionality, and Autoencoders - Mar 12, 2015.
Autoencoders are an extremely exciting new approach to unsupervised learning and for many machine learning tasks they have already surpassed the decades of progress made by researchers handpicking features.
Pages: 1 2 3
Autoencoder, Deep Learning, Face Recognition, Geoff Hinton, Image Recognition, Nikhil Buduma
- Data Science’s Most Used, Confused, and Abused Jargon - Feb 10, 2015.
As data science has spread through the mainstream, so too has a dense vocabulary of ill-defined jargon. In a split-personality post, we offer several perspectives on many of data science's most confused terms.
Big Data Privacy, Data Science, Deep Learning, Zachary Lipton
- Facebook Open Sources deep-learning modules for Torch - Feb 9, 2015.
We review Facebook recently released Torch module for Deep Learning, which helps researchers train large scale convolutional neural networks for image recognition, natural language processing and other AI applications.
Artificial Intelligence, Deep Learning, Facebook, GPU, Neural Networks, NYU, Ran Bi, Torch, Yann LeCun
- (Deep Learning’s Deep Flaws)’s Deep Flaws - Jan 26, 2015.
Recent press has challenged the hype surrounding deep learning, trumpeting several findings which expose shortcomings of current algorithms. However, many of deep learning's reported flaws are universal, affecting nearly all machine learning algorithms.
convnet, Deep Learning, Ian Goodfellow, Machine Learning, Neural Networks, Yoshua Bengio, Zachary Lipton
- Interview: Arno Candel, H2O.ai on the Basics of Deep Learning to Get You Started - Jan 20, 2015.
We discuss how Deep Learning is different from the other methods of Machine Learning, unique characteristics and benefits of Deep Learning, and the key components of H2O architecture.
Apache Spark, Arno Candel, Deep Learning, H2O, Machine Learning
- MetaMind Competes with IBM Watson Analytics and Microsoft Azure Machine Learning - Jan 14, 2015.
While Microsoft and IBM rush to bring data science and visualization to the masses, MetaMind follows another path, offering deep learning as a service.
Azure ML, Deep Learning, IBM Watson, MetaMind, Richard Socher, Zachary Lipton
- Deep Learning can be easily fooled - Jan 14, 2015.
It is almost impossible for human eyes to label the images below to be anything but abstract arts. However, researchers found that Deep Neural Network will label them to be familiar objects with 99.99% confidence. The generality of DNN is questioned again.
Deep Learning, Deep Neural Network, Evolutionary Algorithm, Image Recognition, Ran Bi
- Deep Learning in a Nutshell – what it is, how it works, why care? - Jan 12, 2015.
Deep learning and neural networks are increasingly important concepts in computer science with great strides being made by large companies like Google and startups like DeepMind.
Brain, Deep Learning, DeepMind, Neural Networks, Nikhil Buduma
- Research Leaders on Data Mining, Data Science, and Big Data key trends, top papers - Jan 9, 2015.
We asked global research leaders in Data Science and Big Data what are the most interesting research papers/advances of 2014 and what are the key trends they see in 2015. Here are their answers.
Charu Aggarwal, Deep Learning, Eamonn Keogh, Healthcare, Jeff Ullman, Jian Pei, Jiawei Han, Mohammed Zaki
- Geoff Hinton AMA: Neural Networks, the Brain, and Machine Learning - Dec 9, 2014.
In a wide-ranging Q&A, Geoff Hinton addresses the future of deep learning, its biological inspirations, and his research philosophy.
Backpropagation, Deep Learning, Geoff Hinton, Michael Jordan, Neural Networks, Neuroscience, Zachary Lipton
- Geoffrey Hinton talks about Deep Learning, Google and Everything - Dec 1, 2014.
A review of Dr. Geoffrey Hinton’s Ask Me Anything on Reddit. He talked about his current research and his thought on some deep learning issues.
Deep Learning, DeepMind, Geoff Hinton, Google, Neural Networks, Reddit, Yann LeCun
- Will Deep Learning take over Machine Learning, make other algorithms obsolete? - Oct 27, 2014.
Will deep learning will take over machine learning and make other algorithms obsolete, or is it too complex to use on simpler problems? We look at both sides of this discussion.
Deep Learning, Machine Learning, Quora
- Top KDnuggets tweets, Sep 19-21: Dilbert funniest cartoons on #BigData, data mining; Guess which pattern is random - Sep 22, 2014.
Guess which pattern is random, which machine-generated? Dilbert 20 funniest cartoons on #BigData, data mining, privacy; Data Scientist Cartoon; Neural Networks and Deep Learning, free online book (draft).
Cartoon, Deep Learning, Dilbert, Free ebook, Neural Networks, Random
- Deep Learning – important resources for learning and understanding - Aug 21, 2014.
New and fundamental resources for learning about Deep Learning - the hottest machine learning method, which is approaching human performance level.
Deep Learning, Image Recognition, Machine Learning, Yann LeCun, Yoshua Bengio
- Interview: Pedro Domingos: the Master Algorithm, new type of Deep Learning, great advice for young researchers - Aug 19, 2014.
Top researcher Pedro Domingos on useful maxims for Data Mining, Machine Learning as the Master Algorithm, new type of Deep Learning called sum-product networks, Big Data and startups, and great advice to young researchers.
Advice, Deep Learning, KDD-2014, Machine Learning, Pedro Domingos, Startups
- Does Deep Learning Have Deep Flaws? - Jun 19, 2014.
A recent study of neural networks found that for every correctly classified image, one can generate an "adversarial", visually indistinguishable image that will be misclassified. This suggests potential deep flaws in all neural networks, including possibly a human brain.
Artificial Intelligence, Deep Learning, Google, Image Recognition, Neural Networks
- NYU Data Science Program – Things to Know - Jun 13, 2014.
Inside summary of NYU Data Science program launched last year, what it is, and what makes it special.
Data Science, Deep Learning, New York-NY, NYU, Ran Bi, Yann LeCun
- Where to Learn Deep Learning – Courses, Tutorials, Software - May 26, 2014.
Deep Learning is a very hot Machine Learning techniques which has been achieving remarkable results recently. We give a list of free resources for learning and using Deep Learning.
Andrew Ng, Deep Learning, Geoff Hinton, Machine Learning, Yann LeCun
- KDnuggets Exclusive: Interview with Yann LeCun, Deep Learning Expert, Director of Facebook AI Lab - Feb 20, 2014.
We discuss what enabled Deep Learning to achieve remarkable successes recently, his argument with Vapnik about (deep) neural nets vs kernel (support vector) machines, and what kind of AI can we expect from Facebook.
Andrew Ng, Deep Learning, Facebook, Interview, NYU, Support Vector Machines, Vladimir Vapnik, Yann LeCun
- Deep Learning Wins Dogs vs Cats competition on Kaggle - Feb 5, 2014.
A Deep learning expert wins Kaggle Dogs vs Cats image competition with an almost perfect result.
Cats, Competition, convnet, Deep Learning, Dogs, Facebook, Kaggle