- How Convolutional Neural Networks Work - Aug 31, 2016.
Get an overview of what is going on inside convolutional neural networks, and what it is that makes them so effective.
Pages: 1 2
Brandon Rohrer, Convolutional Neural Networks, Image Recognition, Neural Networks
- What is the Role of the Activation Function in a Neural Network? - Aug 30, 2016.
Confused as to exactly what the activation function in a neural network does? Read this overview, and check out the handy cheat sheet at the end.
Linear Regression, Logistic Regression, Neural Networks
- A Beginner’s Guide to Neural Networks with R! - Aug 11, 2016.
In this article we will learn how Neural Networks work and how to implement them with the R programming language! We will see how we can easily create Neural Networks with R and even visualize them. Basic understanding of R is necessary to understand this article.
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Beginners, Neural Networks, R, Udemy
- 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
- Multi-Task Learning in Tensorflow: Part 1 - Jul 20, 2016.
A discussion and step-by-step tutorial on how to use Tensorflow graphs for multi-task learning.
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Machine Learning, Neural Networks, TensorFlow
- 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
- 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
- Recursive (not Recurrent!) Neural Networks in TensorFlow - Jun 30, 2016.
Learn how to implement recursive neural networks in TensorFlow, which can be used to learn tree-like structures, or directed acyclic graphs.
Neural Networks, TensorFlow
- 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
- A Visual Explanation of the Back Propagation Algorithm for Neural Networks - Jun 17, 2016.
A concise explanation of backpropagation for neural networks is presented in elementary terms, along with explanatory visualization.
Algorithms, Backpropagation, Explanation, Machine Learning, Neural Networks
- 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
- 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
- Implementing Neural Networks in Javascript - May 12, 2016.
Javascript is one of the most prevalent and fastest growing languages in existence today. Get a quick introduction to implementing neural networks in the language, and direction on where to go from here.
Javascript, MNIST, Neural Networks
- 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
- 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.
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Bing Liu, Charu Aggarwal, Deep Learning, Ingo Mierswa, Internet of Things, IoT, Michael Berthold, Mohammed Zaki, Neural Networks, Padhraic Smyth, Pedro Domingos, Research, Trends
- 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.
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Apache Spark, Caffe, Deep Learning, Distributed Systems, H2O, Matthew Mayo, Neural Networks
- How do Neural Networks Learn? - Dec 2, 2015.
Neural networks are generating a lot of excitement, while simultaneously posing challenges to people trying to understand how they work. Visualize how neural nets work from the experience of implementing a real world project.
Backpropagation, Graph Visualization, 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
- 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.
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Convolutional Neural Networks, Deep Learning, Neural Networks, NLP
- 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.
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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
- 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
- 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
- Data Science 101: Preventing Overfitting in Neural Networks - Apr 17, 2015.
Overfitting is a major problem for Predictive Analytics and especially for Neural Networks. Here is an overview of key methods to avoid overfitting, including regularization (L2 and L1), Max norm constraints and Dropout.
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Neural Networks, Nikhil Buduma, Overfitting, Regularization
- 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
- 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
- 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
- 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
- Supermarket customers segmentation using Self-Organizing Mapping - Oct 23, 2014.
See how a leading European supermarket chain improved customer value and profitability and identified key customer groups by applying business intelligence and analytics techniques like self-organizing maps.
Business Intelligence, Clustering, Consumer Insights, Neural Networks
- Automotive Customer Churn Prediction Results, part 2 - Sep 29, 2014.
Learn how to apply neural networks and self-organizing maps to visualize the macroscopic relationships between clients and the maintenance evolution of cars over the years.
Churn, Gregory Philippatos, Neural Networks, Predictive Analytics, Visualization
- 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
- 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
- OpenNN, An Open Source Library For Neural Networks - Jun 2, 2014.
OpenNN is an open source class library written in C++ which implements neural networks, and runs on Windows, Apple, or Linux.
Neural Networks, Open Source, OpenNN
- Evolution of Fraud Analytics – An Inside Story - Mar 14, 2014.
The amazing analytic innovations in payment fraud prevention can be grouped into three major categories: large data-set modeling, sparse data-set modeling, and false-positive reductions - a view from the inside.
False positive, FICO, Fraud analytics, Fraud Prevention, Neural Networks, Sparse data
