- A Friendly Introduction to Graph Neural Networks - Nov 30, 2020.
Despite being what can be a confusing topic, graph neural networks can be distilled into just a handful of simple concepts. Read on to find out more.
- Recurrent Neural Networks (RNN): Deep Learning for Sequential Data - Jul 20, 2020.
Recurrent Neural Networks can be used for a number of ways such as detecting the next word/letter, forecasting financial asset prices in a temporal space, action modeling in sports, music composition, image generation, and more.
- Interactive Machine Learning Experiments - May 26, 2020.
Dive into experimenting with machine learning techniques using this open-source collection of interactive demos built on multilayer perceptrons, convolutional neural networks, and recurrent neural networks. Each package consists of ready-to-try web browser interfaces and fully-developed notebooks for you to fine tune the training for better performance.
- LSTM for time series prediction - Apr 27, 2020.
Learn how to develop a LSTM neural network with PyTorch on trading data to predict future prices by mimicking actual values of the time series data.
- A Comprehensive Guide to Natural Language Generation - Jan 7, 2020.
Follow this overview of Natural Language Generation covering its applications in theory and practice. The evolution of NLG architecture is also described from simple gap-filling to dynamic document creation along with a summary of the most popular NLG models.
- Deep Learning for NLP: ANNs, RNNs and LSTMs explained! - Aug 7, 2019.
Learn about Artificial Neural Networks, Deep Learning, Recurrent Neural Networks and LSTMs like never before and use NLP to build a Chatbot!
- Examining the Transformer Architecture – Part 2: A Brief Description of How Transformers Work - Jul 2, 2019.
As The Transformer may become the new NLP standard, this review explores its architecture along with a comparison to existing approaches by RNN.
- Understanding Backpropagation as Applied to LSTM - May 30, 2019.
Backpropagation is one of those topics that seem to confuse many once you move past feed-forward neural networks and progress to convolutional and recurrent neural networks. This article gives you and overall process to understanding back propagation by giving you the underlying principles of backpropagation.
- KDnuggets™ News 19:n17, May 1: The most desired skill in data science; Seeking KDnuggets Editors, work remotely - May 1, 2019.
This week, find out about the most desired skill in data science, learn which projects to include in your portfolio, identify a single strategy for pulling data from a Pandas DataFrame (once and for all), read the results of our Top Data Science and Machine Learning Methods poll, and much more.
- Attention Craving RNNS: Building Up To Transformer Networks - Apr 24, 2019.
RNNs let us model sequences in neural networks. While there are other ways of modeling sequences, RNNs are particularly useful. RNNs come in two flavors, LSTMs (Hochreiter et al, 1997) and GRUs (Cho et al, 2014)
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- Getting started with NLP using the PyTorch framework - Apr 3, 2019.
We discuss the classes that PyTorch provides for helping with Natural Language Processing (NLP) and how they can be used for related tasks using recurrent layers.
- Deep Learning for Natural Language Processing (NLP) – using RNNs & CNNs - Feb 21, 2019.
We investigate several Natural Language Processing tasks and explain how Deep Learning can help, looking at Language Modeling, Sentiment Analysis, Language Translation, and more.
- Sequence Modeling with Neural Networks – Part I - Oct 3, 2018.
In the context of this post, we will focus on modeling sequences as a well-known data structure and will study its specific learning framework.
- ODSC India Highlights: Deep Learning Revolution in Speech, AI Engineer vs Data Scientist, and Reinforcement Learning for Enterprise - Sep 26, 2018.
Key takeaways and highlights from ODSC India 2018 conference about the latest trends, breakthroughs and revolutions in the field of Data Science and Artificial Intelligence
- KDnuggets™ News 18:n24, Jun 20: Data Lakes – The evolution of data processing; Text Generation with RNNs in 4 Lines of Code - Jun 20, 2018.
How to spot a beginner Data Scientist; How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning; Statistics, Causality, and What Claims are Difficult to Swallow: Judea Pearl debates Kevin Gray; Cartoon: FIFA World Cup Football and Machine Learning
- Taming LSTMs: Variable-sized mini-batches and why PyTorch is good for your health - Jun 14, 2018.
After reading this, you’ll be back to fantasies of you + PyTorch eloping into the sunset while your Recurrent Networks achieve new accuracies you’ve only read about on Arxiv.
- Generating Text with RNNs in 4 Lines of Code - Jun 14, 2018.
Want to generate text with little trouble, and without building and tuning a neural network yourself? Let's check out a project which allows you to "easily train your own text-generating neural network of any size and complexity on any text dataset with a few lines of code."
- 5 Machine Learning Projects You Should Not Overlook, June 2018 - Jun 12, 2018.
Here is a new installment of 5 more machine learning or machine learning-related projects you may not yet have heard of, but may want to consider checking out!
- Using Genetic Algorithm for Optimizing Recurrent Neural Networks - Jan 22, 2018.
In this tutorial, we will see how to apply a Genetic Algorithm (GA) for finding an optimal window size and a number of units in Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN).
- Exploring Recurrent Neural Networks - Dec 1, 2017.
We explore recurrent neural networks, starting with the basics, using a motivating weather modeling problem, and implement and train an RNN in TensorFlow.
- 7 Steps to Mastering Deep Learning with Keras - Oct 30, 2017.
Are you interested in learning how to use Keras? Do you already have an understanding of how neural networks work? Check out this lean, fat-free 7 step plan for going from Keras newbie to master of its basics as quickly as is possible.
- A Guide For Time Series Prediction Using Recurrent Neural Networks (LSTMs) - Oct 5, 2017.
Looking at the strengths of a neural network, especially a recurrent neural network, I came up with the idea of predicting the exchange rate between the USD and the INR.
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- New-Age Machine Learning Algorithms in Retail Lending - Sep 13, 2017.
We review the application of new age Machine Learning algorithms for better Customer Analytics in Lending and Credit Risk Assessment.
- Going deeper with recurrent networks: Sequence to Bag of Words Model - Aug 8, 2017.
Deep learning makes it possible to convert unstructured text to computable formats, incorporating semantic knowledge to train machine learning models. These digital data troves help us understand people on a new level.
- Using the TensorFlow API: An Introductory Tutorial Series - Jun 28, 2017.
This post summarizes and links to a great multi-part tutorial series on learning the TensorFlow API for building a variety of neural networks, as well as a bonus tutorial on backpropagation from the beginning.
- Building, Training, and Improving on Existing Recurrent Neural Networks - May 8, 2017.
In this post, we’ll provide a short tutorial for training a RNN for speech recognition, including code snippets throughout.
- How to Build a Recurrent Neural Network in TensorFlow - Apr 26, 2017.
This is a no-nonsense overview of implementing a recurrent neural network (RNN) in TensorFlow. Both theory and practice are covered concisely, and the end result is running TensorFlow RNN code.
- 3 practical thoughts on why deep learning performs so well - Feb 3, 2017.
Why does Deep Learning perform better than other machine learning methods? We offer 3 reasons: integration of integration of feature extraction within the training process, collection of very large data sets, and technology development.
- Deep Learning Key Terms, Explained - Oct 12, 2016.
Gain a beginner's perspective on artificial neural networks and deep learning with this set of 14 straight-to-the-point related key concept definitions, including Biological Neuron, Multilayer Perceptron (MLP), Feedforward Neural Network, and Recurrent Neural Network.
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- Introduction to Recurrent Networks in TensorFlow - May 31, 2016.
A straightforward, introductory overview of implementing Recurrent Neural Networks in TensorFlow.
- 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?
- 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.
- Top KDnuggets tweets, Jan 11-24: Why R Users will inevitably become #Bayesians; Is #Quran really more violent that #Bible? - Jan 25, 2016.
TextAnalytics examines: Is #Quran really more violent that #Bible? Why R Users will inevitably become #Bayesians; Next #MachineLearning problem: what to do with 80% accurate algorithm? ;Learning to Code #NeuralNetworks #MachineLearning Tutorial;
- 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.
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- 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!
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- Top 5 Deep Learning Resources, January - Jan 7, 2016.
There is an increasing volume of deep learning research, articles, blog posts, and news constantly emerging. Our Deep Learning Reading List aims to make this information easier to digest.
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- 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.
- Deep Learning, Language Understanding, and the Quest for Human Capacity Cognitive Computing - Nov 16, 2015.
To develop cognitive computing at human capacity understanding, deep learning research must heed what certain aspects of human symbol processing reveal about the architecture of the human mind.
- Recurrent Neural Net describes images like Taylor Swift or Romantic Novel - Nov 14, 2015.
Deep learning has recently, and famously, taken on painting by imitating artists. We now find recurrent neural networks writing stories corresponding to images, in the style of romance novels or Taylor Swift lyrics.
- 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.
- 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.
- 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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- KDnuggets™ News 15:n21, Jul 1: Top 20 R packages; Using Ensembles in Kaggle; Tutorials and How-Tos - Jul 1, 2015.
Top 20 R packages by popularity; Tutorials, Overviews, How-Tos; Open Source Enabled Interactive Analytics; Using Ensembles in Kaggle Data Science Competitions.
- Top KDnuggets tweets, Jun 22-29: Kaggle Machine Learning Tutorial in R; 50 Smartest Companies – shaping the #technology landscape - Jun 30, 2015.
Free @Kaggle #MachineLearning Tutorial in R - learn how to compete; 50 Smartest Companies - shaping the #technology landscape; Excellent Tutorial on #Sequence #Learning using #Recurrent #Neural #Networks; How a #DataScientist buys a #car.
- Excellent Tutorial on Sequence Learning using Recurrent Neural Networks - Jun 26, 2015.
Excellent tutorial explaining Recurrent Neural Networks (RNNs) which hold great promise for learning general sequences, and have applications for text analysis, handwriting recognition and even machine translation.
- Top /r/MachineLearning Posts, May: Unreasonable Effectiveness of Recurrent Neural Networks, Time-Lapse Mining - Jun 1, 2015.
The Unreasonable Effectiveness of Recurrent Neural Networks, Time-lapse mining from Net photos, Deep Learning Textbook Part I, Kaggle R Tutorial, and Free Machine Learning ebooks.
- Top /r/MachineLearning posts, Jan 18-24: K-means clustering is not a free lunch; A Deep Dive into Recurrent Neural Nets - Jan 26, 2015.
Textbook Easter Eggs, issues with k-means, recurrent neural networks, genetic algorithm challenges, and the implementation of machine learning pipelines are all in this week's top /r/MachineLearning posts.
- Top KDnuggets tweets, Jan 12-18: Dilbert looks at #analytics of #dating and A/B testing; Deep Learning and Human Beings - Jan 19, 2015.
Dilbert looks at #analytics of #dating and A/B testing; A Deep Dive into Recurrent Neural Nets #DeepLearning; Great read: #Visualizing Representations: #DeepLearning and Human Beings; 9 Lessons: Picking the Right #NoSQL Tools.
- Top KDnuggets tweets, Jan 14-15: 10 FB likes predicts personality better than a co-worker; A Deep Dive into Recurrent Neural Nets - Jan 16, 2015.
A Deep Dive into Recurrent Neural Nets #DeepLearning; SOASTA announces #DataScience Workbench for insights from user experience; What's Wrong with this Picture? The Art of Honest Visualizations; Deep Learning can be easily fooled.
- Deep Learning RNNaissance, an insightful, comprehensive, and entertaining overview - Oct 9, 2014.
Watch this great overview of history and present state of Deep Learning, which is revolutionizing Machine learning, vision, robotics, and many other areas.