- Let’s Admit It: We’re a Long Way from Using “Real Intelligence” in AI - Apr 19, 2018.
With the growth of AI systems and unstructured data, there is a need for an independent means of data curation, evaluation and measurement of output that does not depend on the natural language constructs of AI and creates a comparative method of how the data is processed.
- Robust Word2Vec Models with Gensim & Applying Word2Vec Features for Machine Learning Tasks - Apr 17, 2018.
The gensim framework, created by Radim Řehůřek consists of a robust, efficient and scalable implementation of the Word2Vec model.
- Top 10 Technology Trends of 2018 - Apr 13, 2018.
In this article, we will focus on the modern trends that took off well on the market by the end of 2017 and discuss the major breakthroughs expected in 2018.
- Understanding What is Behind Sentiment Analysis – Part 1 - Apr 13, 2018.
Build your first sentiment classifier in 3 steps.
- Implementing Deep Learning Methods and Feature Engineering for Text Data: The Skip-gram Model - Apr 10, 2018.
Just like we discussed in the CBOW model, we need to model this Skip-gram architecture now as a deep learning classification model such that we take in the target word as our input and try to predict the context words.
- Implementing Deep Learning Methods and Feature Engineering for Text Data: The Continuous Bag of Words (CBOW) - Apr 3, 2018.
The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words).
- Understanding Feature Engineering: Deep Learning Methods for Text Data - Mar 28, 2018.
Newer, advanced strategies for taming unstructured, textual data: In this article, we will be looking at more advanced feature engineering strategies which often leverage deep learning models.
- Text Data Preprocessing: A Walkthrough in Python - Mar 26, 2018.
This post will serve as a practical walkthrough of a text data preprocessing task using some common Python tools.
- Top KDnuggets tweets, Feb 21-27: Top 20 Python #AI and #MachineLearning Open Source Projects; Intro to Reinforcement Learning Algorithms - Feb 28, 2018.
Also: #NeuralNetwork #AI is simple. So... Stop pretending; 5 Free Resources for Getting Started with #DeepLearning for Natural Language Pro; Want a Job in #Data? Learn This
- 5 Fantastic Practical Natural Language Processing Resources - Feb 22, 2018.
This post presents 5 practical resources for getting a start in natural language processing, covering a wide array of topics and approaches.
- Top 15 Scala Libraries for Data Science in 2018 - Feb 9, 2018.
For your convenience, we have prepared a comprehensive overview of the most important libraries used to perform machine learning and Data Science tasks in Scala.
- Elasticsearch for Dummies - Jan 12, 2018.
In this blog, you’ll get to know the basics of Elasticsearch, its advantages, how to install it and indexing the documents using Elasticsearch.
- OpenMinTED Open Tender Phase II Funding opportunity for text and data mining developers - Jan 11, 2018.
OpenMinTED invites researchers, service providers and SMEs to submit proposals related to the development and integration of existing text mining/NLP applications or software components. Apply by Jan 26, 2018.
- A General Approach to Preprocessing Text Data - Dec 1, 2017.
Recently we had a look at a framework for textual data science tasks in their totality. Now we focus on putting together a generalized approach to attacking text data preprocessing, regardless of the specific textual data science task you have in mind.
- Natural Language Processing Library for Apache Spark – free to use - Nov 28, 2017.
Introducing the Natural Language Processing Library for Apache Spark - and yes, you can actually use it for free! This post will give you a great overview of John Snow Labs NLP Library for Apache Spark.
- Building a Wikipedia Text Corpus for Natural Language Processing - Nov 23, 2017.
Wikipedia is a rich source of well-organized textual data, and a vast collection of knowledge. What we will do here is build a corpus from the set of English Wikipedia articles, which is freely and conveniently available online.
- A Framework for Approaching Textual Data Science Tasks - Nov 22, 2017.
Although NLP and text mining are not the same thing, they are closely related, deal with the same raw data type, and have some crossover in their uses. Let's discuss the steps in approaching these types of tasks.
- Top KDnuggets tweets, Nov 08-14: Approaching (Almost) Any NLP Problem on #Kaggle; Choosing an Open Source #MachineLearning Library - Nov 15, 2017.
Also: What is the difference between Bagging and Boosting?; Which #Python package manager should you use?; The Practical Importance of Feature Selection.
- Top KDnuggets tweets, Oct 18-24: Chihuahua or muffin? The #DataScience Project Playbook - Oct 25, 2017.
Chihuahua or muffin? My search for the best computer vision API; Could #AI Be the Future of #FakeNews and Product Reviews? 7 Types of Artificial #NeuralNetworks for NLP.
- Data Science Bootcamp in Zurich, Switzerland, January 15 – April 6, 2018 - Oct 12, 2017.
Come to the land of chocolate and Data Science where the local tech scene is booming and the jobs are a plenty. Learn the most important concepts from top instructors by doing and through projects. Use code KDNUGGETS to save.
- How to win Kaggle competition based on NLP task, if you are not an NLP expert - Sep 29, 2017.
Here is how we got one of the best results in a Kaggle challenge remarkable for a number of interesting findings and controversies among the participants.
Pages: 1 2
- Machine Learning Reveals 9 Elements of Deal-Closing Sales - Sep 26, 2017.
The data science team at Gong.io analyzed over 67,000 sales calls/demos to understand the patterns that close deals. Here is what we found.
Pages: 1 2
- Top KDnuggets tweets, Sep 13-19: Top Books on NLP; What Else Can AI Guess From Your Face? - Sep 20, 2017.
Also: The Ten Fallacies of Data Science; #Python #Pandas tips and tricks; Geoff Hinton says we need to start all over.
- I built a chatbot in 2 hours and this is what I learned - Sep 7, 2017.
I set out to test two things: 1) building a bot is useless from a business perspective and 2) building bots is crazy tough. Here is what I learned.
Pages: 1 2
- Search Millions of Documents for Thousands of Keywords in a Flash - Sep 1, 2017.
We present a python library called FlashText that can search or replace keywords / synonyms in documents in O(n) – linear time.
- O’Reilly NYC AI Conference Highlights: Explainable AI, Vector Representation, Bias, and Future - Aug 21, 2017.
The answer to questions of trust and bias in AI is largely seen in the focus on Explainable AI. Although traditionally viewed as "black boxes", AI and machine learning systems are not ontologically inscrutable.
- PayPal: Applied Research Scientist (AI-ML R&D / NLP / Deep Learning) - Aug 18, 2017.
Seeking an Applied Research Scientist to work on deep learning research for multiple data science applications within the company. There will be access to huge amount of internal data and lots of opportunities to innovate.
- 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.
- Top KDnuggets tweets, Jul 19-25: 5 Free Resources for Getting Started with #DeepLearning for NLP; 10 Free Must-Read Books for ML, DS - Jul 26, 2017.
Also: 10 Free Must-Read Books for #MachineLearning and #DataScience; 4 cases when not to use #DeepLearning; #Internet speed and cost by country
- KDnuggets™ News 17:n28, Jul 26: 5 Free Resources to start with Deep Learning for NLP; Emotional Intelligence for Data Science Teams - Jul 26, 2017.
Also AI and Deep Learning, Explained Simply; When not to use deep learning; Optimism for AI drop with experience developing AI systems.
- 5 Free Resources for Getting Started with Deep Learning for Natural Language Processing - Jul 19, 2017.
This is a collection of 5 deep learning for natural language processing resources for the uninitiated, intended to open eyes to what is possible and to the current state of the art at the intersection of NLP and deep learning. It should also provide some idea of where to go next.
- Text Mining 101: Mining Information From A Resume - May 24, 2017.
We show a framework for mining relevant entities from a text resume, and how to separation parsing logic from entity specification.
- AIA Group: Natural Language Processing (NLP) Engineer - May 22, 2017.
Responsible for leveraging ML and Natural Language Processing (NLP) techniques to build solutions to better insurance processes and business model benefiting both internal and external stakeholders and creating the next generation insurance platform.
- How Deep Learning Is Changing The Finance and Retail Sectors - May 11, 2017.
Explore the latest advancements in deep learning and their applications in industry at the Deep Learning in Finance Summit and Deep Learning in Retail Summit in London, 1-2 June. Use discount code KDNUGGETS to save 20% off all tickets.
- Using Deep Learning To Extract Knowledge From Job Descriptions - May 9, 2017.
We present a deep learning approach to extract knowledge from a large amount of data from the recruitment space. A learning to rank approach is followed to train a convolutional neural network to generate job title and job description embeddings.
- Top KDnuggets tweets, Apr 12-18: 10 Free Must-Read Books for #MachineLearning and #DataScience - Apr 19, 2017.
Also Modern NLP in Python, or What you can learn about food by analyzing a million Yelp reviews; The Periodic Table of #DataScience; What is #Blockchain Technology?
- Text Analytics: A Primer - Mar 14, 2017.
Marketing scientist Kevin Gray asks Professor Bing Liu to give us a quick snapshot of text analytics in this informative interview.
- Top /r/MachineLearning Posts, February: Oxford Deep NLP Course; Data Visualization for Scikit-learn Results - Mar 6, 2017.
Oxford Deep NLP Course; scikit-plot: Data Visualization for Scikit-learn Results; Machine Learning at Berkeley's ML Crash Course: Neural Networks; Predicting parking difficulty with machine learning; TensorFlow 1.0 Release
- Introduction to Natural Language Processing, Part 1: Lexical Units - Feb 16, 2017.
This series explores core concepts of natural language processing, starting with an introduction to the field and explaining how to identify lexical units as a part of data preprocessing.
- Natural Language Processing Key Terms, Explained - Feb 16, 2017.
This post provides a concise overview of 18 natural language processing terms, intended as an entry point for the beginner looking for some orientation on the topic.
- Top KDnuggets tweets, Feb 08-14: 5 Free Courses for Getting Started in AI; Deep Learning for NLP at Oxford, course materials - Feb 15, 2017.
5 Free Courses for Getting Started in #AI; Python #DataScience tutorial: Making #Python Speak #SQL with pandasql; Course materials: #DeepLearning for Natural Language Processing at Oxford; Resources for Learning AI, courtesy of McGill #AI Society.
- 50+ Useful Machine Learning & Prediction APIs, updated - Feb 8, 2017.
Very useful, updated list of 50+ APIs in machine learning, prediction, text analytics & classification, face recognition, language translation, and more.
- Deep Learning Research Review: Natural Language Processing - Jan 31, 2017.
This edition of Deep Learning Research Review explains recent research papers in Natural Language Processing (NLP). If you don't have the time to read the top papers yourself, or need an overview of NLP with Deep Learning, this post is for you.
Pages: 1 2 3
- Deep Learning Can be Applied to Natural Language Processing - Jan 16, 2017.
This post is a rebuttal to a recent article suggesting that neural networks cannot be applied to natural language given that language is not a produced as a result of continuous function. The post delves into some additional points on deep learning as well.
- Social Media for Marketing and Healthcare: Focus on Adverse Side Effects - Jan 9, 2017.
Social media like twitter, facebook are very important sources of big data on the internet and using text mining, valuable insights about a product or service can be found to help marketing teams. Lets see, how healthcare companies are using big data and text mining to improve their marketing strategies.
- An NLP Approach to Analyzing Twitter, Trump, and Profanity - Nov 3, 2016.
Who swears more? Do Twitter users who mention Donald Trump swear more than those who mention Hillary Clinton? Let’s find out by taking a natural language processing approach (or, NLP for short) to analyzing tweets.
Pages: 1 2
- SlangSD: A Sentiment Dictionary for Slang Words - Sep 14, 2016.
The Slang Sentiment Dictionary (SlangSD) includes over 90,000 slang words together with their sentiment scores, facilitating sentiment analysis in user-generated contents.
- Exploring Social Media Diversity with Natural Language Processing - Aug 10, 2016.
This post uses natural language processing on Twitter data to determine the diversity of Twitter accounts the author is following. An innovative take on social media analytics.
Pages: 1 2
- America’s Next Topic Model - Jul 15, 2016.
Topic modeling is a a great way to get a bird's eye view on a large document collection using machine learning. Here are 3 ways to use open source Python tool Gensim to choose the best topic model.
- NLP, Sentiment Analysis, Consumer and Market Insights at SAS16 - Jul 5, 2016.
The next Sentiment Analysis Symposium (the premier industry event) takes place July 12 in New York. Register today with your 10% KDnuggets discount!
- 5 More Machine Learning Projects You Can No Longer Overlook - Jun 28, 2016.
There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects.
- The Amazing Power of Word Vectors - May 18, 2016.
A fantastic overview of several now-classic papers on word2vec, the work of Mikolov et al. at Google on efficient vector representations of words, and what you can do with them.
Pages: 1 2
- PocketConfidant AI: Computational Linguist (NLP/AI) - Mar 26, 2016.
Rely on the user behavior data to design and implement Machine Learning algorithms and methods of Natural Language Processing to build smart conversational robots. Make user experience personal, proactive and empathetic.
- Around the World in 60 Days: Getting Deep Speech to Work in Mandarin - Feb 24, 2016.
Baidu continues to make impressive gains with deep learning. Their latest achievement centers on Mandarin speech recognition, which you can read about here from the researchers involved in the project.
Pages: 1 2
- Elementary, My Dear Watson! An Introduction to Text Analytics via Sherlock Holmes - Feb 12, 2016.
Want to learn about the field of text mining, go on an adventure with Sherlock & Watson. Here you will find what are different sub-domains of text mining along with a practical example.
- The Top A.I. Breakthroughs of 2015 - Feb 2, 2016.
Learn about the biggest developments of 2015 in the field of Artificial Intelligence.
Pages: 1 2 3
- 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
- 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.
Pages: 1 2
- Everything You Need to Know about Natural Language Processing - Dec 21, 2015.
Natural language processing (NLP) helps computers understand human speech and language. We define the key NLP concepts and explain how it fits in the bigger picture of Artificial Intelligence.
- 50 Useful Machine Learning & Prediction APIs - Dec 7, 2015.
We present a list of 50 APIs selected from areas like machine learning, prediction, text analytics & classification, face recognition, language translation etc. Start consuming APIs!
Pages: 1 2
- Sentiment Analysis 101 - Dec 3, 2015.
Sentiment analysis can be incredibly useful, and can help companies better answer pertinent questions and gain valuable business insights. Sentiment analysis technologies will continue to improve as they become more widely adopted. But what can sentiment analysis do for you?
- 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.
- 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
- An Inside View of Language Technologies at Google - Oct 29, 2015.
Learn about language technologies at Google, including projects, technologies, and philosophy, from an interview with a Googler.
Pages: 1 2
- DistrictDataLabs Courses on Data Mining, Machine Learning, R, NLP, Social Media, and more - Oct 17, 2015.
District Data Labs upcoming workshops and courses include Data Mining & Machine Learning with R, Building a Django Data Product, Analyzing Social Media Data with R, and Natural Language Processing with R.
- 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
- Top KDnuggets tweets, Sep 15-21: Top Machine Learning researcher Pedro Domingos new book: The Master Algorithm - Sep 23, 2015.
Top Machine Learning researcher Pedro Domingos new book: The Master #Algorithm; #Dilbert brilliant take on Character; SentimentBuilder - Free Online Natural #Language Processing Tool.
- SAS: Machine Learning Algorithm Research/Developer - Jul 15, 2015.
Developer with a strong analytical background and excellent programming skills to collaborate with a team developing new machine learning algorithms for NLP, text classification, sentiment analysis, and similar tasks.
- Math of Ideas: A Word is Worth a Thousand Vectors - Apr 16, 2015.
Word vectors give us a simple and flexible platform for understanding text, there are a few diverse examples that should help build your confidence in developing and deploying NLP systems and what problems they can solve.
Pages: 1 2 3
- Top /r/MachineLearning Posts, Mar 29-Apr 4: Andrew Ng AMA, Deep Learning for NLP, and OpenCL Convnets - Apr 10, 2015.
Andrew Ng's upcoming AMA, scikit-learn updates, Richard Socher's Deep Learning NLP videos, Criteo's huge new dataset, and convolutional neural networks on OpenCL are the top topics discussed this week on /r/MachineLearning.
- Interview: Xia Wang, AstraZeneca on Unraveling Patient Treatment Journey by NLP on Clinical Notes - Apr 9, 2015.
We discuss Analytics at AstraZeneca, prominent use cases, how NLP helped understanding patient treatment journey in diabetes, data sources, insights, and more.
- Awesome Public Datasets on GitHub - Apr 6, 2015.
A long, categorized list of large datasets (available for public use) to try your analytics skills on. Which one would you pick?
Pages: 1 2
- Text By the Bay conference, San Francisco, Apr 24-25 - Apr 2, 2015.
The inaugural Text By the Bay conference has an amazing program, with speakers from top universities, Big text data powerhouses, Growing global players, Startups, Text/NLP tech providers, and more. KDnuggets discount.
- Top /r/MachineLearning Posts, Mar 8-14: Word vectors, Hardware for Deep Learning, and Neural Graphics Engines - Mar 19, 2015.
Word vectors in NLP, Machine Learning's place in programming, hardware for deep learning, Machine Learning interviews, and neural graphics engines are all topics covered this week on /r/MachineLearning.
- Machine Learning Table of Elements Decoded - Mar 11, 2015.
Machine learning packages for Python, Java, Big Data, Lua/JS/Clojure, Scala, C/C++, CV/NLP, and R/Julia are represented using a cute but ill-fitting metaphor of a periodic table. We extract the useful links.
- Higher Up Recruiting: Data Scientists, Data Miners, NLP Experts - Oct 28, 2014.
Higher Up Recruiting is working with a San Diego based company recruiting for both Contract and Direct Hire positions for a Biotech related company in San Diego.
- Top stories for Oct 19-25: Ebola Data Science Lessons; DM Radio, Oct 30 on Predictive Tools with KDnuggets, Predixion - Oct 26, 2014.
Ebola Analytics and Data Science Lessons; DM Radio: Predictive Tools Are Pervasive, with KDnuggets, Predixion, RedPoint, and Appnomic, Oct 30; Big Data for Social Good IBM + Hadoop Challenge; TweetNLP: Twitter Natural Language Processing.
- Top KDnuggets tweets, Oct 22-23: Baidu revenue jumps after Deep Learning use; Great viz: chess piece survival - Oct 24, 2014.
Great viz: chances of survival of #chess pieces in average game; Baidu, 'Chinese Google', had big revenue jump after it started using Deep Learning; 4 ways to become a Data Scientist w/out a PhD; Machine-Learning expert Michael Jordan on the Delusions of #BigData.
- TweetNLP: Twitter Natural Language Processing - Oct 24, 2014.
A short overview of Natural Language Processing tools and utilities developed by Prof. Noah Smith, CMU and his team to analyze Twitter data.
- TU-Darmstadt: Postdocs in Statistical NLP, IR, Machine Learning - May 24, 2014.
Researcher with a background in statistical NLP, machine learning, or IR for a postdoc position working to combine large-scale knowledge bases with semantic information from large amounts of text.
- Top KDnuggets tweets, Apr 25-27: Recommended Tutorials for Data Scientists; How One Woman Hid Her Pregnancy from Big Data - Apr 28, 2014.
Recommended Tutorials for Data Scientists from PyCon 2014; How One Woman Hid Her Pregnancy from #BigData; MLTK: Machine Learning Toolkit in Java - free download; Deep Learning for Natural Language Processing.
- Educational Testing Service: Research Director NLP, Speech Processing - Mar 12, 2014.
Lead a team of 26 scientists and engineers in the research and development of fundamental and applied research in NLP and speech processing to address current needs and anticipate future needs of education and assessment.
- Top KDnuggets tweets, Mar 7-9: Experiments with Twitter and IPython; Cloudera Data Scientist Solution Kit - Mar 10, 2014.
Learn very useful skills! #DataScience Experiments with Twitter and IPython; Cloudera Data Scientist Solution Kit; For data science hackers: combining Emacs, ESS and R for Zombies; Mashape - Free Natural Language Processing Service.
- Top KDnuggets tweets, Jan 8-9: Great list of NLP APIs; Python erodes R hegemony, but do not go all-in Python now - Jan 10, 2014.
Great list of 25+ NLP APIs for Sentiment Analysis, Text Processing, Topic Extraction; MLbase: Distributed Machine Learning using Apache Spark; "Sexy" Data Science should be a Team Sport, or it will fail ; LinkedIn files lawsuit over data-mining bots which mine user profiles