- Natural Language Processing for Social Media - Feb 12, 2019.
Marketing scientist Kevin Gray asks Dr. Anna Farzindar of the University of Southern California about Natural Language Processing and how it is used in social media analytics.
- How I used NLP (Spacy) to screen Data Science Resumes - Feb 6, 2019.
A real life example of when using NLP can help filter down a list of candidates for a job opening, with full source code and methodology.
- ELMo: Contextual Language Embedding - Jan 31, 2019.
Create a semantic search engine using deep contextualised language representations from ELMo and why context is everything in NLP.
- Building an image search service from scratch - Jan 30, 2019.
By the end of this post, you should be able to build a quick semantic search model from scratch, no matter the size of your dataset.
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- What were the most significant machine learning/AI advances in 2018? - Jan 22, 2019.
2018 was an exciting year for Machine Learning and AI. We saw “smarter” AI, real-world applications, improvements in underlying algorithms and a greater discussion on the impact of AI on human civilization. In this post, we discuss some of the highlights.
- 10 Exciting Ideas of 2018 in NLP - Jan 16, 2019.
We outline a selection of exciting developments in NLP from the last year, and include useful recent papers and images to help further assist with your learning.
- Word Embeddings & Self-Supervised Learning, Explained - Jan 16, 2019.
There are many algorithms to learn word embeddings. Here, we consider only one of them: word2vec, and only one version of word2vec called skip-gram, which works well in practice.
- KDnuggets™ News 19:n03, Jan 16: Top 10 Books on NLP and Text Analysis; End To End Guide For Machine Learning Projects - Jan 16, 2019.
Also: Why Vegetarians Miss Fewer Flights - Five Bizarre Insights from Data; 4 Myths of Big Data and 4 Ways to Improve with Deep Data; The Role of the Data Engineer is Changing; How to solve 90% of NLP problems: a step-by-step guide
- How to solve 90% of NLP problems: a step-by-step guide - Jan 14, 2019.
Read this insightful, step-by-step article on how to use machine learning to understand and leverage text.
- Top 10 Books on NLP and Text Analysis - Jan 9, 2019.
When it comes to choosing the right book, you become immediately overwhelmed with the abundance of possibilities. In this review, we have collected our Top 10 NLP and Text Analysis Books of all time, ranging from beginners to experts.
- NLP Overview: Modern Deep Learning Techniques Applied to Natural Language Processing - Jan 8, 2019.
Trying to keep up with advancements at the overlap of neural networks and natural language processing can be troublesome. That's where the today's spotlighted resource comes in.
- Comparison of the Text Distance Metrics - Jan 7, 2019.
There are many different approaches of how to compare two texts (strings of characters). Each has its own advantages and disadvantages and is good only for a range of specific use cases.
- Comparison of the Top Speech Processing APIs - Dec 28, 2018.
There are two main tasks in speech processing. First one is to transform speech to text. The second is to convert the text into human speech. We will describe the general aspects of each API and then compare their main features in the table.
- BERT: State of the Art NLP Model, Explained - Dec 26, 2018.
BERT’s key technical innovation is applying the bidirectional training of Transformer, a popular attention model, to language modelling. It has caused a stir in the Machine Learning community by presenting state-of-the-art results in a wide variety of NLP tasks.
- 10 More Must-See Free Courses for Machine Learning and Data Science - Dec 20, 2018.
Have a look at this follow-up collection of free machine learning and data science courses to give you some winter study ideas.
- KDnuggets™ News 18:n48, Dec 19: Why You Shouldn’t be a Data Science Generalist; Industry Data Science & Machine Learning 2019 Predictions - Dec 19, 2018.
Also: Top Stories of 2018; NLP Breakthrough Imagenet Moment has arrived; Four Approaches to Explaining AI and Machine Learning; Solve any Image Classification Problem Quickly and Easily
- NLP Breakthrough Imagenet Moment has arrived - Dec 14, 2018.
A comprehensive review of the current state of Natural Language Processing, covering the process from shallow to deep pre-training, what's in an ImageNet, the case for language modelling, and more.
- State of Deep Learning and Major Advances: H2 2018 Review - Dec 13, 2018.
In this post we summarise some of the key developments in deep learning in the second half of 2018, before briefly discussing the road ahead for the deep learning community.
- P&G: Data Scientist – Machine Learning/NLP [Cincinnati, OH] - Dec 11, 2018.
P&G is seeking a Data Scientist - Machine Learning/NLP in Cincinnati, OH. In this role you will have multiple projects on which you will leverage machine learning tools to solve these types of problems.
- Introduction to Named Entity Recognition - Dec 11, 2018.
Named Entity Recognition is a tool which invariably comes handy when we do Natural Language Processing tasks. Read on to find out how.
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- Word Morphing – an original idea - Nov 20, 2018.
In this post, we describe how to utilise word2vec's embeddings and A* search algorithm to morph between words.
- Sorry I didn’t get that! How to understand what your users want - Nov 16, 2018.
Creating a chatbot is difficult, it involves knowledge in many AI-Hard tasks, such as Natural Language Understanding, Machine Comprehension, Inference, or Automatic Language Generation (in fact, solving these tasks is close to solving AI) and large human effort is required.
- Multi-Class Text Classification with Doc2Vec & Logistic Regression - Nov 9, 2018.
Doc2vec is an NLP tool for representing documents as a vector and is a generalizing of the word2vec method. In order to understand doc2vec, it is advisable to understand word2vec approach.
- 10 Free Must-See Courses for Machine Learning and Data Science - Nov 8, 2018.
Check out a collection of free machine learning and data science courses to kick off your winter learning season.
- KDnuggets™ News 18:n42, Nov 7: The Most in Demand Skills for Data Scientists; How Machines Understand Our Language: Intro to NLP - Nov 7, 2018.
Also: Machine Learning Classification: A Dataset-based Pictorial; Quantum Machine Learning: A look at myths, realities, and future projections; Multi-Class Text Classification Model Comparison and Selection; Top 13 Python Deep Learning Libraries
- Text Preprocessing in Python: Steps, Tools, and Examples - Nov 6, 2018.
We outline the basic steps of text preprocessing, which are needed for transferring text from human language to machine-readable format for further processing. We will also discuss text preprocessing tools.
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- Multi-Class Text Classification Model Comparison and Selection - Nov 1, 2018.
This is what we are going to do today: use everything that we have presented about text classification in the previous articles (and more) and comparing between the text classification models we trained in order to choose the most accurate one for our problem.
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- Labeling Unstructured Text for Meaning to Achieve Predictive Lift - Oct 31, 2018.
In this post, we examine several advance NLP techniques, including: labeling nouns and noun phrases for meaning, labeling (most often) adverbs and adjectives for sentiment, and labeling verbs for intent.
- How Machines Understand Our Language: An Introduction to Natural Language Processing - Oct 31, 2018.
The applications of NLP are endless. This is how a machine classifies whether an email is spam or not, if a review is positive or negative, and how a search engine recognizes what type of person you are based on the content of your query to customize the response accordingly.
- KDnuggets™ News 18:n41, Oct 31: Introduction to Deep Learning with Keras; Easy Named Entity Recognition with Scikit-Learn - Oct 31, 2018.
Also: Generative Adversarial Networks - Paper Reading Road Map; How I Learned to Stop Worrying and Love Uncertainty; Implementing Automated Machine Learning Systems with Open Source Tools; Notes on Feature Preprocessing: The What, the Why, and the How
- Named Entity Recognition and Classification with Scikit-Learn - Oct 25, 2018.
Named Entity Recognition and Classification is a process of recognizing information units like names, including person, organization and location names, and numeric expressions from unstructured text. The goal is to develop practical and domain-independent techniques in order to detect named entities with high accuracy automatically.
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- Monash University: Academic Opportunities in Dialogue Research [Melbourne, Australia] - Oct 25, 2018.
Monash University is seeking to fill multiple academic opportunities in Dialogue Research in Melbourne, Australia: Level B Lecturer (equivalent to Assistant Professor in North America), Level C Senior Lecturer (equivalent to Associate Professor in North America).
- Building a Question-Answering System from Scratch - Oct 24, 2018.
This part will focus on introducing Facebook sentence embeddings and how it can be used in building QA systems. In the future parts, we will try to implement deep learning techniques, specifically sequence modeling for this problem.
- The Main Approaches to Natural Language Processing Tasks - Oct 17, 2018.
Let's have a look at the main approaches to NLP tasks that we have at our disposal. We will then have a look at the concrete NLP tasks we can tackle with said approaches.
- GitHub Python Data Science Spotlight: High Level Machine Learning & NLP, Ensembles, Command Line Viz & Docker Made Easy - Oct 16, 2018.
This post spotlights 5 data science projects, all of which are open source and are present on GitHub repositories, focusing on high level machine learning libraries and low level support tools.
- 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.
- KDnuggets™ News 18:n37, Oct 3: Mathematics of Machine Learning; Effective Transfer Learning for NLP; Path Analysis with R - Oct 3, 2018.
Also: Introducing VisualData: A Search Engine for Computer Vision Datasets; Raspberry Pi IoT Projects for Fun and Profit; Recent Advances for a Better Understanding of Deep Learning; Basic Image Data Analysis Using Python - Part 3; Introduction to Deep Learning
- More Effective Transfer Learning for NLP - Oct 1, 2018.
Until recently, the natural language processing community was lacking its ImageNet equivalent — a standardized dataset and training objective to use for training base models.
- 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
- Beyond Refuge: Natural Language Understanding Engineer [Remote Position] - Sep 25, 2018.
Beyond Refuge is seeking a Natural Language Understanding Engineer passionate about social change and getting involved on a leadership level with a startup-like idea within an innovative, agile nonprofit.
- Free resources to learn Natural Language Processing - Sep 18, 2018.
An extensive list of free resources to help you learn Natural Language Processing, including explanations on Text Classification, Sequence Labeling, Machine Translation and more.
- The Data Science of “Someone Like You” or Sentiment Analysis of Adele’s Songs - Sep 13, 2018.
An extensive analysis of Adele's songs using Natural Language Processing (NLP) on the lyrics, to uncover the underlying emotions and sentiments.
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- Machine Learning for Text Classification Using SpaCy in Python - Sep 11, 2018.
In this post, we will demonstrate how text classification can be implemented using spaCy without having any deep learning experience.
- Deep Learning for NLP: An Overview of Recent Trends - Sep 5, 2018.
A new paper discusses some of the recent trends in deep learning based natural language processing (NLP) systems and applications. The focus is on the review and comparison of models and methods that have achieved state-of-the-art (SOTA) results on various NLP tasks and some of the current best practices for applying deep learning in NLP.
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- Topic Modeling with LSA, PLSA, LDA & lda2Vec - Aug 30, 2018.
This article is a comprehensive overview of Topic Modeling and its associated techniques.
- Word Vectors in Natural Language Processing: Global Vectors (GloVe) - Aug 29, 2018.
A well-known model that learns vectors or words from their co-occurrence information is GlobalVectors (GloVe). While word2vec is a predictive model — a feed-forward neural network that learns vectors to improve the predictive ability, GloVe is a count-based model.
- Multi-Class Text Classification with Scikit-Learn - Aug 27, 2018.
The vast majority of text classification articles and tutorials on the internet are binary text classification such as email spam filtering and sentiment analysis. Real world problem are much more complicated than that.
- Emotion and Sentiment Analysis: A Practitioner’s Guide to NLP - Aug 24, 2018.
Sentiment analysis is widely used, especially as a part of social media analysis for any domain, be it a business, a recent movie, or a product launch, to understand its reception by the people and what they think of it based on their opinions or, you guessed it, sentiment!
- Comparison of the Most Useful Text Processing APIs - Aug 23, 2018.
There is a need to compare different APIs to understand key pros and cons they have and when it is better to use one API instead of the other. Let us proceed with the comparison.
- Named Entity Recognition: A Practitioner’s Guide to NLP - Aug 17, 2018.
Named entity recognition (NER) , also known as entity chunking/extraction , is a popular technique used in information extraction to identify and segment the named entities and classify or categorize them under various predefined classes.
- Understanding Language Syntax and Structure: A Practitioner’s Guide to NLP - Aug 10, 2018.
Knowledge about the structure and syntax of language is helpful in many areas like text processing, annotation, and parsing for further operations such as text classification or summarization.
- GitHub Python Data Science Spotlight: AutoML, NLP, Visualization, ML Workflows - Aug 8, 2018.
This post includes a wide spectrum of data science projects, all of which are open source and are present on GitHub repositories.
- Text Wrangling & Pre-processing: A Practitioner’s Guide to NLP - Aug 3, 2018.
I will highlight some of the most important steps which are used heavily in Natural Language Processing (NLP) pipelines and I frequently use them in my NLP projects.
- Data Retrieval with Web Scraping: A Practitioner’s Guide to NLP - Jul 26, 2018.
Proven and tested hands-on strategies to tackle NLP tasks.
- Comparison of Top 6 Python NLP Libraries - Jul 23, 2018.
Today, we want to outline and compare the most popular and helpful natural language processing libraries, based on our experience.
- Efficient Graph-based Word Sense Induction - Jul 18, 2018.
This paper describes a set of algorithms for Natural Language Processing (NLP) that match or exceed the state of the art on several evaluation tasks, while also being much more computationally efficient.
- Text Mining on the Command Line - Jul 13, 2018.
In this tutorial, I use raw bash commands and regex to process raw and messy JSON file and raw HTML page. The tutorial helps us understand the text processing mechanism under the hood.
- Text Classification & Embeddings Visualization Using LSTMs, CNNs, and Pre-trained Word Vectors - Jul 5, 2018.
In this tutorial, I classify Yelp round-10 review datasets. After processing the review comments, I trained three model in three different ways and obtained three word embeddings.
- Overview and benchmark of traditional and deep learning models in text classification - Jul 3, 2018.
In this post, traditional and deep learning models in text classification will be thoroughly investigated, including a discussion into both Recurrent and Convolutional neural networks.
- 30 Free Resources for Machine Learning, Deep Learning, NLP & AI - Jun 25, 2018.
Check out this collection of 30 ML, DL, NLP & AI resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next.
- Detecting Sarcasm with Deep Convolutional Neural Networks - Jun 21, 2018.
Detection of sarcasm is important in other areas such as affective computing and sentiment analysis because such expressions can flip the polarity of a sentence.
- 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
- Natural Language Processing Nuggets: Getting Started with NLP - Jun 19, 2018.
Check out this collection of NLP resources for beginners, starting from zero and slowly progressing to the point that readers should have an idea of where to go next.
- 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."
- How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning - Jun 13, 2018.
An end-to-end example of how to build a system that can search objects semantically.
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- 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!
- On the contribution of neural networks and word embeddings in Natural Language Processing - May 31, 2018.
In this post I will try to explain, in a very simplified way, how to apply neural networks and integrate word embeddings in text-based applications, and some of the main implicit benefits of using neural networks and word embeddings in NLP.
- NLP in Online Courses: an Overview - May 28, 2018.
This article examines several Natural Language Processing (NLP) courses across a variety of online sources and programming languages.
- Top KDnuggets tweets, May 16-22: Python eats away at R; Data Science Plan 2018 - May 23, 2018.
Also: AI is learning to see in the dark; Introducing state of the art text classification with universal language models; Top 100 Books for Data Scientists.
- If chatbots are to succeed, they need this - May 22, 2018.
Can logic be used to make chatbots intelligent? In the 1960s this was taken for granted. Now we have all but forgotten the logical approach. Is it time for a revival?
- Getting Started with spaCy for Natural Language Processing - May 2, 2018.
spaCy is a Python natural language processing library specifically designed with the goal of being a useful library for implementing production-ready systems. It is particularly fast and intuitive, making it a top contender for NLP tasks.
- Implementing Deep Learning Methods and Feature Engineering for Text Data: FastText - May 1, 2018.
Overall, FastText is a framework for learning word representations and also performing robust, fast and accurate text classification. The framework is open-sourced by Facebook on GitHub.
- Implementing Deep Learning Methods and Feature Engineering for Text Data: The GloVe Model - Apr 25, 2018.
The GloVe model stands for Global Vectors which is an unsupervised learning model which can be used to obtain dense word vectors similar to Word2Vec.
- KDnuggets™ News 18:n17, Apr 25: Python Regular Expressions Cheat Sheet; Deep Learning With Apache Spark; Building a Question Answering Model - Apr 25, 2018.
Also: Derivation of Convolutional Neural Network from Fully Connected Network Step-By-Step; Presto for Data Scientists - SQL on anything; Why Deep Learning is perfect for NLP (Natural Language Processing); Top 16 Open Source Deep Learning Libraries and Platforms
- Why Deep Learning is perfect for NLP (Natural Language Processing) - Apr 20, 2018.
Deep learning brings multiple benefits in learning multiple levels of representation of natural language. Here we will cover the motivation of using deep learning and distributed representation for NLP, word embeddings and several methods to perform word embeddings, and applications.
- NLP – Building a Question Answering Model - Apr 20, 2018.
In this blog, I want to cover the main building blocks of a question answering model.
- Understanding What is Behind Sentiment Analysis – Part 2 - Apr 20, 2018.
Fine-tuning our sentiment classifier...
- 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.
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- 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.
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- 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.
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- 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.
- 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.
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- 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.
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- 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.
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- 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.
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- 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.
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