Scale By the Bay 2019 is held on November 13-15 in sunny Oakland, California, on the shores of Lake Merritt: https://scale.bythebay.io. Join us!
—–

In this talk, I will describe deep learning algorithms that learn representations for language that are useful for solving a variety of complex language problems. I will focus on 3 tasks: Fine-Grained sentiment analysis; Question answering to win trivia competitions (like Whatson’s Jeopardy system but with one neural network); Multimodal sentence-image embeddings (with a fun demo!) to find images that visualize sentences. I will also show some demos of how deepNLP can be made easy to use with MetaMind.io’s software.

Richard Socher is the CTO and founder of MetaMind, a startup that seeks to improve artificial intelligence and make it widely accessible. He obtained his PhD from Stanford working on deep learning with Chris Manning and Andrew Ng. He is interested in developing new AI models that perform well across multiple different tasks in natural language processing and computer vision. He was awarded the 2011 Yahoo! Key Scientific Challenges Award, the Distinguished Application Paper Award at ICML 2011, a Microsoft Research PhD Fellowship in 2012 and a 2013 ‘Magic Grant’ from the Brown Institute for Media Innovation and the 2014 GigaOM Structure Award.

Machine learning is everywhere in today’s NLP, but by and large machine learning amounts to numerical optimization of weights for human designed representations and features. The goal of deep learning is to explore how computers can take advantage of data to develop features and representations appropriate for complex interpretation tasks. This tutorial aims to cover the basic motivation, ideas, models and learning algorithms in deep learning for natural language processing. Recently, these methods have been shown to perform very well on various NLP tasks such as language modeling, POS tagging, named entity recognition, sentiment analysis and paraphrase detection, among others. The most attractive quality of these techniques is that they can perform well without any external hand-designed resources or time-intensive feature engineering. Despite these advantages, many researchers in NLP are not familiar with these methods. Our focus is on insight and understanding, using graphical illustrations and simple, intuitive derivations.

This six-part video series goes through an end-to-end Natural Language Processing (NLP) project in Python to compare stand up comedy routines.

– Natural Language Processing (Part 1): Introduction to NLP & Data Science
– Natural Language Processing (Part 2): Data Cleaning & Text Pre-Processing in Python
– Natural Language Processing (Part 3): Exploratory Data Analysis & Word Clouds in Python
– Natural Language Processing (Part 4): Sentiment Analysis with TextBlob in Python
– Natural Language Processing (Part 5): Topic Modeling with Latent Dirichlet Allocation in Python
– Natural Language Processing (Part 6): Text Generation with Markov Chains in Python

All of the supporting Python code can be found here: https://github.com/adashofdata/nlp-in-python-tutorial

Today we’re going to talk about how computers understand speech and speak themselves. As computers play an increasing role in our daily lives there has been an growing demand for voice user interfaces, but speech is also terribly complicated. Vocabularies are diverse, sentence structures can often dictate the meaning of certain words, and computers also have to deal with accents, mispronunciations, and many common linguistic faux pas. The field of Natural Language Processing, or NLP, attempts to solve these problems, with a number of techniques we’ll discuss today. And even though our virtual assistants like Siri, Alexa, Google Home, Bixby, and Cortana have come a long way from the first speech processing and synthesis models, there is still much room for improvement.

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A word embedding is a learned representation for text where words that have the same meaning have a similar representation. It is this approach to representing words and documents that may be considered one of the key breakthroughs of deep learning on challenging natural language processing problems.

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Take an adapted version of this course as part of the Stanford Artificial Intelligence Professional Program. Learn more at: https://stanford.io/2rf9OO3

Professor Christopher Potts & Consulting Assistant Professor Bill MacCartney, Stanford University
http://onlinehub.stanford.edu/

Professor Christopher Potts
Professor of Linguistics and, by courtesy, Computer Science
Director, Stanford Center for the Study of Language and Information
http://web.stanford.edu/~cgpotts/

Consulting Assistant Professor Bill MacCartney
Senior Engineering Manager, Apple
https://nlp.stanford.edu/~wcmac/

To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224u/

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Talk by Ekaterina Kochmar, University of Cambridge, at the Cambridge Coding Academy Data Science Bootcamp: https://cambridgecoding.com/datascience-bootcamp

Difference between natural intelligence and artificial intelligence Natural intelligence vs Artificial intelligence AI in urdu Human vs. Artificial Intelligence: Key Similarities and Differences
https://youtu.be/v0h6vaTjn-Q

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Types of agents in AI https://www.youtube.com/watch?v=pfLG2l1m9rw
Knowledge representation in AI https://www.youtube.com/watch?v=73H7s_88bIc&t=1s
Properties of Environment in AI https://www.youtube.com/watch?v=mxv70nY6psQ
AI Natural language processing https://www.youtube.com/watch?v=8KmCjtlxTw8
Artificial Intelligence searching strategies https://www.youtube.com/watch?v=4qyXFQGruTM
Depth first search DFS algorithm Problem https://www.youtube.com/watch?v=HJeQ4VJun7k
Breadth first search algorithm in AI https://www.youtube.com/watch?v=DBwAe-Ca0BY&t=26s

The Future of Intelligence, Artificial and Natural
https://www.creativeinnovationglobal.com.au

Ray Kurzweil is one of the world’s leading inventors, thinkers, and futurists, with a thirty-year track record of accurate predictions. Called “the restless genius” by The Wall Street Journal and “the ultimate thinking machine” by Forbes magazine, he was selected as one of the top entrepreneurs by Inc. magazine, which described him as the “rightful heir to Thomas Edison.” PBS selected him as one of the “sixteen revolutionaries who made America.”

Ray was the principal inventor of the first CCD flat-bed scanner, the first omni-font optical character recognition, the first print-to-speech reading machine for the blind, the first text-to-speech synthesizer, the first music synthesizer capable of recreating the grand piano and other orchestral instruments, and the first commercially marketed large-vocabulary speech recognition.

Among Ray’s many honors, he received a Grammy Award for outstanding achievements in music technology; he is the recipient of the National Medal of Technology, was inducted into the National Inventors Hall of Fame, holds twenty-one honorary Doctorates, and honors from three U.S. presidents.

Ray has written five national best-selling books, including New York Times best sellers The Singularity Is Near (2005) and How To Create A Mind (2012). He is Co-Founder and Chancellor of Singularity University and a Director of Engineering at Google heading up a team developing machine intelligence and natural language understanding.

Ci2019 featured over 40 global leaders including Chief Technology Officer of Google Ray Kurzweil (USA), CEO of NESTA Geoff Mulgan CBE (UK), Chief Data and Transformation Officer at DBS Bank Paul Cobban (Singapore), A.I. Experts Professor Toby Walsh and Liesl Yearsley (USA), Co-founder of Oxford Insights Emma Martinho-Truswell (UK), Ethics leader Professor Simon Longstaff, Ethics and Culture of Robots and AI Professor Kathleen Richardson (UK), brain performance neuroscientist Dr Etienne Van Der Walt (South Africa), transdisciplinary Behavioural Scientist Dr Richard Claydon (Hong Kong), Director of the Learning Technology Research Centre Carl Smith (UK), Australia’s Chief Scientist Dr Alan Finkel AO, Deakin University Vice Chancellor Professor Jane Den Hollander, ATO’s Jane King, Innovation & Science Australia CEO Dr Charles Day, CEDA CEO Melinda Cilento, Jobs for NSW CEO Nicole Cook, Behaviour Innovation founder & CEO John Pickering, People and Performance expert Andrew Horsfield, TEDx Melbourne’s Jon Yeo and many more to be announced.

The theme for Creative Innovation 2019 Asia Pacific was “Human Intelligence 2.0 – A Collective Future? How will we manage the transition?” . The event showcased world changing innovators, disruptors, futurists, scientist, inspired thinkers and curious souls gathered together in an interactive community. Creative Innovation Global is the premiere conference for anyone who cares about creativity, innovation, leadership and transformation. Creative Innovation Global delivers world class creative and exponential thinking, innovation leadership and pragmatic solutions. The event offers credible forecasts, strategies and practices to help transform you and prepare the leadership of organisations for disruption in all its forms. A place to imagine the future, inspire your leadership and achieve business success.

Creative Innovation Global has received two global awards and numerous Australian Awards winning the title of best corporate event in the world. Creative Universe and Creative Innovation Global have just been ranked among the top 20 event organizers and agencies from around the world as part of the newly released Eventex All-Stars Index: https://eventex.co/all-stars/ We work on engaging hearts and minds and building leadership and innovation capability with teams through custom designing Creative Innovation half and full day workshops for organisations on a regular basis. To see more about our inspirational leadership programs and other events and offerings, please visit http://creativeinnovationglobal.com.au

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Here is a detailed discussion of the Term Frequency and Inverse Document Frequency in Natural Language Processing.
NLP playlist: https://www.youtube.com/playlist?list=PLZoTAELRMXVMdJ5sqbCK2LiM0HhQVWNzm

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Google Workshop on Quantum Biology
D-Wave: Natural Quantum Computation
Presented by Geordie Rose
October 22, 2010

ABSTRACT

Description and philosophy of the D-Wave superconducting processor and quantum annealing algorithms.

About the speaker: Geordie Rose is a founder and CTO of D-Wave. He is known as a leading advocate for quantum computing and physics-based processor design, and has been invited to speak on these topics in venues ranging from the 2003 TED Conference to Supercomputing 2008.

His innovative and ambitious approach to building quantum computing technology has received coverage in MIT Technology Review magazine, The Economist, New Scientist, Scientific American and Science magazines, and one of his business strategies was profiled in a Harvard Business School case study. He has received several awards and accolades for his work with D-Wave, including being short-listed for a 2005 World Technology Award.

Dr. Rose holds a PhD in theoretical physics from the University of British Columbia, specializing in quantum effects in materials. While at McMaster University, he graduated first in his class with a BEng in Engineering Physics, specializing in semiconductor engineering.

Now that we understand some of the basics of of natural language processing with the Python NLTK module, we’re ready to try out text classification. This is where we attempt to identify a body of text with some sort of label.

To start, we’re going to use some sort of binary label. Examples of this could be identifying text as spam or not, or, like what we’ll be doing, positive sentiment or negative sentiment.

Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1

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Natural Language Processing (NLP) is a field of computer science that aims to understand or generate human languages, either in text or speech form. Computers are programmed to identify written and spoken words. But to really communicate with people, they need to understand context. Learn more: https://accntu.re/2MX0rwT

#NaturalLanguageProcessing
#NLP
#AI

Welcome to Zero to Hero for Natural Language Processing using TensorFlow! If you’re not an expert on AI or ML, don’t worry — we’re taking the concepts of NLP and teaching them from first principles with our host Laurence Moroney (@lmoroney).

In this first lesson we’ll talk about how to represent words in a way that a computer can process them, with a view to later training a neural network to understand their meaning.

Hands-on Colab → https://goo.gle/2uO6Gee

NLP Zero to Hero playlist → https://goo.gle/nlp-z2h
Subscribe to the TensorFlow channel → https://goo.gle/TensorFlow

This video covers Stanford CoreNLP Example.

GitHub link for example: https://github.com/TechPrimers/core-nlp-example

Stanford Core NLP: https://stanfordnlp.github.io/CoreNLP/
Stanford API example: https://stanfordnlp.github.io/CoreNLP/api.html

Slack Community: https://techprimers.slack.com

Twitter: https://twitter.com/TechPrimers

Facebook: http://fb.me/TechPrimers

GitHub: https://github.com/TechPrimers or https://techprimers.github.io/

Video Editing: iMovie

Intro Music: A Way for me (www.hooksounds.com)

#CoreNLP #TechPrimers

Transfer Learning in Natural Language Processing (NLP): Open questions, current trends, limits, and future directions. Slides: https://tinyurl.com/FutureOfNLP
A walk through interesting papers and research directions in late 2019/early-2020 on:
– model size and computational efficiency,
– out-of-domain generalization and model evaluation,
– fine-tuning and sample efficiency,
– common sense and inductive biases.
by Thomas Wolf (Science lead at HuggingFace)

HuggingFace on Twitter: https://twitter.com/huggingface
Thomas Wolf on Twitter: https://twitter.com/Thom_Wolf

For more information go to https://curiositystream.com/crashcourse
So far in this series, we’ve mostly focused on how AI can interpret images, but one of the most common ways we interact with computers is through language – we type questions into search engines, use our smart assistants like Siri and Alexa to set alarms and check the weather, and communicate across language barriers with the help of Google Translate. Today, we’re going to talk about Natural Language Processing, or NLP, show you some strategies computers can use to better understand language like distributional semantics, and then we’ll introduce you to a type of neural network called a Recurrent Neural Network or RNN to build sentences.

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What is natural language generation, what should clients be doing with it, and what is its future? Get answers from Deloitte’s interview with Kris Hammond, chief scientist at Narrative Science.

Natural language processing (Wikipedia): “Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages

Natural Language Processing techniques allow addressing tasks like text classification and information extraction and content generation. They can give the perception of machines being able to understand humans and respond more naturally.

In this session, Barbara will introduce basic concepts of natural language processing. Using Python and its machine learning libraries the attendees will go through the process of building the bag of words representation and using it for text classification. It can be then used to recognise the sentiment, category or the authorship of the document.
The goal of this tutorial is to build the intuition on the simple natural language processing task. After this session, the audience will know basics of the text representation, learn how to develop the classification model and use it in real-world applications.

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You might be familiar with NLP (especially if you are a subscriber of my channel). But do you know what is NLG?

In today’s video, I’ll explain the meaning of Natural Language Generation, and its relation with NLP.
NLG and NLP are closely related, since Speech Recognition is a subfield of NLP, or to be more precise, it is a subfield of computational linguistics. So if you are interested in this topic, in AI and/or machine learning, watch it right now! Also, don’t forget to leave your impressions about it and recommendations in the comments.

Link to the video What is NLP
* https://www.youtube.com/watch?v=Hbx9bxt7gvc&t

Link to the video What is Speech Recognition
* https://www.youtube.com/watch?v=wWLNSYhdKf4

#ConsumerCentric #NLG #NaturalLanguageGeneration

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Learn most important Natural Language Processing Interview Questions and Answers, asked at every Artificial Intelligence interview. These Interview questions will be useful to all entry level candidates, beginners, interns and experienced candidates interviewing for the role of NLP Engineer, NLP Researcher, NLP Intern etc.
The examples and sample answers with each question will make it easier for candidates to understand these conceptual, general and situational interview questions.

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#NaturalLanguageProcessing #NLPInterview #NLPForBeginners

Natural Language Processing , Natural Language Processing (NLP) refers to AI method of communicating with an intelligent systems using a natural language such as English.
Natural Language Understanding (NLU)
Natural Language Generation (NLG)

Natural Language Processing, or NLP, is made up of Natural Language Understanding and Natural Language Generation. NLU helps the machine understand the intent of the sentence or phrase using profanity filtering, sentiment detection, topic classification, entity detection, and more.

In this episode, Tia breaks down the differences between NLP, NLU, and NLG, and explains how Deep Learning plays a role in getting you better search results, faster.

For more information on all things AI, subscribe to the Lucid Thoughts channel!

Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com

A Markov Chain is a system that transitions between states using a random, memoryless process. Markov Chains are a great tool for simulating real-world phenomena and environments with computers. In this video, we’ll give a specific example of how to use Markov Chains in Natural Language Generation.

Watch this video to learn:

– What is a Markov Chain
– How are Markov Chains being used
– The reasons they’re useful for Natural Language Generation

In this segment, you will learn the basics of Natural Language Generation and the Integration between TIBCO Spotfire and Automated Insights’s Natural Language Generation Software Wordsmith.

Presentation by Catherine Henry (2017 Clearwater DevCon).
When teaching a subject through text it can be beneficial to evaluate the reader’s understanding; however, the creation of relevant questions and answers can be time-consuming and tedious. I will walk through how the implementation of NLP libraries and algorithms can assist in, and potentially remove altogether, the current necessity of an individual manually formulating these tests.

Learn more advanced front-end and full-stack development at: https://www.fullstackacademy.com

Natural Language Processing (NLP) is a field of artificial intelligence that focuses on the interactions between computers and human language. In this Natural Language Processing Tutorial, we give an overview of NLP and its uses, before diving into the Natural library for Node.js and how easily you can use it for inflectors, string distance, classifications with machine learning, and term frequency using various algorithms.

Watch this video to learn:

– What is NLP
– Natural Language Processing use cases
– How to use the Natural library in Node.js

Natural language processing is a subfield of artificial intelligence (AI) concerned with the interactions between computers and human languages. It was formulated to build software that generates and comprehends natural languages so that a user can have natural conversations with the computer instead of through programming or artificial languages like Java or C. In this video, we will be learning all about Natural Language Processing (NLP), its various aspects and what the future holds for NLP.
Stay tuned to learn more about NLP with Great Learning!
#NaturalLanguageProcessing #ArtificialIntelligence #GreatLearning

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Natural language processing allows computers to understand human language. It has plenty of applications. For example:
Text summarization, translation, keyword generation, sentiment analysis or chat bots.

So how it works? Let’s take a closer look at it.

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Some sources & further reading:
http://www.mind.ilstu.edu/curriculum/protothinker/natural_language_processing.php
https://nlp.stanford.edu/
https://research.google.com/pubs/NaturalLanguageProcessing.html
https://en.wikipedia.org/wiki/Natural_language_processing
https://en.wikipedia.org/wiki/Natural_language_understanding
https://en.wikipedia.org/wiki/Natural_language_generation
https://en.wikipedia.org/wiki/Tokenization_(lexical_analysis)
https://en.wikipedia.org/wiki/Syntax
https://en.wikipedia.org/wiki/Parsing
https://en.wikipedia.org/wiki/Context-free_grammar
https://en.wikipedia.org/wiki/Semantics
https://en.wikipedia.org/wiki/Pragmatics
https://en.wikipedia.org/wiki/Sentiment_analysis

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🔥Intellipaat natural language processing in python course: https://intellipaat.com/nlp-training-course-using-python/
In this natural language processing tutorial video you will learn what is natural language, text mining in nlp, file handling in python, nltk package, tokenization, frequency distribution, stop words and the concepts of bi grams, tri grams and n grams in detail.
#NaturalLanguageProcessingNLPinPython #NaturalLanguageProcessingTutorial #NaturalLanguageProcessing #Intellipaat

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Why should you watch this natural language processing tutorial?

NLP market is speculated to grow to US$26.4 billion by 2024 with CAGR of 21%. One of the principal disciplines of AI, Natural language processing is used to solve uses analysis tools to read data from large amounts of natural language data to arrive at meaningful conclusions. It involves using the ML algorithms to recognize, categorize, and extract natural language rules to transform unstructured language data into a form that computers can understand.

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