For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3waBO2R Jacob Devlin, Google AI Language https://research.google/people/106320/ Professor Christopher Manning Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science Director, Stanford Artificial Intelligence Laboratory (SAIL)
How you can speed up the creation of many repetitive descriptions significantly by using AX Semantics software? You will learn this in this video. AX Semantics software is intuitive and quickly able to generate all the content needed to keep pace with your business needs. AX software is 100% SaaS – everything is available from your desk via your web browser, no programming or IT departments required. Our self-service with integrated e-learning allows customers to start automating text within 48 hours – more than 500 customers have already done this successfully. We already work with some of the world’s best known brands on content generation
In this spaCy tutorial, you will learn all about natural language processing and how to apply it to real-world problems using the Python spaCy library. 💻 Course website with code: http://spacy.pythonhumanities.com/ ✏️ Course developed by Dr. William Mattingly. Check out his channel: https://www.youtube.com/pythontutorialsfordigitalhumanities ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Course Introduction ⌨️ (0:03:56) Intro to NLP ⌨️ (0:11:53) How to Install spaCy ⌨️ (0:17:33) SpaCy Containers ⌨️ (0:21:36) Linguistic Annotations ⌨️ (0:45:03) Named Entity Recognition ⌨️ (0:50:08) Word Vectors ⌨️ (1:05:22) Pipelines ⌨️ (1:16:44) EntityRuler ⌨️ (1:35:44) Matcher ⌨️ (2:09:38) Custom Components ⌨️ (2:16:46) RegEx (Basics) ⌨️ (2:19:59) RegEx (Multi-Word Tokens) ⌨️ (2:38:23) Applied SpaCy Financial NER 🎉 Thanks to our Champion and Sponsor supporters: 👾 Wong Voon jinq 👾 hexploitation 👾 Katia Moran 👾 BlckPhantom 👾 Nick Raker 👾 Otis Morgan 👾 DeezMaster 👾 AppWrite — Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news And subscribe for new videos on technology every day: https://youtube.com/subscription_center?add_user=freecodecamp
In this quick tutorial, we learn that machines can not only make sense of words but also make sense of words in their context. N-grams are one way to help machines understand a word in its context by looking at words in pairs. We go over what n-grams are and some examples of how you could use them in natural language processing. By looking at pairs of words, we capture the broader context of words to then train machines to learn these language queues and gain a better understanding of the real meaning of the text. — Learn more about Data Science Dojo here: https://datasciencedojo.com/data-science-bootcamp/ Watch the latest video tutorials here: https://tutorials.datasciencedojo.com/ See what our past attendees are saying here: https://datasciencedojo.com/bootcamp/reviews/#videos — Like Us: https://www.facebook.com/datasciencedojo Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/datasciencedojo Also find us on: Instagram: https://www.instagram.com/data_science_dojo Vimeo: https://vimeo.com/datasciencedojo #machinelearning #datascience
Ever wondered how we can talk to machines and have them answer back? That is due to the magic of NLP. In this video, we will answer the question ‘What is NLP?’ for you. We will then look at some important steps involved in NLP all in 5 minutes! Don’t forget to take the quiz at 04:07! 🔥Free AI Course: https://www.simplilearn.com/learn-ai-basics-skillup?utm_campaign=NLPin5MinsScribe&utm_medium=Description&utm_source=youtube ✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH ⏩ Check out the AI & Machine Learning tutorial videos: https://www.youtube.com/watch?v=ad79nYk2keg&list=PLEiEAq2VkUULyr_ftxpHB6DumOq1Zz2hq #NaturalLanguageProcessing #NLP #NLPTutorialForBeginners #NaturalLanguageProcessingIn5Minutes #NLPTechniques #NLPTrainingVideos #NLPTutorial #NLPInArtificialIntelligence #NLPTraining #ArtificialIntelligence #MachineLearning #Simplilearn Post Graduate Program in AI and Machine Learning: Ranked #1 AI and Machine Learning course by TechGig Fast track your career with our comprehensive Post Graduate Program in AI and Machine Learning, in partnership with Purdue University and in collaboration with IBM. This AI and machine learning certification program will prepare you for one of the world’s most exciting technology frontiers. This Post Graduate Program in AI and Machine Learning covers statistics, Python, machine learning, deep learning networks, NLP, and reinforcement learning. You will build and deploy deep learning models on the cloud using AWS SageMaker, work on voice assistance devices, build Alexa skills, and gain access to GPU-enabled labs. Key Features: ✅ Purdue Alumni Association Membership ✅ Industry-recognized IBM certificates for IBM courses ✅ Enrollment in Simplilearn’s JobAssist ✅ 25+ hands-on Projects on GPU enabled Labs ✅ 450+ hours of Applied learning ✅ Capstone Project in 3 Domains ✅ Purdue Post Graduate Program [More]
In this video we will understand the detailed explanation of Lemmatization and understand how it can be used in Natural Language Processing. We will also see the basic difference between Lemmatization and stemming. NLP playlist: https://www.youtube.com/playlist?list=PLZoTAELRMXVMdJ5sqbCK2LiM0HhQVWNzm If you want to Give donation to support my channel, below is the Gpay id GPay: krishnaik06@okicici Connect with me here: Twitter: https://twitter.com/Krishnaik06 Facebook: https://www.facebook.com/krishnaik06 instagram: https://www.instagram.com/krishnaik06
I’ve designed a free natural language processing curriculum for anyone interested in improving their skills in order to start a startup, get consulting work, or find full-time work related to NLP. This curriculum is for beginners and starts with basic NLP terminology, then moves into basic language models and word embeddings. Then, it moves onto more advanced concepts like neural networks, sequence modeling and dialogue systems. At the end, I’ll detail what the most experimental, modern-day techniques are in the field. I hope you find this curriculum useful! Curriculum for this video: https://github.com/llSourcell/Learn-Natural-Language-Processing-Curriculum Want more education? Connect with me here: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology instagram: https://www.instagram.com/sirajraval Prerequisites are here: – Learn Python https://www.edx.org/course/introduction-to-python-fundamentals-4 – Statistics http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf – Probability https://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf – Calculus http://tutorial.math.lamar.edu/pdf/Calculus_Cheat_Sheet_All.pdf – Linear Algebra https://www.souravsengupta.com/cds2016/lectures/Savov_Notes.pdf The rest of the curriculum is in the github link above, check it out! Make Money with Tensorflow 2.0: https://www.youtube.com/watch?v=WS9Nckd2kq0 Watch Me Build a Finance Startup: https://www.youtube.com/watch?v=oeraUtRgsbI Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Hit the Join button above to sign up to become a member of my channel for access to exclusive live streams! Join us at the School of AI: https://theschool.ai/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w And please support me on Patreon: https://www.patreon.com/user?u=3191693
This course is a practical introduction to natural language processing with TensorFlow 2.0. In this tutorial you will go from having zero knowledge to writing an artificial intelligence that can compose Shakespearean prose. No prior experience with deep learning is required, though it is always helpful to have more background information. We’ll use a combination of embedding layers, recurrent neural networks, and fully connected layers to perform the classification. ⭐️Course Contents ⭐️ ⌨️ (01:16) Getting Started with Word Embeddings ⌨️ (33:25) How to Perform Sentiment Analysis on Movie Reviews ⌨️ (59:32) Let’s Write An AI That Writes Shakespeare ⭐️Course Description ⭐️ The basic idea behind natural language processing is that we start out with words, i.e. strings of characters, that are almost impossible for the computer to meaningfully parse. We can transform these strings into a vector in a higher dimensional space. Different words will be represented as vectors of different lengths and directions in this space, and this allows us to find relationships between words by finding the component of one vector along another. Don’t worry, the TensorFlow library handles all of this, we just have to have some basic idea of how it works. Since this is a type of supervised learning, we also have labels for our text. This allows the AI to compare the relationships between words to the training labels, and learn which sequences of words represent good and bad movie reviews. This would also work for finding toxic comments, fake product reviews… just about [More]
Today we’re joined Richard Socher, Chief Scientist and Executive VP at Salesforce. Richard, who has been at the forefront of Salesforce’s AI Research since they acquired his startup Metamind in 2016, and his team have been publishing a ton of great projects as of late, including CTRL: A Conditional Transformer Language Model for Controllable Generation, and ProGen, an AI Protein Generator, both of which we cover in-depth in this conversation. We explore the balancing act between investments, product requirement research and otherwise at a large product-focused company like Salesforce, the evolution of his language modeling research since being acquired, and how it ties in with Protein Generation. The complete show notes for this episode can be found at twimlai.com/talk/372.
How to Type very Fast in Hindi using Voice recognition and using Google Keyboard or Google Indic Keyboard. Voice Typing on PC: https://youtu.be/CrJylAvXF3w Type in Hindi – हिन्दी using Voice Recognition is a latest Update of Google but before use to this app Download Hindi words Data in your smart phone using Google Translate App. ▂ ▄ ▅ ▆ ▇ █ Don’t Forget to Like and Follow Us █ ▇ ▆ ▅ ▄ ▂ ► Facebook: https://www.facebook.com/SanjaySharmaGroup/ ► Twitter: https://twitter.com/SanjaySharma_G ► Google+: https://plus.google.com/+SanjaySharmaG ► Website: http://sanjaysharma-g.blogspot.in/ #VoiceTyping #SpeechToText #HindiVoiceTyping #EnglishVoiceTyping #UrduVoiceTyping #VoiceTyping #FastVoiceTyping #GoogleVoiceTyping #GoogleIndicKeyBoard #HindiVoiceTypingInAndroid -~-~~-~~~-~~-~- Please watch: “How to Download Latest Bollywood, Hollywood movies Direct without Torrent” https://www.youtube.com/watch?v=E87Jf_khXH8 -~-~~-~~~-~~-~-
Here is a detailed discussion of the Term Frequency and Inverse Document Frequency in Natural Language Processing. https://github.com/krishnaik06/Natural-Language-Processing/blob/master/TFIDF.py For more videos on ML or deep learning please check the below url NLP playlist: https://www.youtube.com/playlist… Deep Learning : https://goo.gl/iwek57 Statistics in ML :https://goo.gl/x7mkUH Feature Engineering:https://goo.gl/6wiaGt Data Preprocessing Techniques: https://goo.gl/YfC9Kc Machine learning: https://goo.gl/XhHdCd
Here is the detailed discussion of Bag of words document matrix. We will also be covering how we can can implement with the help of python and nltk. NLP playlist: https://www.youtube.com/playlist?list=PLZoTAELRMXVMdJ5sqbCK2LiM0HhQVWNzm If you want to Give donation to support my channel, below is the Gpay id GPay: krishnaik06@okicici Connect with me here: Twitter: https://twitter.com/Krishnaik06 Facebook: https://www.facebook.com/krishnaik06 instagram: https://www.instagram.com/krishnaik06
25 March 2019 This is the inaugural workshop of Giving Voice to Digital Democracies: The Social Impact of Artificially Intelligent Communications Technology, a research project which is part of the Centre for the Humanities and Social Change, Cambridge and funded by the Humanities and Social Change International Foundation.​ The workshop will bring together experts from politics, industry, and academia to consider the social impact of Artificially Intelligent Communications Technology (AICT). The talks and discussions will focus on different aspects of the complex relationships between language, ethics, and technology. These issues are of particular relevance in an age when we talk to Virtual Personal Assistants such as Siri, Cortana, and Alexa ever more frequently, when the automated detection of offensive language is bringing free speech and censorship into direct conflict, and when there are serious ethical concerns about the social biases present in the training data used to build influential AICT systems. Speakers Professor Emily M. Bender, University of Washington Baroness Grender MBE, House of Lords Select Committee on AI Dr Margaret Mitchell, Google Dr Melanie Smallman, UCL, Alan Turing Institute Dr Marcus Tomalin, University of Cambridge Dr Adrian Weller, University of Cambridge, Alan Turing Institute, The Centre for Data Ethics and Innovation Giving Voice to Digital Democracies explores the social impact of Artificially Intelligent Communications Technology – that is, AI systems that use speech recognition, speech synthesis, dialogue modelling, machine translation, natural language processing, and/or smart telecommunications as interfaces. Due to recent advances in machine learning, these technologies are already [More]
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. Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook – https://www.facebook.com/YouTubeCrash… Twitter – http://www.twitter.com/TheCrashCourse Tumblr – http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
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. Please join as a member in my channel to get additional benefits like materials in Data Science, live streaming for Members and many more https://www.youtube.com/channel/UCNU_lfiiWBdtULKOw6X0Dig/join Please do subscribe my other channel too https://www.youtube.com/channel/UCjWY5hREA6FFYrthD0rZNIw If you want to Give donation to support my channel, below is the Gpay id GPay: krishnaik06@okicici Connect with me here: Twitter: https://twitter.com/Krishnaik06 Facebook: https://www.facebook.com/krishnaik06 instagram: https://www.instagram.com/krishnaik06
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/ To get the latest news on Stanford’s upcoming professional programs in Artificial Intelligence, visit: http://learn.stanford.edu/AI.html To view all online courses and programs offered by Stanford, visit: http://online.stanford.edu
Talk by Ekaterina Kochmar, University of Cambridge, at the Cambridge Coding Academy Data Science Bootcamp: https://cambridgecoding.com/datascience-bootcamp
In his talk, Kristian Hammond discusses the evolution of communication by artificial intelligence. Hammond mentions potential future uses, as well as practical applications of machine-generated language that are already affecting, and improving, our day-to-day lives. In addition to being Chief Scientist, Kris is a professor of Computer Science at Northwestern University. Prior to Northwestern, Kris founded the University of Chicago’s Artificial Intelligence Laboratory. His research has always been focused on artificial intelligence, machine-generated content and context-driven information systems. Kris previously sat on a United Nations policy committee run by the United Nations Institute for Disarmament Research (UNIDIR). Kris received his PhD from Yale. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
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 If you want to Give donation to support my channel, below is the Gpay id GPay: krishnaik06@okicici Connect with me here: Twitter: https://twitter.com/Krishnaik06 Facebook: https://www.facebook.com/krishnaik06 instagram: https://www.instagram.com/krishnaik06
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 sample code: http://pythonprogramming.net http://hkinsley.com https://twitter.com/sentdex http://sentdex.com http://seaofbtc.com
📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
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