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 โ€“ 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 worldโ€™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. ๐Ÿ’ป Course website with code: โœ๏ธ Course developed by Dr. William Mattingly. Check out his channel: โญ๏ธ Course Contents โญ๏ธ โŒจ๏ธ (0:00:00) Course Introduction โŒจ๏ธ (0:03:56) Intro to NLP โŒจ๏ธ (0:11:53) How to Install spaCy โŒจ๏ธ (0:17:33) SpaCy Containers โŒจ๏ธ (0:21:36) Linguistic Annotations โŒจ๏ธ (0:45:03) Named Entity Recognition โŒจ๏ธ (0:50:08) Word Vectors โŒจ๏ธ (1:05:22) Pipelines โŒจ๏ธ (1:16:44) EntityRuler โŒจ๏ธ (1:35:44) Matcher โŒจ๏ธ (2:09:38) Custom Components โŒจ๏ธ (2:16:46) RegEx (Basics) โŒจ๏ธ (2:19:59) RegEx (Multi-Word Tokens) โŒจ๏ธ (2:38:23) Applied SpaCy Financial NER ๐ŸŽ‰ Thanks to our Champion and Sponsor supporters: ๐Ÿ‘พ Wong Voon jinq ๐Ÿ‘พ hexploitation ๐Ÿ‘พ Katia Moran ๐Ÿ‘พ BlckPhantom ๐Ÿ‘พ Nick Raker ๐Ÿ‘พ Otis Morgan ๐Ÿ‘พ DeezMaster ๐Ÿ‘พ AppWrite — Learn to code for free and get a developer job: Read hundreds of articles on programming: And subscribe for new videos on technology every day:
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: Watch the latest video tutorials here: See what our past attendees are saying here: — Like Us: Follow Us: Connect with Us: Also find us on: Instagram: Vimeo: #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! ๐Ÿ”ฅFree AI Course: โœ…Subscribe to our Channel to learn more about the top Technologies: โฉ Check out the AI & Machine Learning tutorial videos: #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 worldโ€™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: โœ… Purdue Alumni Association Membership โœ… Industry-recognized IBM certificates for IBM courses โœ… Enrollment in Simplilearnโ€™s JobAssist โœ… 25+ hands-on Projects on GPU enabled Labs โœ… 450+ hours of Applied learning โœ… Capstone Project in 3 Domains โœ… 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: If you want to Give donation to support my channel, below is the Gpay id GPay: krishnaik06@okicici Connect with me here: Twitter: Facebook: instagram:
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: Want more education? Connect with me here: Twitter: Facebook: instagram: Prerequisites are here: – Learn Python – Statistics – Probability – Calculus – Linear Algebra The rest of the curriculum is in the github link above, check it out! Make Money with Tensorflow 2.0: Watch Me Build a Finance Startup: Join us in the Wizards Slack channel: 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: Signup for my newsletter for exciting updates in the field of AI: And please support me on Patreon:
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. Weโ€™ll use a combination of embedding layers, recurrent neural networks, and fully connected layers to perform the classification. โญ๏ธCourse Contents โญ๏ธ โŒจ๏ธ (01:16) Getting Started with Word Embeddings โŒจ๏ธ (33:25) How to Perform Sentiment Analysis on Movie Reviews โŒจ๏ธ (59:32) Letโ€™s Write An AI That Writes Shakespeare โญ๏ธCourse Description โญ๏ธ 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. Donโ€™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 reviewsโ€ฆ just about [More]
Here is a detailed discussion of the Term Frequency and Inverse Document Frequency in Natural Language Processing. For more videos on ML or deep learning please check the below url NLP playlist:โ€ฆ Deep Learning : Statistics in ML : Feature Engineering: Data Preprocessing Techniques: Machine learning:
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: If you want to Give donation to support my channel, below is the Gpay id GPay: krishnaik06@okicici Connect with me here: Twitter: Facebook: instagram:
Scale By the Bay 2019 is held on November 13-15 in sunny Oakland, California, on the shores of Lake Merritt: 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’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:
Today weโ€™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 weโ€™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: Want to know more about Carrie Anne? The Latest from PBS Digital Studios: Want to find Crash Course elsewhere on the internet? Facebook –… Twitter – Tumblr – Support Crash Course on Patreon: CC Kids:
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 Please do subscribe my other channel too If you want to Give donation to support my channel, below is the Gpay id GPay: krishnaik06@okicici Connect with me here: Twitter: Facebook: instagram:
Take an adapted version of this course as part of the Stanford Artificial Intelligence Professional Program. Learn more at: Professor Christopher Potts & Consulting Assistant Professor Bill MacCartney, Stanford University Professor Christopher Potts Professor of Linguistics and, by courtesy, Computer Science Director, Stanford Center for the Study of Language and Information Consulting Assistant Professor Bill MacCartney Senior Engineering Manager, Apple To follow along with the course schedule and syllabus, visit: To get the latest news on Stanfordโ€™s upcoming professional programs in Artificial Intelligence, visit: To view all online courses and programs offered by Stanford, visit:
Talk by Ekaterina Kochmar, University of Cambridge, at the Cambridge Coding Academy Data Science Bootcamp:
Difference between natural intelligence and artificial intelligence Natural intelligence vs Artificial intelligence AI in urdu Human vs. Artificial Intelligence: Key Similarities and Differences โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜… Thank you For Watching.. Comment below Hit the Like Button Share with your friends For more software engineering tutorials Subscribe our Channel โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜…โ˜… Follow me on Facebook : #naturalintelligence #artificialintelligence #ai Types of agents in AI Knowledge representation in AI Properties of Environment in AI AI Natural language processing Artificial Intelligence searching strategies Depth first search DFS algorithm Problem Breadth first search algorithm in AI
The Future of Intelligence, Artificial and Natural Ray Kurzweil is one of the worldโ€™s leading inventors, thinkers, and futurists, with a thirty-year track record of accurate predictions. Called โ€œthe restless geniusโ€ by The Wall Street Journal and โ€œthe ultimate thinking machineโ€ by Forbes magazine, he was selected as one of the top entrepreneurs by Inc. magazine, which described him as the โ€œrightful heir to Thomas Edison.โ€ PBS selected him as one of the โ€œsixteen revolutionaries who made America.โ€ 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 Rayโ€™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 sellersย The Singularity Is Nearย (2005) andย How To Create A Mindย (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 [More]
Here is a detailed discussion of the Term Frequency and Inverse Document Frequency in Natural Language Processing. NLP playlist: If you want to Give donation to support my channel, below is the Gpay id GPay: krishnaik06@okicici Connect with me here: Twitter: Facebook: instagram:
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: sample code:
๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING ๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“๐ŸŽ“ 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) ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. ๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก๐Ÿ’ก THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. ๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™๐Ÿ™ YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING ๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š๐Ÿ“š
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: #NaturalLanguageProcessing #NLP #AI
Welcome to Zero to Hero for Natural Language Processing using TensorFlow! If youโ€™re not an expert on AI or ML, donโ€™t worry — weโ€™re taking the concepts of NLP and teaching them from first principles with our host Laurence Moroney (@lmoroney). In this first lesson weโ€™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 โ†’ NLP Zero to Hero playlist โ†’ Subscribe to the TensorFlow channel โ†’
This video covers Stanford CoreNLP Example. GitHub link for example: Stanford Core NLP: Stanford API example: Slack Community: Twitter: Facebook: GitHub: or Video Editing: iMovie Intro Music: A Way for me ( #CoreNLP #TechPrimers