Myself Shridhar Mankar a Engineer l YouTuber l Educational Blogger l Educator l Podcaster. My Aim- To Make Engineering Students Life EASY. Website – 5 Minutes Engineering English YouTube Channel – Instagram – A small donation would mean the world to me and will help me to make AWESOME videos for you. • UPI ID : 5minutesengineering@apl Playlists : • 5 Minutes Engineering Podcast : • Aptitude : • Machine Learning : • Computer Graphics : • C Language Tutorial for Beginners : • R Tutorial for Beginners : • Python Tutorial for Beginners : • Embedded and Real Time Operating Systems (ERTOS) : • Shridhar Live Talks : • Welcome to 5 Minutes Engineering : • Human Computer Interaction (HCI) : • Computer Organization and Architecture : • Deep Learning : • Genetic Algorithm : • Cloud Computing : • Information and Cyber Security : • Soft Computing and Optimization Algorithms : • Compiler Design :–SachxUTOiQ7XHw • Operating System : • Hadoop : • CUDA : • Discrete Mathematics : • Theory of Computation (TOC) : • Data Analytics : • Software Modeling and Design : • Internet Of Things (IOT) : • Database Management Systems (DBMS) : • Computer Network (CN) : • Software Engineering and Project Management : • Design and Analysis of Algorithm : [More]
Explained how to Calculate Term Frequency–Inverse Document Frequency (TF-IDF) with vey simple example. TF-IDF is a statistical measure that evaluates how relevant a word is to a document in a collection of documents. This is done by multiplying two metrics: how many times a word appears in a document, and the inverse document frequency of the word across a set of documents. It has many uses, most importantly in automated text analysis, and is very useful for scoring words in machine learning and data science algorithms for Natural Language Processing (NLP). TF-IDF was invented for document search and information retrieval. This method can be uses for text clustering, text classification, and text information retrieval in real life projects and data science tasks. This video introduces a calculation example of how to get TF-IDF for a corpus consist just of two sentences for a given term. On the top of this video, you should be little familiar with BOW (Bag of Words), Stemming, Stop Words meaning, Semantic Segmentation and related NLP/NLU (Natural Language Understanding techniques). With this video I did not dive into real Python programming. If you feel that you need such tutorial, let me know in comments. #tfidf #naturallanguageprocessing #textanalytics
Full Course of Artificial Intelligence(AI) – In this video you can learn about Semantic Networks in Artificial Intelligence with Components(Lexical, Structural, Semantic, Procedural) and Example. This topic is very important for College University Semester Exams and Other Competitive exams. Artificial Intelligence Video Lectures in Hindi
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
Learn more advanced front-end and full-stack development at: 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
Find out how you can make use of Google’s machine learning expertise to power your applications. Google Cloud Platform (GCP) offers five APIs that provide access to pre-trained machine learning models with a single API call: Google Cloud Vision API, Cloud Speech API, Cloud Natural Language API, Cloud Translation API and Cloud Video API. Using these APIs, you can focus on adding new features to your app rather than building and training your own custom models. In this session we’ll share an overview of each API and dive into code with some live demos. See all the talks from Google I/O ’17 here: Watch more Android talks at I/O ’17 here: Watch more Chrome talks at I/O ’17 here: Watch more Firebase talks at I/O ’17 here: Subscribe to the Google Developers channel: #io17 #GoogleIO #GoogleIO2017
Peer to Peer Network – P2P Network – Fundamental concepts explained with example – What Why How
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example of how to convert statements into predicate logic example is about marcus is a man marcus was a pomapian
Artificial Intelligence 31 Resolution Explanation with Example in Ai resolution is proof by contradiction or you can say resolution is technique which uses negotiation to prove result resolution basically is done in four steps first resolution step is conversion of given statements into predicate logic second resolution step is convert predicate logic into cnf or conjunctive normal form third resolution step take negotiation of statement that is to be proved or contradict the statement that is to be proved in resolution fourth step in resolution is resolve clause to get contradictory statement in this video i have taken example of cats like fish; cats eats everything they like;mani is cat; to prove “mani eats fish”
In this article i will show you, how to compare two images in c# like finger print biometric system. In this system i used base64String method to convert the stream object into string.
“Apache Spark is a powerful, scalable real-time data analytics engine that is fast becoming the de facto hub for data science and big data. However, in parallel, GPU clusters are fast becoming the default way to quickly develop and train deep learning models. As data science teams and data savvy companies mature, they will need to invest in both platforms if they intend to leverage both big data and artificial intelligence for competitive advantage. This session will cover: – How to leverage Spark and TensorFlow for hyperparameter tuning and for deploying trained models – DeepLearning4J, CaffeOnSpark, IBM’s SystemML and Intel’s BigDL – Sidecar GPU cluster architecture and Spark-GPU data reading patterns – The pros, cons and performance characteristics of various approaches You’ll leave the session better informed about the available architectures for Spark and deep learning, and Spark with and without GPUs for deep learning. You’ll also learn about the pros and cons of deep learning software frameworks for various use cases, and discover a practical, applied methodology and technical examples for tackling big data deep learning. Session hashtag: #SFds14″