Openi embeddings

Openi embeddings смотреть последние обновления за сегодня на .

BEST OPEN Alternative to OPENAI's EMBEDDINGs for Retrieval QA: LangChain


In this video tutorial, we will explore the use of InstructorEmbeddings as a potential replacement for OpenAI's Embeddings for information retrieval using LangChain. This will be the first step in using Open Source ChatBot on your data. The results will surprise you!!! LINKS: Google NoteBOOK: 🤍 Instructor Embeddings: 🤍 Videos to Watch: 🤍 LangChain Crash Course: 🤍 - ☕ Buy me a Coffee: 🤍 Join the Patreon: - All Interesting Videos: Everything LangChain: 🤍 Everything LLM: 🤍 Everything Midjourney: 🤍 AI Image Generation: 🤍

Open AI Embeddings Tutorial for Beginners: Build Powerful AI Apps With Unlimited Memory


► VIEW/CLONE THIS FLUTTERFLOW APP (and all my other FlutterFlow/NoCode apps), get access to live streams, Q&As and an exclusive behind the scenes content, in-depth masterclasses and support my work! 🤍 ► GET MY NEW TRAINING — MASTERING FLUTTERFLOW 🤍 ► FREE NOCODE TRAINING 🤍 Building apps by leveraging AI and ChatGPT is nothing less than awesome. However, ChatGPT has a limitation: it can only store a limited number of tokens, which means it has a rather short memory. This makes it all but impossible to feed it a large amount of information, such as a 300-page book or all of Joe Rogan's podcast episodes (or just one episode), and expect to query based on all that information. There's a solution. In this video, I'll show you how to use embeddings and vector databases to create your own ChatBot that can query specific information, such as a big book or a series of Podcast episodes. 00:00 Intro 02:23 Architecture 04:54 What Are Embeddings and What Do They Do? 07:08 How To Store Embeddings 09:17 Building A ChatBot Based Around a Popular Podcast 10:39 Setting Up Pinecone DB 14:53 Proof of Concept App 17:27 How To Build Such An App 26:50 How To Make It 10x Better 31:36 Get More Content

Vectoring Words (Word Embeddings) - Computerphile


How do you represent a word in AI? Rob Miles reveals how words can be formed from multi-dimensional vectors - with some unexpected results. 08:06 - Yes, it's a rubber egg :) Unicorn AI: EXTRA BITS: 🤍 AI YouTube Comments: 🤍 More from Rob Miles: 🤍 Thanks to Nottingham Hackspace for providing the filming location: 🤍 🤍 🤍 This video was filmed and edited by Sean Riley. Computer Science at the University of Nottingham: 🤍 Computerphile is a sister project to Brady Haran's Numberphile. More at 🤍

Arvind Neelakan (Open AI) Text and Code Embeddings


We are introducing embeddings, a new endpoint in the OpenAI API that makes it easy to perform natural language and code tasks like semantic search, clustering, topic modeling, and classification. Embeddings are numerical representations of concepts converted to number sequences, which make it easy for computers to understand the relationships between those concepts. Our embeddings outperform top models in 3 standard benchmarks, including a 20% relative improvement in code search. View more mlcon sessions on AI & ML at

Box Embeddings: An open-source library for representation learning using geometric structures


This video is the presentation accompanying the paper of the same name, accepted to the EMNLP 2021 demo track. Paper: 🤍 GitHub library: 🤍 Documentation: 🤍

Frank Liu – Building an Open-source Framework for Generating Embedding Vectors


The combination of big data and deep learning has fundamentally changed the way we approach search systems, allowing us to index audio, images, video, and other human-generated data based on an embedding vector instead of an auxiliary description. These advancements are backed by new and often times increasingly complex machine learning (ML) models, leading to an even wider research-to-industry gap despite the introduction of MLOps platforms and a variety of model hubs. We summarize some of the challenges facing practical machine learning in 2022 and beyond as follows: 1) many ML applications require a combination of multiple models, leading to a lot of overly complex and difficult-to-maintain auxiliary code, 2) many engineers are unfamiliar with ML and/or data science, making it difficult for them to train, test, and integrate ML models into existing infrastructure, and 3) constant architectural updates to SOTA deep learning models creates significant overhead when deploying said models in production environments. In this talk, we discuss lessons learned from building an open-source (🤍 and scalable framework for generating embedding vectors purpose-built to tackle the above challenges. Early on, we communicated with dozens of industry partners to understand their application(s) and architected our platform around their requirements. This open source project is currently being used by 3 major corporations ($10B+ market value) and a number of small- and mid-size startups in proof-of-concept and production systems. More: 🤍 Web: 🤍 Twitter: 🤍 LinkedIn: 🤍

The Open Syllabus co-assignment Galaxy: Mapping embeddings and disciplines | David McClure


This video is a recording of the original CUDAN Open Lab Seminar zoom session on 2021-02-15. David McClure – The Open Syllabus co-assignment Galaxy: Mapping embeddings and disciplines Websites: 🤍 & 🤍 & 🤍 🤍

Transformers, explained: Understand the model behind GPT, BERT, and T5


Dale’s Blog → 🤍 Classify text with BERT → 🤍 Over the past five years, Transformers, a neural network architecture, have completely transformed state-of-the-art natural language processing. Want to translate text with machine learning? Curious how an ML model could write a poem or an op ed? Transformers can do it all. In this episode of Making with ML, Dale Markowitz explains what transformers are, how they work, and why they’re so impactful. Watch to learn how you can start using transformers in your app! Chapters: 0:00 - Intro 0:51 - What are transformers? 3:18 - How do transformers work? 7:41 - How are transformers used? 8:35 - Getting started with transformers Watch more episodes of Making with Machine Learning → 🤍 Subscribe to Google Cloud Tech → 🤍 #MakingwithMachineLearning #MakingwithML product: Cloud - General; fullname: Dale Markowitz; re_ty: Publish;

Open your eyes through de-embedding


Even the most severe signal impairments caused by cables and fixtures can be compensated for. Meaning accurate measurements can still be made under less than ideal conditions.

Embedding Yourself in Open Community


I'm a Clinically Trained & Certified Executive Coach, Licensed Marriage & Family Therapist, Author and Speaker. Check out more of my work at my website 🤍

Visualizing Word Embeddings using Tensorflow | Deep Learning Series | Open Knowledge Share


This Video is a part of Deep Learning Tutorial Series from Open Knowledge Share. In this video, we explain how to visualize Word Embeddings using Tensorflow Please refer the following playlist which contains all the Videos in the Deep Learning Tutorial Series. 🤍

Illustrated Guide to Transformers Neural Network: A step by step explanation


Transformers are the rage nowadays, but how do they work? This video demystifies the novel neural network architecture with step by step explanation and illustrations on how transformers work. CORRECTIONS: The sine and cosine functions are actually applied to the embedding dimensions and time steps! Audo Studio | Automagically Make Audio Recordings Studio Quality 🤍 Magic Mic | Join waitlist and get it FREE forever when launched! 🎙️ 🤍 Audo AI | Audio Background Noise Removal Developer API and SDK 🤍 Subscribe to my email newsletter for updated Content. No spam 🙅‍♂️ only gold 🥇. 🤍 Discord Server: Join a community of A.I. Hackers 🤍 Hugging Face Write with Transformers 🤍

How to use Aesthetic Gradients: Stable Diffusion Tutorial


A new paper "Personalizing Text-to-Image Generation via Aesthetic Gradients" was published which allows for the training of a special "aesthetic embedding" which allows the user to specify more clearly what they want to any existing stable diffusion model. In this tutorial we walk through how to train an aesthetic embedding, and use it to generate images. Discord: 🤍 00:00 - Summary 01:07 - Paper Explanation 06:51 - Webui Installation 10:25 - Aesthetic Gradients Installation 11:45 - Using Pretrained Embeddings 21:50 - Training New Embedding 29:40 - Comparing Embeddings 34:39 - Experiment Outcomes Requirements python 3.10 Local Nvidia GPU CUDA 11.3+ Links Aesthetic Gradients Paper: 🤍 GIthub Desktop: 🤍 Webui: 🤍 Huggingface Stable Diffusion 1.4 Model: 🤍 Aesthetic Gradients Extension: 🤍 Premade Aesthetic Embeddings: 🤍 CLIP search of LAION: 🤍 Code for downloading LAION images: 🤍 Script to download LAION: 🤍 Webui thread on embeddings: 🤍 Another webui thread on embeddings: 🤍 Github Repo for original implementaion: 🤍 Useful Blog Post: 🤍 Github Repo containing the portrait embeddings: 🤍 Misc ffmpeg commands to extract images from videos: ffmpeg resize and crop video: ffmpeg -i ghib.mp4 -c:a copy -filter:v "scale=960:512,crop=iw-448:ih-0" smol-ghib.mp4 ffmpeg extract images: ffmpeg -i smol-ghib.mp4 -r 0.3 -f image2 image-%4d.jpeg Music Music from 🤍 ‘Late Morning’ by ‘LuKremBo’:🤍 ‘Daily’ by ‘LuKremBo’:🤍 ‘Marshmallow’ by ‘LuKremBo’:🤍 ‘Travel’ by ‘LuKremBo’:🤍 ‘Sunset’ by ‘LuKremBo’:🤍 ‘Biscuit’ by ‘LuKremBo’:🤍 ‘Sunflower’ by ‘LuKremBo’: 🤍 ‘Chocolate’ by ‘LuKremBo’: 🤍 ‘Branch’ by ‘LuKremBo’: 🤍 ‘Rose’ by ‘LuKremBo’: 🤍 ‘Butter’ by LuKremBo: 🤍 ‘Onion’ by LuKremBo: 🤍 ‘Animal Friends’ by LuKremBo: 🤍 ‘Snow’ by LuKremBo: 🤍 ‘Affogato’ by LuKremBo: 🤍 Many thanks to LuKremBo #stablediffusion #aiart #tutorials #techtutorials #promptcrafting #install #installation #researchpaper

I sold my OpenAI GPT Website for $30,000


This last year I designed and developed a Website using React and AI as a SaaS from scratch in just a month, and now I've sold it. I was able to make just over $30,000 from the whole experience! This video was also sponsored by APILayer, check them out below! ⭐ Try the OpenAI Template - Starter Kit I've made ⭐ 🤍 This is new and ready for those looking to kickstart their own app or website using the OpenAI GPT models. I've built it on MERN it should save you over 100 hours if you want to build or test your own ideas! This also supports the channel if you have found these videos of benefit! p.s. I read all comments :) New - ChatGPT Starter Kit 🤍 APILayer - API Marketplace (Coupon: AdrianT20) 🤍 For those following along, I build Enhance AI not long ago which went live at The website used OpenAI together with GPT3 to perform code completion and other features like blogging for developers. As a programmer, this was my first proper successful software as a service, and I was able to get heaps of people signed up and using it. I coded it using react, MongoDB, NodeJs, HTML, CSS, JS and lots more. As part of the software development, (which require lots of javascript to work in the end! haha) I found the blogging aspect was the most successful. However additional effort didn't yield better results. 🤍SimonHoiberg recommended I check out microaquire and try selling it. And I did! Micro acquire was a cool site and I learned a lot from the whole experience, including how to negotiate a contract, terms and conditions, and lots more! #website #saas #development Learn Design for Developers! A book I've created to help you improve the look of your apps and websites. 📘 Enhance UI: 🤍 Feel free to follow me on: 🐦 Twitter: 🤍 💬 Discord: 🤍 💸 Patreon: 🤍 Software & Discounts: 🚾 Webflow: 🤍 🌿 Envato: 🤍 🌿 Envato Elements: 🤍 🔴 Elementor: 🤍 ✖ Editor X: 🤍 Computer Gear: ⬛ Monitor: 🤍 ⌨ Keyboard: 🤍 🐁 Mouse: 🤍 🎤 Mic: 🤍 📱 Tablet: 🤍 💡 Lighting: 🤍 💡 Key Lighting: 🤍 Camera Equipment: 📷 Camera: 🤍 📸 Primary Lens: 🤍 📸 Secondary Lens: 🤍 🎥 Secondary Camera: 🤍 🎙 Camera Mic: 🤍 🎞 USB to HDMI: 🤍

How to Build an AI Document Chatbot in 10 Minutes


🚀 Kick-start your freelance career in data: 🤍 Easily Build LLMs Apps - In this video, we are going to explore Flowise, an open-source UI visual tool to build your customized LLM flow using LangchainJS, written in Node Typescript/Javascript. 🔗 Links 🤍 🤍 🤍 🤍 🤍 👋🏻 About Me Hey there, my name is 🤍daveebbelaar and I work as a freelance data scientist and run a company called Datalumina. You've stumbled upon my YouTube channel, where I give away all my secrets when it comes to working with data. I'm not here to sell you any data course — everything you need is right here on YouTube. Making videos is my passion, and I've been doing it for 18 years. While I don't sell any data courses, I do offer a coaching program for data professionals looking to start their own freelance business. If that sounds like you, head over to 🤍 to learn more about working with me and kick-starting your freelance career.

From Documents to Vectors: ChatGPT's Technical Marvels with OpenAI's Plugins


Are you ready to dive into the technical aspects of OpenAI's powerful API and plugins? Join us as we embark on an exciting journey through real-time data retrieval with ChatGPT. In this video, we delve into the concept of a vector database, showcasing how it stores the vector representation of external documents to enhance ChatGPT's capabilities. Discover various options for vector store, including open-source versions and commercial solutions, and witness their impact on data retrieval and analysis. Get ready for a captivating exploration of cutting-edge technologies and gain insights into the seamless integration of ChatGPT with real-world applications. Technical aspects of OpenAI's API Endpoints (GPT-4, ChatGPT) and their role in real-time data retrieval with OpenAI's plugins. The future of AI? Short explanation of the concept of an API (Application Programming Interface) and how it facilitates communication between different software systems. OpenAI's retrieval plugin, which is built using FastAPI, a Python web framework, is highlighted. The plugin communicates with various elements such as databases, Google's Search API, YouTube's Data API, and the archive pre-print server, eg via ScholarAI. Technical summary that a plugin consists of an API, an API schema, and a manifest, a JSON file that defines the plugin's metadata. OpenAI's retrieval plugin uses ADA-002 embeddings for semantic search, which is more complex than a simple keyword search but provides more relevant results. OpenAI's plugin has four main API endpoints: upsert (for adding new documents to the vector database), upsert-file (for handling file uploads), query (for searching the vector database), and delete (for removing documents from the vector database). The video presents the concept of a vector database, which stores the vector representations of documents. Open source Vector DB options such as Weaviate and Milvus are mentioned, as well as commercial solutions like Azure's Cognitive Search. The presenter emphasizes the importance of security, noting that both OpenAI and the vector database provider require authentication tokens. At the end of the video a short overview of the concept of hybrid search, which combines "neural search" and index search (like TF-IDF) to provide more effective results for complex queries on GPT-4. Concludes by promising to delve deeper into the flow of data between GPT-4, the OpenAI plugin, and Azure Cognitive Search in the next video. #ai #gpt4 #plugins

HELM in GridCal: Holomorphic Embedding Power Flow - closed vs open science approach


This is a Talk presented by Santiago Peñate in the context of the 1st Open Energy Modelling Initiative Online Lightning Talk Mini-workshop. For background on Open Energy Modelling Initiative workshops, please visit: 🤍

CHS Kosmos Society Open House | Persian epic & the embedding of a song of lament, with O. Davidson


For further details of the article and publications referred to in this video, please see: 🤍

Embedding Privacy by Design Into Data Infrastructure Through Open-Source, Extensible Tooling


The systemic privacy issues in our digital infrastructure stem largely from a fundamental design flaw: privacy is only considered reactively, once personal data is already flowing. Consumer trust is more valuable than ever, and the legal stakes for respecting personal data continue to climb. Appointing a privacy engineer to check boxes at the time of deployment won't cut it...the status quo for data context and data control - in other words, privacy controls - needs to change. Analogous to AppSec's leftward shift, privacy responsibility lies with builders and maintainers of data and software systems. This requires resources for developers to embrace their role in tasks like evaluating privacy risk with minimal friction, compatible with the array of modern data infrastructure. Cillian will share actionable steps to implement Privacy by Design and offer just one example of what it could look like in action with open-source devtools for automated privacy checks in the CI pipeline. Connect with us: Website: 🤍 Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍 Instagram: 🤍



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I tested a STACK of FREE Large Language's how it went.


👨‍💻 Sign up for the Full Stack course and use YOUTUBE50 to get 50% off: 🤍 🐍 Get the free Python course 🤍 Hopefully you enjoyed this video. 💼 Find AWESOME ML Jobs: 🤍 🤖 Get the Code: 🤍 Oh, and don't forget to connect with me! LinkedIn: 🤍 Facebook: 🤍 GitHub: 🤍 Patreon: 🤍 Join the Discussion on Discord: 🤍 Happy coding! Nick

The text-to-image revolution, explained


How programmers turned the internet into a paintbrush. DALL-E 2, Midjourney, Imagen, explained. Subscribe and turn on notifications 🔔 so you don't miss any videos: 🤍 Beginning in January 2021, advances in AI research have produced a plethora of deep-learning models capable of generating original images from simple text prompts, effectively extending the human imagination. Researchers at OpenAI, Google, Facebook, and others have developed text-to-image tools that they have not yet released to the public, and similar models have proliferated online in the open-source arena and at smaller companies like Midjourney. These tools represent a massive cultural shift because they remove the requirement for technical labor from the process of image-making. Instead, they select for creative ideation, skillful use of language, and curatorial taste. The ultimate consequences are difficult to predict, but — like the invention of the camera, and the digital camera thereafter — these algorithms herald a new, democratized form of expression that will commence another explosion in the volume of imagery produced by humans. But, like other automated systems trained on historical data and internet images, they also come with risks that have not been resolved. The video above is a primer on how we got here, how this technology works, and some of the implications. And for an extended discussion about what this means for human artists, designers, and illustrators, check out this bonus video: 🤍 Midjourney: 🤍 List of free AI Art tools: 🤍 Sources: 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 🤍 Make sure you never miss behind the scenes content in the Vox Video newsletter, sign up here: 🤍 is a news website that helps you cut through the noise and understand what's really driving the events in the headlines. Check out 🤍 Support Vox's reporting with a one-time or recurring contribution: 🤍 Shop the Vox merch store: 🤍 Watch our full video catalog: 🤍 Follow Vox on Facebook: 🤍 Follow Vox on Twitter: 🤍 Follow Vox on TikTok: 🤍

Transformer Neural Networks - EXPLAINED! (Attention is all you need)


Please subscribe to keep me alive: 🤍 BLOG: 🤍 MATH COURSES (7 day free trial) 📕 Mathematics for Machine Learning: 🤍 📕 Calculus: 🤍 📕 Statistics for Data Science: 🤍 📕 Bayesian Statistics: 🤍 📕 Linear Algebra: 🤍 📕 Probability: 🤍 OTHER RELATED COURSES (7 day free trial) 📕 ⭐ Deep Learning Specialization: 🤍 📕 Python for Everybody: 🤍 📕 MLOps Course: 🤍 📕 Natural Language Processing (NLP): 🤍 📕 Machine Learning in Production: 🤍 📕 Data Science Specialization: 🤍 📕 Tensorflow: 🤍 REFERENCES [1] The main Paper: 🤍 [2] Tensor2Tensor has some code with a tutorial: 🤍 [3] Transformer very intuitively explained - Amazing: 🤍 [4] Medium Blog on intuitive explanation: 🤍 [5] Pretrained word embeddings: 🤍 [6] Intuitive explanation of Layer normalization: 🤍 [7] Paper that gives even better results than transformers (Pervasive Attention): 🤍 [8] BERT uses transformers to pretrain neural nets for common NLP tasks. : 🤍 [9] Stanford Lecture on RNN: 🤍 [10] Colah’s Blog: 🤍 [11] Wiki for timeseries of events: 🤍

Embedding an Open Textbook in Canvas Course Navigations


This video explains how to embed an open textbook into a course's main course navigation.

[Open DMQA Seminar] Contrastive Learning for Sentence Embedding


Contrastive learning(CL)은 self supervised learning의 한 방법론이다. 데이터 간의 유사도를 바탕으로 비슷한 데이터끼리는 가까워지게, 다른 데이터끼리는 멀어지게 학습한다. CL은 비전 분야에서 뛰어난 성과를 거두었지만 자연어 처리 분야에서는 큰 성과를 이루지 못했다. 그 이유는 자연어 데이터를 잘못 augmentation하면, 문장의 의미를 크게 해쳐 positive와 negative 의미가 사라지기 때문이다. 최근 자연어 처리 연구에서는 이러한 한계를 극복하는 연구가 수행되었다. 본 세미나에서는 자연어 처리 분야에 CL을 적용한 방법론에 대해서 설명하고자 한다. 참고문헌: [1] Giorgi, J., Nitski, O., Wang, B., & Bader, G. (2020). Declutr: Deep contrastive learning for unsupervised textual representations. arXiv preprint arXiv:2006.03659. [2] Gao, T., Yao, X., & Chen, D. (2021). Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821. [3] Chuang, Y. S., Dangovski, R., Luo, H., Zhang, Y., Chang, S., Soljačić, M., ... & Glass, J. (2022). DiffCSE: Difference-based contrastive learning for sentence embeddings. arXiv preprint arXiv:2204.10298.

High Resolution PCAP Slim 12.1'' Open Frame Monitor For Embedding Installation


iTAYFT Embedded Touch Monitors are durable and rugged. And they can work over an extended temperature range, in extreme environments, be glove operated, withstand heavy impacts and be capable of being washed down. iTAYFT innovative solutions in touch screen technology are used across an incredibly diverse range of markets, as embedded solutions in appliance control panels, monitors, all-in-one computers, kiosks and various industrial devices, etc. Welcome to visit our official website : 🤍

What is BERT? | Deep Learning Tutorial 46 (Tensorflow, Keras & Python)


What is BERT (Bidirectional Encoder Representations From Transformers) and how it is used to solve NLP tasks? This video provides a very simple explanation of it. I am not going to go in details of how transformer based architecture works etc but instead I will go over an overview where you understand the usage of BERT in NLP tasks. In coding section we will generate sentence and word embeddings using BERT for some sample text. We will cover various topics such as, * Word2vec vc BERT * How BERT is trained on masked language model and next sentence completion task ⭐️ Timestamps ⭐️ 00:00 Introduction 00:39 Theory 11:00 Coding in tensorflow Code: 🤍 BERT article: 🤍 Word2Vec video: 🤍 Do you want to learn technology from me? Check 🤍 for my affordable video courses. Deep learning playlist: 🤍 Machine learning playlist: 🤍   🔖Hashtags🔖 #bertmodelnlppython #tensorflowbert #tensorflowberttutorial #bert #bertneuralnetwork #bertdeeplearning #whatisbert #bertnlp #bertindeeplearning #bertmodel #bertmodelnlp 🌎 My Website For Video Courses: 🤍 Need help building software or data analytics and AI solutions? My company 🤍 can help. Click on the Contact button on that website. 🎥 Codebasics Hindi channel: 🤍 #️⃣ Social Media #️⃣ 🔗 Discord: 🤍 📸 Dhaval's Personal Instagram: 🤍 📸 Instagram: 🤍 🔊 Facebook: 🤍 📱 Twitter: 🤍 📝 Linkedin (Personal): 🤍 📝 Linkedin (Codebasics): 🤍 ❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.

Embedding Organics In Open Back Bezels


How To Use Organics In Open Back Nunn Design Bezels With 2 Part Resin

OpenAI Product RoadMap 2023-2024 - Sam Altman


Details - 🤍 ❤️ If you want to support the channel ❤️ Support here: Patreon - 🤍 Ko-Fi - 🤍

Open Access Wikipedia Challenge - Embedding Media


How to look at nominated open access files on Wikimedia commons and place them into Wikipedia pages. This is part of the series Open Access Wikipedia Challenge, available at P2PU 🤍 Produced by Max Klein OCLC Wikipedian in Residence 🤍

How to Link Excel to PowerPoint | Excel to PPT


In this step-by-step tutorial, learn how you can link a Microsoft Excel spreadsheet to a PowerPoint presentation. When you update the table or chart in Excel, PowerPoint will also update. 👋 Additional resources - How to make animated cartoon character of yourself: 🤍 - Learn the fundamentals of Excel in just 2 hours: 🤍 ⌚ Timestamps 0:00 Introduction 0:49 Link Excel table to PowerPoint 3:10 Link Excel chart to PowerPoint 5:33 Wrap up 📃 Watch related playlists - Playlist with all my videos on Excel: 🤍 - Playlist with all my videos on PowerPoint: 🤍 🚩 Connect with me on social - LinkedIn: 🤍 - Twitter: 🤍 - Facebook: 🤍 - TikTok: 🤍 - Instagram: 🤍 🔔 Subscribe to my YouTube channel 🤍 🎬 Want to watch again? Navigate back to my YouTube channel quickly 🤍 🛍 Support me with your Amazon purchases: 🤍 ⚖ As full disclosure, I use affiliate links above. Purchasing through these links gives me a small commission to support videos on this channel the price to you is the same. #stratvert

How to Insert YouTube Video in PowerPoint


In this step-by-step tutorial video, learn how to insert a video on YouTube into a Microsoft PowerPoint presentation. Along with embedding the video, I also show advanced tips, such as starting a YouTube video at a certain timestamp, formatting the video brightness, color, and frame, and setting it to start automatically. 0:00 Introduction 0:19 Insert a video in PowerPoint 1:27 Copy YouTube video URL 3:07 Paste YouTube video URL into PowerPoint 3:46 Adjust video format and poster frame / thumbnail 5:27 Adjust playback settings (e.g. start automatically) 5:57 Adjust video size & position 6:32 Wrap up Playlist of all my PowerPoint videos: 🤍 Playlist of all my YouTube videos: 🤍

Singular Value Decomposition (SVD): Overview


This video presents an overview of the singular value decomposition (SVD), which is one of the most widely used algorithms for data processing, reduced-order modeling, and high-dimensional statistics. These lectures follow Chapter 1 from: "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz Amazon: 🤍 Book Website: 🤍 Chapters available at: 🤍 Brunton Website: 🤍

Weekly Series: Azure Open AI Service - Parte 4 Embeddings


En esta sesión vamos a continuar con nuestro aprendizaje sobre (Azure) Open AI, vamos a utilizar la característica de Embeddings a fin de proporcionarle al modelo existende de GPT-3 toda la información que necesita (nuestros datos) para responder preguntas con mayor certeza.

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