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Vector Databases simply explained. Learn what vector databases and vector embeddings are and how they work. Then I'll go over some use cases for it and I briefly show you different options you can use. Resources: - Gentle introduction: https://frankzliu.com/blog/a-gentle-introduction-to-vector-databases - What is a vector database: https://www.pinecone.io/learn/vector-database/ Get your Free Token for AssemblyAI👇 https://www.assemblyai.com/?utm_source=youtube&utm_medium=referral&utm_campaign=yt_pat_75 00:00 - Intro 00:44 - Why do we need vector databases 01:29 - Vector embeddings and indexes 02:58 - Use cases 03:45 - Different vector databases Vector Database Options: - Pinecone - Weaviate - Chroma - Redis - Qdrant - Milvus - Vespa ▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬ 🖥️ Website: https://www.assemblyai.com 🐦 Twitter: https://twitter.com/AssemblyAI 🦾 Discord: https://discord.gg/Cd8MyVJAXd ▶️ Subscribe: https://www.youtube.com/c/AssemblyAI?sub_confirmation=1 🔥 We're hiring! Check our open roles: https://www.assemblyai.com/careers ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ #MachineLearning #DeepLearning

Embeddings and Vectors are a great way of storing and retrieving information for use with AI services. OpenAI provides a great embedding API to do this. Postman lets you make these with easy at https://www.postman.com/ (today's sponsor) In this video we will explore how to create a Vector Database by creating embeddings using the OpenAI API and then storing them in SingleStore. The first part of the video will cover how to create an embedding using just API requests with Postman. Then we will jump into Single Store and store these in a new database made specifically for vectors like this. 00:00 - Introduction 00:10 - What are Embeddings and Vectors 02:14 - Setup OpenAI Embeddings 03:11 - Setup Postman API Requests to create Embeddings 03:55 - Create Embedding 03:55 - Create Embedding 06:55 - Create PDF or Document Embedding 07:43 - Vector Database - Setup with SingleStore 08:20 - Vector Database - Create Database 09:32 - Vector Database - Create Table 10:41 - Vector Database - Insert Embedding Row 13:25 - Vector Database - Search Embeddings 15:18 - Embedding function with JavaScript and NodeJS 18:08 - OpenAI and GPT Digital Book 18:29 - Conclusion Postman: (today's Sponsor) for API Requests https://www.postman.com/ OpenAI Embedding Documentation: https://platform.openai.com/docs/api-reference/embeddings SingleStore Vector Database https://www.singlestore.com/cloud-trial/?utm_campaign=adrian-twarog&utm_medium=video&utm_source=youtube ⭐ Teach Me OpenAI & GPT - Digital Book ⭐ https://enhanceui.gumroad.com/l/teach-me-openai Learn Design for Developers! A book I've created to help you improve the look of your apps and websites. 📘 Enhance UI: https://www.enhanceui.com/ Feel free to follow me on: 🐦 Twitter: https://twitter.com/intent/follow?screen_name=adrian_twarog 💬 Discord: https://discord.gg/6gmmFvxkD7 💸 Patreon: https://www.patreon.com/adriantwarog Software & Discounts: 🚾 Webflow: https://webflow.grsm.io/adrian 🌿 Envato: https://1.envato.market/yRZjz2 🌿 Envato Elements: https://1.envato.market/LP0OJZ 🔴 Elementor: https://elementor.com/adrian/?ref=23140 ✖ Editor X: https://www.editorx.com/adrian-twarog Computer Gear: ⬛ Monitor: https://amzn.to/3f9DOQI ⌨ Keyboard: https://amzn.to/3eA5UFD 🐁 Mouse: https://amzn.to/3xVJO8l 🎤 Mic: https://amzn.to/3hgCfms 📱 Tablet: https://amzn.to/3ewt7sa 💡 Lighting: https://amzn.to/3vOZeZY 💡 Key Lighting: https://amzn.to/3f6qP2f Camera Equipment: 📷 Camera: https://amzn.to/3uCv4J9 📸 Primary Lens: https://amzn.to/3vT6wMm 📸 Secondary Lens: https://amzn.to/3tyqWIX 🎥 Secondary Camera: https://amzn.to/3o2zCGi 🎙 Camera Mic: https://amzn.to/33tCz9l 🎞 USB to HDMI: https://amzn.to/33yW9RE

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Find out everything you need to know about learning AI in 2023, from tips to get you started, helpful resources, and insights from industry experts.

1/ Starting my road to learning about AI. I’ve done some courses here and there but I am planning a more consistent effort over the next few years. Mostly documenting my path for others and for fun. Often people suggest things I didn’t know which can be helpful.

Learn Machine Learning in a way that is accessible to absolute beginners. You will learn the basics of Machine Learning and how to use TensorFlow to implement many different concepts. ✏️ Kylie Ying developed this course. Check out her channel: https://www.youtube.com/c/YCubed ⭐️ Code and Resources ⭐️ 🔗 Supervised learning (classification/MAGIC): https://colab.research.google.com/drive/16w3TDn_tAku17mum98EWTmjaLHAJcsk0?usp=sharing 🔗 Supervised learning (regression/bikes): https://colab.research.google.com/drive/1m3oQ9b0oYOT-DXEy0JCdgWPLGllHMb4V?usp=sharing 🔗 Unsupervised learning (seeds): https://colab.research.google.com/drive/1zw_6ZnFPCCh6mWDAd_VBMZB4VkC3ys2q?usp=sharing 🔗 Dataets (add a note that for the bikes dataset, they may have to open the downloaded csv file and remove special characters) 🔗 MAGIC dataset: https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope 🔗 Bikes dataset: https://archive.ics.uci.edu/ml/datasets/Seoul+Bike+Sharing+Demand 🔗 Seeds/wheat dataset: https://archive.ics.uci.edu/ml/datasets/seeds 🏗 Google provided a grant to make this course possible. ⭐️ Contents ⭐️ ⌨️ (0:00:00) Intro ⌨️ (0:00:58) Data/Colab Intro ⌨️ (0:08:45) Intro to Machine Learning ⌨️ (0:12:26) Features ⌨️ (0:17:23) Classification/Regression ⌨️ (0:19:57) Training Model ⌨️ (0:30:57) Preparing Data ⌨️ (0:44:43) K-Nearest Neighbors ⌨️ (0:52:42) KNN Implementation ⌨️ (1:08:43) Naive Bayes ⌨️ (1:17:30) Naive Bayes Implementation ⌨️ (1:19:22) Logistic Regression ⌨️ (1:27:56) Log Regression Implementation ⌨️ (1:29:13) Support Vector Machine ⌨️ (1:37:54) SVM Implementation ⌨️ (1:39:44) Neural Networks ⌨️ (1:47:57) Tensorflow ⌨️ (1:49:50) Classification NN using Tensorflow ⌨️ (2:10:12) Linear Regression ⌨️ (2:34:54) Lin Regression Implementation ⌨️ (2:57:44) Lin Regression using a Neuron ⌨️ (3:00:15) Regression NN using Tensorflow ⌨️ (3:13:13) K-Means Clustering ⌨️ (3:23:46) Principal Component Analysis ⌨️ (3:33:54) K-Means and PCA Implementations 🎉 Thanks to our Champion and Sponsor supporters: 👾 Raymond Odero 👾 Agustín Kussrow 👾 aldo ferretti 👾 Otis Morgan 👾 DeezMaster -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

In this video, I give you my step by step process on how I would learn Machine Learning if I could start over again, and provide you with all recommended resources. All courses: https://github.com/AssemblyAI-Examples/ML-Study-Guide Get your Free Token for AssemblyAI Speech-To-Text API 👇 https://www.assemblyai.com/?utm_source=youtube&utm_medium=referral&utm_campaign=yt_pat_60 ▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬ 🖥️ Website: https://www.assemblyai.com 🐦 Twitter: https://twitter.com/AssemblyAI 🦾 Discord: https://discord.gg/Cd8MyVJAXd ▶️ Subscribe: https://www.youtube.com/c/AssemblyAI?sub_confirmation=1 🔥 We're hiring! Check our open roles: https://www.assemblyai.com/careers ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ #MachineLearning #DeepLearning 0:00 Introduction 1:01 MATH 1:58 PYTHON PYTHON 2:37 ML TECH STACK ML TECH STACK 3:35 ML COURSES ML COURSES 4:44 HANDS-ON & DATA PREPARATION 5:17 PRACTICE & PRACTICE & BUILD PORTFOLIO 6:16 SPECIALIZE & CREATE BLOG

1. Learn broadly. 2. Learn deeply. 3. Don't be afraid to re-learn

Free monthly learning resources and insights https://gilesknowledge.substack.com/ Here are the links to the machine learning resources mentioned: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/ https://opentechschool.github.io/python-data-intro/core/recap.html https://www.kaggle.com/learn/intro-to-machine-learning https://developers.google.com/machine-learning/crash-course/ https://www.tensorflow.org/tutorials https://machinelearningmastery.com/start-here/ https://www.youtube.com/playlist?list=PL8erL0pXF3JYm7VaTdKDaWc8Q3FuP8Sa7 https://mml-book.github.io/ https://www.probabilitycourse.com/chapter1/1_1_0_what_is_probability.php https://www.statisticsdonewrong.com/ https://github.com/ageron/handson-ml3 https://courses.cs.duke.edu/spring20/compsci527/papers/Domingos.pdf https://datascience.stackexchange.com/ https://stats.stackexchange.com/?tags=machine-learning https://pytorch.org/tutorials/ https://scikit-learn.org/stable/tutorial/index.html That's 16 in total! Learn Data Science 🎓 Data Quest - https://bit.ly/3yClqbZ Learn Python with Giles 🎓 Exploratory Data Analysis with Python and Pandas - https://bit.ly/2QXMpxJ 🎓 Complete Python Programmer Bootcamp - http://bit.ly/2OwUA09 📚 My favourite python books for beginners (affiliate links) 📗 Python Crash Course 2nd Edition https://amzn.to/33tATAE 📘 Automate the Boring Stuff with Python https://amzn.to/3qM1DFl 📙 Python Basics - A Practical Introduction to Python 3 https://amzn.to/3fHRMdb 📕 Python Programming An Introduction to Computer Science https://amzn.to/33VeQCr 📗 Invent Your Own Computer Games with Python https://amzn.to/3FM3H4b 🆓 Free Python Resource https://python-programming.quantecon.org/intro.html (This is a great introduction to python) ⚙ My Gear 💡 BenQ Screen Bar Desk Light - https://amzn.to/3tH6ysL 🎧 Sony Noise Cancelling Headphones - https://amzn.to/3tLl82G 📱 Social Media https://www.instagram.com/gilesmcmullen/ https://twitter.com/GilesMcMullen 👌 SUBSCRIBE to ME!👌 https://www.youtube.com/channel/UC68KSmHePPePCjW4v57VPQg?sub_confirmation=1 #learnmachinelearning #machinelearning #learnpython

Let’s Understand The Model Architecture

In the rapidly evolving landscape of artificial intelligence, Generative AI, especially Language Model Machines (LLMs) have emerged as the…

In the age of burgeoning data complexity and high-dimensional information, traditional databases...

A Roadmap to Becoming an AI Engineer — From Zero to AI Engineer

Explore this conversation and more from the learnmachinelearning community

Explore this post and more from the learnmachinelearning community

Getting into machine learning is quite the adventure. And as any adventurer knows, sometimes it can be helpful to have a compass to figure out if you're heading in the right direction. Although the title of this video says machine learning roadmap, you should treat it as a compass. Explore it, follow your curiosity, learn something and use what you learn to create your next steps. Links: Interactive Machine Learning Roadmap - https://dbourke.link/mlmap Machine Learning Roadmap Resources - https://github.com/mrdbourke/machine-learning-roadmap Learn ML (beginner-friendly courses I teach) - https://www.mrdbourke.com/ml-courses/ ML courses/books I recommend - https://www.mrdbourke.com/ml-resources/ Read my novel Charlie Walks - https://www.charliewalks.com Timestamps: 0:00 - Hello & logistics 0:57 - PART 0: INTRO 1:42 - Brief overview of topics 3:05 - What is machine learning? 4:37 - Machine learning vs. traditional programming 7:41 - Why use machine learning? 8:44 - The number 1 rule of machine learning 10:45 - What is machine learning good for? 14:27 - How Tesla uses machine learning 17:57 - What we're going to cover in this video 20:52 - PART 1: Machine Learning Problems 22:27 - Categories of learning 26:17 - Machine learning problem domains 29:04 - Classification 33:57 - Regression 39:35 - PART 2: Machine Learning Process 41:57 - 6 major steps in a machine learning project 43:57 - Data collection 49:15 - Data preparation 1:04:00 - Training a model 1:23:33 - Analysis/evaluation 1:26:40 - Serving a model 1:29:09 - Retraining a model 1:30:07 - An example machine learning project 1:33:15 - PART 3: Machine Learning Tools 1:34:20 - Machine learning tools overview 1:38:36 - Machine learning toolbox (experiment tracking) 1:39:54 - Pretrained models for transfer learning 1:41:49 - Data and model tracking 1:43:35 - Cloud compute services 1:47:07 - Deep learning hardware (build your own deep learning PC) 1:47:53 - AutoML (automatic machine learning) 1:51:47 - Explainability (explaining the outputs of your machine learning model) 1:53:38 - Machine learning lifecycle (tools for end-to-end projects) 1:59:24 - PART 4: Machine Learning Mathematics 1:59:37 - The main branches of mathematics used in machine learning 2:03:16 - How I learn the math for machine learning 2:06:37 - PART 5: Machine Learning Resources 2:07:17 - A warning 2:08:42 - Where to start learning machine learning 2:14:51 - Made with ML (one of my favourite new websites for ML) 2:16:07 - Wokera ai (test your AI skills) 2:17:17 - A beginner-friendly path to start machine learning 2:19:02 - An advanced path for learning machine learning (after the beginner path) 2:21:43 - Where to learn the mathematics for machine learning 2:22:23 - Books for machine learning 2:24:27 - Where to learn cloud services 2:24:47 - Helpful rules and tidbits of machine learning 2:26:05 - How and why you should create your own blog 2:28:29 - Example machine learning curriculums 2:30:19 - Useful machine learning websites to visit 2:30:59 - Open-source datasets 2:31:26 - How to learn how to learn 2:32:57 - PART 6: Summary & Next Steps Connect elsewhere: Get email updates on my work - https://dbourke.link/newsletter Support on Patreon - https://bit.ly/mrdbourkepatreon Web - https://dbourke.link/web Quora - https://dbourke.link/quora Medium - https://dbourke.link/medium Twitter - https://dbourke.link/twitter LinkedIn - https://dbourke.link/linkedin #machinelearning #datascience

Follow these roadmaps to become an Artificial Intelligence expert.

Normcore LLM Reads. GitHub Gist: instantly share code, notes, and snippets.

1. How to build AI tools like ChatGPT?2. How to learn Machine Learning?3. How to learn Deep Learning?I will answer all these questions in this video.► All li...

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What's the big deal, intuition on query-key-value vectors, multiple heads, multiple layers, and more.

Our team of software engineers, designers, project managers and other technology experts love to share insights and opinions. See this post by Jason Pollentier

Here’s everything you need to go from beginner to expert in AI:

Banana provides inference hosting on serverless GPUs for machine learning models. Deploy on Banana in three easy steps and a single line of code.

Proving you're a human on a web flooded with generative AI content

One remarkable byproduct of the AI proliferation boom bought about by ChatGPT is the amount of...