@minsupark9246
This lecture on vector embedding is undoubtedly one of the best I've encountered! Huge thanks to Ania and FCC! Kudos to you all!
@NAITIK-17-17
do you agree that this legendary channel is better than most paid courses for coding out there?
@MarcusNeufeldt
🎯 Key Takeaways for quick navigation: 00:00 📘 Anikubo's course covers vector embeddings using OpenAI's GPT-4. 01:49 🖥️ Vector embeddings transform various data types into numeric forms for algorithm processing. 06:12 📈 Numbers can represent complex data, and cosine similarity helps compare them. 08:04 🌐 Embeddings find applications in recommendation systems, NLP tasks, and more. 14:04 🛠️ LangChain, an open-source framework, enhances AI interactions, chaining models and data. 23:25 🛠️ The tutorial walks through setting up a Python environment and key scripting steps. 24:22 ⚙️ Essential packages and tools are installed for AI development. 33:17 🤖 The AI assistant, using vector-based search, fetches relevant documents from a database. Made with HARPA AI
@Sanyat100
You really are one of the best teachers.
@ClarkRuell
10:51 THIS is a 'golden nugget' right here: "The core advantage of vector embeddings..." Such a great summation of exactly what an ai model really is. Thanks for such a fantastic video. I love it !
@VittorioLizzerri
Ania, that was freaking amazing! You simplified all the concepts without going too high-level and dumbing it down altogether. You told us what happens and showed us HOW they happen. I found this very informative and you answered so many questions I have been pondering. I'm not a developer or an AI person, I'm a network engineer. So, thank you... By the way, I used an embedding model to map your face, and the semantic engine returned the words "gorgeous," "lovely," "beautiful," etc... 🙂
@Lemure_Noah
Simply love this presentation! That vec math (King - man + woman = Queen) just blow my mind!
@I61void
Wow the instructor for this vid was actually amazing. I only clicked on it because It was 30 minutes long, having no real intention to actually learn and just have play in the background while I read a textbook for fizz. The instructor was phenomenal, I understood everything she said, every instruction was clear to follow although I only really know some JavaScript and cpp. I actually learned a few things. Before the video began like I said no real intentions of implementing this but since I actually learned and understood pretty much all of it I could see myself actually implementing it on some project and adding it to my resume. Would be cool. Thanks
@kangmoabel
Can we stop just for a moment and appreciate her!!! learned loads of thing from you!!! hats off 🎓🎓 ❤❤ love you from Ethiopia
@maddisoncore
Incredibly helpful !! I love how thorough or concise the explanations were in the right time and place. Many thanks to the lovely instructor, Ania Kubow, and freeCodeCamp 😍🙏
@asutoshpanda7998
You're dazzling and wise, a true blend of grace and intellect.
@ActiveAndReactive
This girl is an ideal perfect educator!
@SpiritOfIndiaaa
Really great, she has great presentation skills , to the point which needed demos ...clarity in tone, content and body language...thanks a lot.
@andreyklepikov7084
You have a talent to deliver complex information in a very interesting manner! Waiting for more videos!
@MaverickCoder-mz6hp
Nice introduction to vector embeddings with clear explanation. One of the best lecture/videos encountered on you tube related to this topic.
@ChameeraDedduwage
Wow. Probably the best lecture to meaningfully explain what vector embeddings are, and how cosine similarity works. Thank you very much!
@ShadowMind312
I did this in my Numerical Analysis course using Maple and MatLab. Then i did some analysis on images when i took Fourier analysis. Shame i never got much chance to use it professionally, as i tend to work with financial data.
@ARGHYABINDUPATRA-g3b
Timestamps (Powered by Merlin AI) 00:04 - Learn about Vector embeddings 02:10 - Text embeddings represent words' semantic meaning. 06:42 - Text embeddings can be used to compare and manipulate words 09:02 - Vector embeddings can be used for various applications, including recommendation systems, anomaly detection, transfer learning, visualizations, information retrieval, natural language processing, audio and speech processing, and facial recognition. 13:53 - Vector embeddings are important for AI models and require a purpose-built database for storage and access. 16:24 - Create a Vector database in Stacks and log in 20:39 - Get token and secure connect bundle, and API key 22:46 - Setting up a Python environment in VS Code 26:53 - Set up API keys and client details 28:45 - Importing required packages and configuring connections 32:48 - The script generates embeddings, stores them in an astro DB, and determines the number of headlines inserted. 35:00 - Silicon Valley and vector search explained
@akinyemisodiq6885
dishing it out like piece of cake,
@aniakubow