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Building LLMs - Stanford Course
#ai #generativeai #llm
https://www.youtube.com/watch?v=9vM4p9NN0Ts
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00:10 Building Large Language Models overviewЧитать полностью…
02:21 Focus on data evaluation and systems in industry over architecture
06:25 Auto regressive language models predict the next word in a sentence.
08:26 Tokenizing text is crucial for language models
12:38 Training a large language model involves using a large corpus of text.
14:49 Tokenization process considerations
18:40 Tokenization improvement in GPT 4 for code understanding
20:31 Perplexity measures model hesitation between tokens
24:18 Comparing outputs and model prompting
26:15 Evaluation of language models can yield different results
30:15 Challenges in training large language models
32:06 Challenges in building large language models
35:57 Collecting real-world data is crucial for large language models
37:53 Challenges in building large language models
41:38 Scaling laws predict performance improvement with more data and larger models
43:33 Relationship between data, parameters, and compute
47:21 Importance of scaling laws in model performance
49:12 Quality of data matters more than architecture and losses in scaling laws
52:54 Inference for large language models is very expensive
54:54 Training large language models is costly
59:12 Post training aligns language models for AI assistant use
1:01:05 Supervised fine-tuning for large language models
1:04:50 Leveraging large language models for data generation and synthesis
1:06:49 Balancing data generation and human input for effective learning
1:10:23 Limitations of human abilities in generating large language models
1:12:12 Training language models to maximize human preference instead of cloning human behaviors.
1:16:06 Training reward model using softmax logits for human preferences.
1:18:02 Modeling optimization and challenges in large language models (LLMs)
1:21:49 Reinforcement learning models and potential benefits
1:23:44 Challenges with using humans for data annotation
1:27:21 LLMs are cost-effective and have better agreement with humans than humans themselves
1:29:12 Perplexity is not calibrated for large language models
1:33:00 Variance in performance of GPT-4 based on prompt specificity
1:34:51 Pre-training data plays a vital role in model initialization
1:38:32 Utilize GPUs efficiently with matrix multiplication
1:40:21 Utilizing 16 bits for faster training in deep learning
1:44:08 Building Large Language Models from scratch
https://blog.google/products/google-cloud/ai-trends-business-2025/
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#AINews #Google
Differences between RAG, Agents and Agentic RAG
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Читать полностью…The AI for Impact Hackathon, presented by Google Cloud and powered by Hack2skill, is a unique opportunity to leverage the transformative power of AI to address pressing social challenges across the APAC region.
You can participate in the hackathon for free with below link.
Deadline: 17th Nov
Register : https://bit.ly/4hEcMQI
AI Python for beginners Free course for limited period by Andrew Ng
#ai #python
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https://www.deeplearning.ai/short-courses/ai-python-for-beginners/
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https://www.youtube.com/watch?v=wjZofJX0v4M
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Marketing strategy and AI-Free Webinar from Google
In this session you will learn:
How is AI changing the marketing landscape and what are the opportunities for marketers
AI-powered marketing tools you can try
Supercharging your digital marketing strategy
https://bit.ly/googleaimarketing
#ai #freecourse #marketing
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500 AI Machine Learning Projects list with code
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#github #projects
https://github.com/ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
Sponsored Post
Yachay.ai (https://www.yachay.ai/) has launched a machine learning hackathon focused on advancing the development of text-to-location models. Participants have the chance to win a prize of $500! If you're up for the challenge, here are instructions to participate: https://github.com/Yachay-AI/confident-predictions-challenge. The submission deadline is November 25, 2023.
Yachay.ai team has built and open-sourced a architecture for geotagging models that take textual inputs and produce coordinates as outputs. Codebase here: https://github.com/Yachay-AI/byt5-geotagging. Currently, they are actively seeking ways to enhance validation metrics.
If you are looking for ideas on leveraging geotagging models in your projects, join the discussion on potential use cases: https://discord.com/invite/PaqpppYTxs
Yachay.ai plans to release a downloadable model for Spanish texts and provide more free text/coordinates datasets in the near future. Stay tuned for updates!
Virtual Medicine has created the most advanced, multi-award winning medical anatomy platform crossing Virtual Reality (VR), Mixed Reality (MR), and Augmented Reality (AR) for medical education.
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#VR #AR
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Language translation + speech recognition + speech synthesis in a single model: speech-to-speech, text-to-text, speech-to-text and text-to-speech.
Works for 100 languages.
Code available under CC-BY-NC license.
From Meta - Fundamental AI Research.
#meta #speech #language #text
Blog post: https://bit.ly/45z0e6s
Details ➡️ https://bit.ly/47In6m0
Demo ➡️ https://bit.ly/45jjYLz
Code ➡️ https://bit.ly/3KRne8W
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Microsoft announcements #generativeai
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1 hour mini course for Chatgpt with Python
Exclusively Free for limited period
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https://www.udemy.com/course/chatgpt-api-python-masterclass-with-google-apps-integration/?couponCode=NEWDAY
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Google announced Gemini 2.0, a more advanced AI model capable of native image and audio output and tool use. This new model powers several projects, including Project Astra (a universal AI assistant) and Project Mariner (browser-based task completion). Gemini 2.0 Flash, an experimental version, is available to developers, with wider release planned. Google emphasizes responsible AI development, prioritizing safety and security in its applications. The announcement highlights Gemini 2.0's integration into Google products and its potential to revolutionize user experience
https://blog.google/technology/google-deepmind/google-gemini-ai-update-december-2024/
#gemini
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Join the FREE 5-Day 🤖 Gen AI Intensive Course with Google
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If you are a fresher important to realise how important data structures and algorithms are in your preparation to get into software journey. Here is a well documented roadmap that you could use with any programming language. If you are looking to expand into datascience then via python 🐍
#beginner #datastructure #python #fresher
Feel free to discuss on practising this in our exclusive python chats channel
https://t.me/joinchat/BNEH6lWLXAYvblVPWbb_ag
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https://www.kdnuggets.com/collection-of-free-courses-to-learn-data-science-data-engineering-machine-learning-mlops-and-llmops
#freecourses
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Curated list of Artificial Intelligence courses, books 📚,video lectures and papers 📝
https://github.com/owainlewis/awesome-artificial-intelligence
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#courses #ai
https://blog.google/technology/ai/google-gemini-ai/amp/
#artificialintelligence #gemini
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Python in Excel – making it possible to integrate Python and Excel analytics within the same Excel grid for uninterrupted workflow.
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#python #excel
Blog:
https://techcommunity.microsoft.com/t5/microsoft-365-blog/introducing-python-in-excel-the-best-of-both-worlds-for-data/ba-p/3905482
Video:
https://www.youtube.com/watch?v=H4XbvL8Mglc
Stanford online has released the course videos on "Natural Language Understanding" by Professor Christopher Potts.
The course covers topics including
⦿ Intro & evolution of NLU
⦿ Contextual word representations
⦿ Information retrieval
⦿ In-context learning
⦿ Behavioural evolution of NLU models
⦿ Analysis methods for NLU
⦿ NLP methods and metrics
⦿ Presenting your research
⦿ Language models and how to build them
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#stanford #nlp
Credit: Linkedin Sudalai Rajkumar
https://www.youtube.com/playlist?list=PLoROMvodv4rOwvldxftJTmoR3kRcWkJBp
MediSearch gives direct answers to medical questions based on trust worthy sources using llms
https://medisearch.io
#llm #machinelearning
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https://www.classcentral.com/report/openai-chatgpt-course/
#chatgpt
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