🧱 Large Language Models with Python
Learn how to build your own large language model, from scratch. This course goes into the data handling, math, and transformers behind large language models. You will use Python.
🔗 Course Link
Learn how to build your own large language model, from scratch. This course goes into the data handling, math, and transformers behind large language models. You will use Python.
🔗 Course Link
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Will LLMs always hallucinate?
As large language models (LLMs) become more powerful and pervasive, it's crucial that we understand their limitations.
A new paper argues that hallucinations - where the model generates false or nonsensical information - are not just occasional mistakes, but an inherent property of these systems.
While the idea of hallucinations as features isn't new, the researchers' explanation is.
They draw on computational theory and Gödel's incompleteness theorems to show that hallucinations are baked into the very structure of LLMs.
In essence, they argue that the process of training and using these models involves undecidable problems - meaning there will always be some inputs that cause the model to go off the rails.
This would have big implications. It suggests that no amount of architectural tweaks, data cleaning, or fact-checking can fully eliminate hallucinations.
So what does this mean in practice? For one, it highlights the importance of using LLMs carefully, with an understanding of their limitations.
It also suggests that research into making models more robust and understanding their failure modes is crucial.
No matter how impressive the results, LLMs are not oracles - they're tools with inherent flaws and biases
LLM & Generative AI Resources: https://t.iss.one/generativeai_gpt
As large language models (LLMs) become more powerful and pervasive, it's crucial that we understand their limitations.
A new paper argues that hallucinations - where the model generates false or nonsensical information - are not just occasional mistakes, but an inherent property of these systems.
While the idea of hallucinations as features isn't new, the researchers' explanation is.
They draw on computational theory and Gödel's incompleteness theorems to show that hallucinations are baked into the very structure of LLMs.
In essence, they argue that the process of training and using these models involves undecidable problems - meaning there will always be some inputs that cause the model to go off the rails.
This would have big implications. It suggests that no amount of architectural tweaks, data cleaning, or fact-checking can fully eliminate hallucinations.
So what does this mean in practice? For one, it highlights the importance of using LLMs carefully, with an understanding of their limitations.
It also suggests that research into making models more robust and understanding their failure modes is crucial.
No matter how impressive the results, LLMs are not oracles - they're tools with inherent flaws and biases
LLM & Generative AI Resources: https://t.iss.one/generativeai_gpt
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HandsOnLLM/Hands-On-Large-Language-Models
Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
Language:Jupyter Notebook
Total stars: 194
Stars trend:
#jupyternotebook
#artificialintelligence, #book, #largelanguagemodels, #llm, #llms, #oreilly, #oreillybooks
Official code repo for the O'Reilly Book - "Hands-On Large Language Models"
Language:Jupyter Notebook
Total stars: 194
Stars trend:
16 Sep 2024
5pm ▊ +6
6pm ▊ +6
7pm ▉ +7
8pm ▎ +2
9pm ▍ +3
10pm ▌ +4
11pm ▍ +3
17 Sep 2024
12am ▏ +1
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3am ██▎ +18
4am ██▏ +17#jupyternotebook
#artificialintelligence, #book, #largelanguagemodels, #llm, #llms, #oreilly, #oreillybooks
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OpenAI Mafia 🔥
Over 87 former employees have launched around 32 AI startups and OpenAI mafia just getting bigger and bigger!
Notable ventures include Andrej Karpathy's Eureka Labs & Ilya Sutskever's Safe Superintelligence Inc.. With founders like Dario Amodei of Anthropic and Tim Salimans of Aidence, these ex-OpenAI talents are revolutionizing the AI landscape.
Today Several former OpenAI employees have launched their own AI startups. Companies such as Anthropic, Pilot, and Perplexity, have collectively raised almost $10 billion. Many of these startups focus on AI safety, robotics, and AI applications in various industries.
OpenAI had approximately 2600 employees as of last month & who knows how many more AI startups would spin out of the company. It is fascinating to see new tech entrepreneurs being born out of the OpenAI ecosystem, which is acting as a training ground for future AI leaders.
Over 87 former employees have launched around 32 AI startups and OpenAI mafia just getting bigger and bigger!
Notable ventures include Andrej Karpathy's Eureka Labs & Ilya Sutskever's Safe Superintelligence Inc.. With founders like Dario Amodei of Anthropic and Tim Salimans of Aidence, these ex-OpenAI talents are revolutionizing the AI landscape.
Today Several former OpenAI employees have launched their own AI startups. Companies such as Anthropic, Pilot, and Perplexity, have collectively raised almost $10 billion. Many of these startups focus on AI safety, robotics, and AI applications in various industries.
OpenAI had approximately 2600 employees as of last month & who knows how many more AI startups would spin out of the company. It is fascinating to see new tech entrepreneurs being born out of the OpenAI ecosystem, which is acting as a training ground for future AI leaders.
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Stanford just uploaded their new "Building LLMS" lecture.
"This lecture provides a concise overview of building a ChatGPT-like model, covering both pretraining (language modeling) and post-training (SFT/RLHF).
For each component, it explores common practices in data collection, algorithms, and evaluation methods." https://www.youtube.com/watch?v=9vM4p9NN0Ts
"This lecture provides a concise overview of building a ChatGPT-like model, covering both pretraining (language modeling) and post-training (SFT/RLHF).
For each component, it explores common practices in data collection, algorithms, and evaluation methods." https://www.youtube.com/watch?v=9vM4p9NN0Ts
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Towards Natural Image Matting in the Wild via Real-Scenario Prior
Publication date: 9 Oct 2024
Topic: Semantic Segmentation
Paper: https://arxiv.org/pdf/2410.06593v1.pdf
GitHub: https://github.com/xiarho/semat
Description:
We propose SEMat which revamps the network architecture and training objectives. For network architecture, the proposed feature-aligned transformer learns to extract fine-grained edge and transparency features. The proposed matte-aligned decoder aims to segment matting-specific objects and convert coarse masks into high-precision mattes. For training objectives, the proposed regularization and trimap loss aim to retain the prior from the pre-trained model and push the matting logits extracted from the mask decoder to contain trimap-based semantic information. Extensive experiments across seven diverse datasets demonstrate the superior performance of our method, proving its efficacy in interactive natural image matting.
Publication date: 9 Oct 2024
Topic: Semantic Segmentation
Paper: https://arxiv.org/pdf/2410.06593v1.pdf
GitHub: https://github.com/xiarho/semat
Description:
We propose SEMat which revamps the network architecture and training objectives. For network architecture, the proposed feature-aligned transformer learns to extract fine-grained edge and transparency features. The proposed matte-aligned decoder aims to segment matting-specific objects and convert coarse masks into high-precision mattes. For training objectives, the proposed regularization and trimap loss aim to retain the prior from the pre-trained model and push the matting logits extracted from the mask decoder to contain trimap-based semantic information. Extensive experiments across seven diverse datasets demonstrate the superior performance of our method, proving its efficacy in interactive natural image matting.
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📊 Transform Your Sales Data into Insights with Claude’s New Tool!
Claude’s Analysis Tool can now break down your sales funnel data into clear, actionable insights and interactive visuals to boost your conversions.
Quick Guide:
1️⃣ Head to Claude AI, enable the Analysis Tool under Feature Preview in settings.
2️⃣ Upload your sales funnel CSV and ask: “Analyze this sales funnel data for conversion rates, drop-off points, and improvement areas.”
3️⃣ Visualize it: “Create an interactive funnel visualization showing user numbers, conversion rates, and key metrics at each stage.”
4️⃣ Get tailored recommendations: “Suggest the top 3 ways to optimize based on this analysis, with specific action steps.”
💡 Tip: Clean up your CSV data first to ensure the best results!
Claude’s Analysis Tool can now break down your sales funnel data into clear, actionable insights and interactive visuals to boost your conversions.
Quick Guide:
1️⃣ Head to Claude AI, enable the Analysis Tool under Feature Preview in settings.
2️⃣ Upload your sales funnel CSV and ask: “Analyze this sales funnel data for conversion rates, drop-off points, and improvement areas.”
3️⃣ Visualize it: “Create an interactive funnel visualization showing user numbers, conversion rates, and key metrics at each stage.”
4️⃣ Get tailored recommendations: “Suggest the top 3 ways to optimize based on this analysis, with specific action steps.”
💡 Tip: Clean up your CSV data first to ensure the best results!
👍5
Generative AI isn't easy!
It’s the groundbreaking technology that creates new content—whether it’s images, text, music, or even entire virtual worlds.
To truly master Generative AI, focus on these key areas:
0. Understanding the Basics: Learn the foundational concepts of generative models, including GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and diffusion models.
1. Mastering Neural Networks: Dive deep into the types of neural networks used in generative AI, such as convolutional neural networks (CNNs) for image generation and transformer models for text.
2. Exploring Text Generation Models: Understand the mechanics behind language models like GPT and BERT, and how they generate human-like text.
3. Creating Images with AI: Learn how models like DALL-E and Stable Diffusion generate realistic images from textual prompts.
4. Working with Audio and Music Generation: Explore models like Jukedeck and OpenAI’s MuseNet to create music and sound using AI.
5. Building Custom AI Models: Get hands-on experience with frameworks like TensorFlow, PyTorch, and Hugging Face to train your own generative models.
6. Fine-Tuning Pre-Trained Models: Learn how to adapt large pre-trained models to specific tasks by fine-tuning them with domain-specific data.
7. Ethics and Bias in Generative AI: Understand the ethical implications of creating content using AI, including issues of plagiarism, bias, and misinformation.
8. Evaluating and Enhancing Generated Content: Learn how to assess the quality of generated content and fine-tune models to improve their results.
9. Staying Updated with Cutting-Edge Developments: Generative AI is rapidly evolving—keep up with new advancements, techniques, and applications in the field.
Generative AI is a creative force that blends technology with imagination.
💡 Embrace the challenge of creating innovative, AI-powered content that can transform industries and art.
⏳ With practice, patience, and creativity, you’ll unlock the potential of generative AI to create something truly unique!
#genai
It’s the groundbreaking technology that creates new content—whether it’s images, text, music, or even entire virtual worlds.
To truly master Generative AI, focus on these key areas:
0. Understanding the Basics: Learn the foundational concepts of generative models, including GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), and diffusion models.
1. Mastering Neural Networks: Dive deep into the types of neural networks used in generative AI, such as convolutional neural networks (CNNs) for image generation and transformer models for text.
2. Exploring Text Generation Models: Understand the mechanics behind language models like GPT and BERT, and how they generate human-like text.
3. Creating Images with AI: Learn how models like DALL-E and Stable Diffusion generate realistic images from textual prompts.
4. Working with Audio and Music Generation: Explore models like Jukedeck and OpenAI’s MuseNet to create music and sound using AI.
5. Building Custom AI Models: Get hands-on experience with frameworks like TensorFlow, PyTorch, and Hugging Face to train your own generative models.
6. Fine-Tuning Pre-Trained Models: Learn how to adapt large pre-trained models to specific tasks by fine-tuning them with domain-specific data.
7. Ethics and Bias in Generative AI: Understand the ethical implications of creating content using AI, including issues of plagiarism, bias, and misinformation.
8. Evaluating and Enhancing Generated Content: Learn how to assess the quality of generated content and fine-tune models to improve their results.
9. Staying Updated with Cutting-Edge Developments: Generative AI is rapidly evolving—keep up with new advancements, techniques, and applications in the field.
Generative AI is a creative force that blends technology with imagination.
💡 Embrace the challenge of creating innovative, AI-powered content that can transform industries and art.
⏳ With practice, patience, and creativity, you’ll unlock the potential of generative AI to create something truly unique!
#genai
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Here are some project ideas for a data science and machine learning project focused on generating AI:
1. Natural Language Generation (NLG) Model: Build a model that generates human-like text based on input data. This could be used for creating product descriptions, news articles, or personalized recommendations.
2. Code Generation Model: Develop a model that generates code snippets based on a given task or problem statement. This could help automate software development tasks or assist programmers in writing code more efficiently.
3. Image Captioning Model: Create a model that generates captions for images, describing the content of the image in natural language. This could be useful for visually impaired individuals or for enhancing image search capabilities.
4. Music Generation Model: Build a model that generates music compositions based on input data, such as existing songs or musical patterns. This could be used for creating background music for videos or games.
5. Video Synthesis Model: Develop a model that generates realistic video sequences based on input data, such as a series of images or a textual description. This could be used for generating synthetic training data for computer vision models.
6. Chatbot Generation Model: Create a model that generates conversational agents or chatbots based on input data, such as dialogue datasets or user interactions. This could be used for customer service automation or virtual assistants.
7. Art Generation Model: Build a model that generates artistic images or paintings based on input data, such as art styles, color palettes, or themes. This could be used for creating unique digital artwork or personalized designs.
8. Story Generation Model: Develop a model that generates fictional stories or narratives based on input data, such as plot outlines, character descriptions, or genre preferences. This could be used for creative writing prompts or interactive storytelling applications.
9. Recipe Generation Model: Create a model that generates new recipes based on input data, such as ingredient lists, dietary restrictions, or cuisine preferences. This could be used for meal planning or culinary inspiration.
10. Financial Report Generation Model: Build a model that generates financial reports or summaries based on input data, such as company financial statements, market trends, or investment portfolios. This could be used for automated financial analysis or decision-making support.
Any project which sounds interesting to you?
1. Natural Language Generation (NLG) Model: Build a model that generates human-like text based on input data. This could be used for creating product descriptions, news articles, or personalized recommendations.
2. Code Generation Model: Develop a model that generates code snippets based on a given task or problem statement. This could help automate software development tasks or assist programmers in writing code more efficiently.
3. Image Captioning Model: Create a model that generates captions for images, describing the content of the image in natural language. This could be useful for visually impaired individuals or for enhancing image search capabilities.
4. Music Generation Model: Build a model that generates music compositions based on input data, such as existing songs or musical patterns. This could be used for creating background music for videos or games.
5. Video Synthesis Model: Develop a model that generates realistic video sequences based on input data, such as a series of images or a textual description. This could be used for generating synthetic training data for computer vision models.
6. Chatbot Generation Model: Create a model that generates conversational agents or chatbots based on input data, such as dialogue datasets or user interactions. This could be used for customer service automation or virtual assistants.
7. Art Generation Model: Build a model that generates artistic images or paintings based on input data, such as art styles, color palettes, or themes. This could be used for creating unique digital artwork or personalized designs.
8. Story Generation Model: Develop a model that generates fictional stories or narratives based on input data, such as plot outlines, character descriptions, or genre preferences. This could be used for creative writing prompts or interactive storytelling applications.
9. Recipe Generation Model: Create a model that generates new recipes based on input data, such as ingredient lists, dietary restrictions, or cuisine preferences. This could be used for meal planning or culinary inspiration.
10. Financial Report Generation Model: Build a model that generates financial reports or summaries based on input data, such as company financial statements, market trends, or investment portfolios. This could be used for automated financial analysis or decision-making support.
Any project which sounds interesting to you?
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📖 Beginner's Guide to Neural Networks!
Ever wondered how AI really works? 🤔 Check out this beginner-friendly lecture: Large Language Models Explained in Simple Terms.
Timestamps:
0:00 - Who this is for
0:41 - What are large language models?
7:48 - Where to learn more
In just 9 minutes, the author explains the basics of AI in a way anyone can understand—covering the attention mechanism, transformers, and other key concepts behind LLMs.
➡️ Watch the full video here: https://www.youtube.com/watch?v=LPZh9BOjkQs
Ever wondered how AI really works? 🤔 Check out this beginner-friendly lecture: Large Language Models Explained in Simple Terms.
Timestamps:
0:00 - Who this is for
0:41 - What are large language models?
7:48 - Where to learn more
In just 9 minutes, the author explains the basics of AI in a way anyone can understand—covering the attention mechanism, transformers, and other key concepts behind LLMs.
➡️ Watch the full video here: https://www.youtube.com/watch?v=LPZh9BOjkQs
1 in 5 Americans have flirted with AI chatbots.
Many see these AI companions as more than just tools for tasks, they're becoming seen as virtual friends and even partners in some cases. A key attraction is the ability to customize the AI partner, ensuring they match personal preferences.
Some are also drawn by the idea of trust and loyalty AI can offer, as well as the novelty of exploring relationships without typical human relationship problems.
This market is expected to grow significantly, with some experts predicting it could become a billion-dollar industry in the near future.
Many see these AI companions as more than just tools for tasks, they're becoming seen as virtual friends and even partners in some cases. A key attraction is the ability to customize the AI partner, ensuring they match personal preferences.
Some are also drawn by the idea of trust and loyalty AI can offer, as well as the novelty of exploring relationships without typical human relationship problems.
This market is expected to grow significantly, with some experts predicting it could become a billion-dollar industry in the near future.
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5 Free Courses for Mastering LLMs
1. Introduction to Large Language Models by Google :-
Course Link
2. AI for Educators by Microsoft:- Course Link
3. Cohere’s LLM University:-
Course Link
4. Anthropic Prompt Engineering Courses:-
Course Link
5. Large Language Model Agents:- Course Link
1. Introduction to Large Language Models by Google :-
Course Link
2. AI for Educators by Microsoft:- Course Link
3. Cohere’s LLM University:-
Course Link
4. Anthropic Prompt Engineering Courses:-
Course Link
5. Large Language Model Agents:- Course Link
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Hiring for Machine Learning Engineer - Generative AI
https://jobs.apple.com/en-us/details/200582801/machine-learning-engineer-generative-ai?team=MLAI
https://jobs.apple.com/en-us/details/200582801/machine-learning-engineer-generative-ai?team=MLAI