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#Model Based Deep Learning Reel by @infusewithai - In most deep learning models, an input is transformed by passing it through a series of large matrices filled with tunable parameters. Each matrix re
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@infusewithai
In most deep learning models, an input is transformed by passing it through a series of large matrices filled with tunable parameters. Each matrix represents a layer, and when your input reaches it, the model performs a bunch of math operations - mainly matrix multiplication, addition, and a nonlinear activation. In reality, deep learning models are simply layers and layers of math transformations and matrix multiplications applied to an input vector. Each layer reshapes the information slightly, highlighting some features and reducing others. During training, the model adjusts the numbers inside these matrices so the transformations produce better and better outputs. Through this mathematical process, deep learning models can gradually turn raw input (like text, images, or audio) into meaningful predictions or representations. C: 3blue1brown #machinelearning #deeplearning #statistics #computerscience #coding #mathematics #math #physics #science #education
#Model Based Deep Learning Reel by @aibutsimple - In most deep learning models, an input is transformed by passing it through a series of large matrices filled with tunable parameters. Each matrix re
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@aibutsimple
In most deep learning models, an input is transformed by passing it through a series of large matrices filled with tunable parameters. Each matrix represents a layer, and when your input reaches it, the model performs a bunch of math operations—mainly matrix multiplication, addition, and a nonlinear activation. In reality, deep learning models are simply layers and layers of math transformations and matrix multiplications applied to an input vector. Each layer reshapes the information slightly, highlighting some features and reducing others. During training, the model adjusts the numbers inside these matrices so the transformations produce better and better outputs. Through this mathematical process, deep learning models can gradually turn raw input (like text, images, or audio) into meaningful predictions or representations. Want to learn ML/AI? Accelerate your learning in our Weekly AI Newsletter—educational, easy to understand, mathematically explained, and completely free (link in bio 🔗). C: 3blue1brown Join our AI community for more posts like this @aibutsimple 🤖 #machinelearning #deeplearning #statistics #computerscience #coding #mathematics #math #physics #science #education
#Model Based Deep Learning Reel by @priyal.py - Chatbot for FAQs Fine-tune a pretrained LLM to answer domain-specific questions (e.g., product FAQs). Tech Stack: Python, HuggingFace Transformers, Py
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@priyal.py
Chatbot for FAQs Fine-tune a pretrained LLM to answer domain-specific questions (e.g., product FAQs). Tech Stack: Python, HuggingFace Transformers, PyTorch, Datasets LegalDoc Assistant Fine-tune GPT/LLaMA on legal text to summarize contracts or answer legal queries. Tech Stack: HuggingFace, PyTorch, LangChain, PDF parsing libraries Code Completion Model Fine-tune CodeLlama or CodeT5 on a repo of code for auto-completion and suggestions. Tech Stack: HuggingFace, PyTorch, Tokenizers, GitHub API Emotion-Aware Chatbot Fine-tune an LLM to recognize emotions in messages and respond empathetically. Tech Stack: PyTorch, HuggingFace, GoEmotions Dataset, PEFT (LoRA/Adapters) Summarization Model Fine-tune BART or T5 to summarize articles, meeting notes, or emails. Tech Stack: HuggingFace, PyTorch Lightning, Datasets Customer Review Analyzer Fine-tune a small LLM on product reviews to generate insights, sentiment, or suggestions. Tech Stack: Transformers, PyTorch, Pandas, Sklearn Domain-Specific RAG Model Fine-tune an LLM to retrieve and answer questions from your company’s knowledge base. Tech Stack: LangChain, ChromaDB/FAISS, HuggingFace, PyTorch TinyGPT for Chat Fine-tune a small GPT model on your own chat logs for personal assistants. Tech Stack: PyTorch, HuggingFace, Tokenizers, WandB #datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency #ai #llm #largelanguagemodels
#Model Based Deep Learning Reel by @mar_antaya (verified account) - Making building your own ML model a little less intimidating if it's your first time :) #ai #machinelearning
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@mar_antaya
Making building your own ML model a little less intimidating if it’s your first time :) #ai #machinelearning
#Model Based Deep Learning Reel by @awomanindatascience - It's Day 14 of building a LLM from scratch ✨ Most people think LLMs are complex because of code. They're complex because of configuration and scale.
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@awomanindatascience
It’s Day 14 of building a LLM from scratch ✨ Most people think LLMs are complex because of code. They’re complex because of configuration and scale. Today I broke down the GPT-2 config that defines how the model thinks, remembers, and attends. GPT-2 is just a set of numbers that define scale: vocab size, context length, embedding dimension, layers, and attention heads. Breaking down the GPT-2 (124M) configuration: 50,257-token vocabulary, 1,024-token context, 768-dimensional embeddings, 12 transformer layers with 12 attention heads, dropout 0.1, and bias-free QKV projections. Understanding these parameters is key to scaling LLMs efficiently. #deeplearning #generativeai #womenwhocode #largelanguagemodels
#Model Based Deep Learning Reel by @pallavibhimte_ - Day 1 of learning LLMs! 🚀 Transformers changed everything - they read all words at once, understanding how each one connects to the others. That's ho
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@pallavibhimte_
Day 1 of learning LLMs! 🚀 Transformers changed everything — they read all words at once, understanding how each one connects to the others. That’s how AI like ChatGPT understands context like humans do. 🧠 Next up → Attention Mechanism 👀 🔗Check the link in bio for the Complete LLM Roadmap! Follow @pallavibhimte_ for more! [LLM, Transformer Architecture, ChatGPT, AI Explained, Machine Learning, Deep Learning, NLP, Artificial Intelligence, Neural Networks, Attention Mechanism, Tech Education, AI Simplified, Learn AI, Data Science, AI for Beginners, AI Series, Transformers, Context Understanding]
#Model Based Deep Learning Reel by @wdf_ai - Building your own ChatGPT-like model at small scale is more achievable than you think. In Large Language Model lots of dataset and compute is requir
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@wdf_ai
Building your own ChatGPT-like model at small scale is more achievable than you think. In Large Language Model lots of dataset and compute is required but the core structure of transformers remains same. 3 free resources that actually work — LLM basics, build from scratch, full training pipeline with fine-tuning. Comment “LLM” and I’ll DM you all the links. #llm #gpt #machinelearning #deeplearning #aiforbeginners
#Model Based Deep Learning Reel by @mar_antaya (verified account) - Deep learning 😋 some ideas if you want to learn more on your journey for any stage in your career. Lots of people think that these are just for colle
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@mar_antaya
Deep learning 😋 some ideas if you want to learn more on your journey for any stage in your career. Lots of people think that these are just for college students looking for a job but that is not the case. I even like to do projects to learn something new, a new field, topic or experiment with libraries or models I haven’t dealt with. #coding #codingprojects #swe #developer #chatgpt #deeplearning #artificialintelligence #ml #productmanager
#Model Based Deep Learning Reel by @aravind_ai_verse (verified account) - Basics of Deep Learning 😮‍💨 . . • Instagram Algorithm • How Instagram Works • Deep Learning Instagram • Deep Learning
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@aravind_ai_verse
Basics of Deep Learning 😮‍💨 . . • Instagram Algorithm • How Instagram Works • Deep Learning Instagram • Deep Learning
#Model Based Deep Learning Reel by @techwithnt (verified account) - Pre-training uses self-supervised learning across massive datasets (text, code, web, etc.) to predict the next word. Fine-tuning takes that base mode
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@techwithnt
Pre-training uses self-supervised learning across massive datasets (text, code, web, etc.) to predict the next word. Fine-tuning takes that base model and updates its weights using labeled examples for specific tasks (e.g., summarization, medical Q&A, code generation). So, to conclude it, Pre-training is reading every book in the library. Fine-tuning is taking one specific course to master just tax law. ✨💻 . 🏷️ Day 12, 50 Day Challenge, Generative Al, Artificial Intelligence, Al, Large Language Models, OpenAl, Al Evolution, Important Concepts, Series, Al Series
#Model Based Deep Learning Reel by @aibutsimple - Large Language Models (LLMs) such as ChatGPT are based on neural networks called transformers, an architecture built using multiple attention mechanis
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@aibutsimple
Large Language Models (LLMs) such as ChatGPT are based on neural networks called transformers, an architecture built using multiple attention mechanisms and multilayer perceptrons (MLPs). These models process input text by learning context through self-attention mechanisms, which weighs the importance of each pair of words. This way, long sequences are no longer an issue. This contextual understanding is passed through MLPs, which learn the representations and patterns of the sequence. To generate text, the model generates a probability distribution of the next word; we choose the highest-probability word and keep predicting the next word, iterating to create a sentence or paragraph. C: 3blue1brown Join our AI community for more posts like this @aibutsimple 🤖 #neuralnetwork #llm #gpt #artificialintelligence #machinelearning #3blue1brown #deeplearning #neuralnetworks #datascience #python #ml #pythonprogramming #datascientist
#Model Based Deep Learning Reel by @harpercarrollai (verified account) - "Reasoning" models are causing a major buzz in the AI / LLM (Large Language Model) space. These models are designed to enhance complex problem-solving
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@harpercarrollai
“Reasoning” models are causing a major buzz in the AI / LLM (Large Language Model) space. These models are designed to enhance complex problem-solving by explicitly “thinking” through steps, often showing their “work” and improving accuracy on tasks like math, coding, and logic. While “thinking” tokens don’t solve the problem of interpretability, it might give some useful insights about the structure of the model’s reasoning.  As @aiprof says, “the model still remains a black box… much like our own minds. 😊”  Is this clear? Share your questions in the comments!  Alongside reasoning models, you may have heard of chain-of-thought (CoT) reasoning. It’s not required for reasoning models but involves generating intermediate steps—think mental breadcrumbs—before the final answer. All the models above use CoT or a CoT-like approach for their intermediary tokens. Could they do more? Possibly—o1 or o3-mini might juggle parallel ideas internally, but we see CoT in the output. As of March 17, 2025, no evidence shows them natively using non-CoT reasoning (e.g., parallel or intuitive leaps). Want a CoT video? Let me know! — Hi, I’m Harper, AI educator, engineer and advisor. ••• My goal is to democratize AI, so everyone can understand it, use it, and make informed decisions about it. Happy you’re here. ••• I have two degrees in Computer Science specializing in Artificial Intelligence from @Stanford, experience teaching PhD-level AI courses there, and 4+ years building AI at @Facebook I was Founding Engineer and then Head of AI/ML at a startup that was acquired by @NVIDIA, and that’s where I began my AI teaching journey. Follow me if you want to learn about AI, and let me know in the comments if there’s anything specific you want to learn about.  #aieducator #airesearch #aiexpert #aiforall #aiforgood #techtalks #amazingtechnology #alwaysbelearning #moderntechnology #techvideos #techindustry #techfacts #techtalk #techinnovation #futureready #scienceandtechnology #techupdates #technologytrends #futuretechnology #techgeek #techtrends #techlover #technologynews #artificialintelligence #ai #airevolution #reasoningmodel #deepseek #openai #chatgpt

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