#Deep Learning Models

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#Deep Learning Models 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
#Deep Learning Models 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
#Deep Learning Models 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
#Deep Learning Models Reel by @aibutsimple - If you want to learn AI in 2026, here's where to start: First, build a strong foundation in machine learning before moving into deep learning. Begin
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@aibutsimple
If you want to learn AI in 2026, here's where to start: First, build a strong foundation in machine learning before moving into deep learning. Begin with supervised methods like linear and logistic regression to understand optimization and decision boundaries, then explore KNN, Naive Bayes, decision trees, random forests, gradient boosting, and SVMs to see different modeling assumptions and performance trade-offs. Next, study unsupervised techniques such as k-means and hierarchical clustering, Gaussian mixture models, and dimensionality reduction methods like PCA, t-SNE, and UMAP to learn how structure can be discovered without labels. With this in mind, transition to deep learning by learning neural networks and autoencoders, then more specialized architectures like CNNs for vision, RNNs for sequences, transformers and LLMs for language, and diffusion models for generative tasks. This progression builds intuition step by step, from classical algorithms to modern AI systems. If you want to commit to learning AI, Join 7000+ Others in our Visually Explained AI Newsletter. It's easy to understand, with math included—it's also completely free. The link is in our bio 🔗. Join our AI community for more posts like this @aibutsimple 🤖 #machinelearning #deeplearning #statistics #computerscience #coding #mathematics #math #physics #science #education
#Deep Learning Models Reel by @khushigrewall (verified account) - Link in Highlights 🫶🏻 Want to actually code machine learning instead of just understanding theory? Today's video is about TensorFlow: one of the c
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@khushigrewall
Link in Highlights 🫶🏻 Want to actually code machine learning instead of just understanding theory? Today’s video is about TensorFlow: one of the core libraries used to build and train ML models. From defining tensors to solving real ML problems, this is where concepts turn into code. If you’re serious about learning AI, start practicing. #machinelearning #datascience #artificialintelligence #tensorflow #deeplearning mlcoding pythonforai aieducation aireels techreels learnml buildinpublic techcreators reelsindia indiantech futureofai viralreels explorepage
#Deep Learning Models Reel by @aitoolhub.co (verified account) - AI is more than LLM's (large language models) 1️⃣ LLMs - Large Language Models 🧠 Token-by-token text processing for creative writing, coding, and de
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@aitoolhub.co
AI is more than LLM’s (large language models) 1️⃣ LLMs – Large Language Models 🧠 Token-by-token text processing for creative writing, coding, and deep reasoning. 2️⃣ LCMs – Large Concept Models 🌀 Meta’s approach: encode whole sentences as “concepts” in SONAR space, going beyond word-level. 3️⃣ VLMs – Vision-Language Models 🖼 Fuse images and text for visual understanding and captioning the core of multimodal AI. 4️⃣ SLMs – Small Language Models⚡️ Designed for edge devices. Compact, fast, and energy-efficient. 5️⃣ MoE Mixture of Experts 🧩 Activate only relevant subnetworks per query high efficiency, no quality loss. 6️⃣ MLMs – Masked Language Models 📚 The original bidirectional models understand context by seeing both sides of a sentence. 7️⃣ LAMs – Large Action Models 🔧 From understanding to action execute complex system-level operations. 8️⃣ SAMs – Segment Anything Models 🎯 Visual segmentation with pixel-level accuracy. Universal, foundational, powerful. Follow @aitoolhub.co for more Vid by LinkedIn / Francesco Massa #llm #ml #ai
#Deep Learning Models 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
#Deep Learning Models Reel by @workiniterations - 4 ML papers to read this weekend instead of touching grass. Attention Is All You Need Yes, everyone talks about this one, but that's because it compl
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@workiniterations
4 ML papers to read this weekend instead of touching grass. Attention Is All You Need Yes, everyone talks about this one, but that’s because it completely changed the direction of AI. This paper introduced the Transformer architecture, which is the foundation behind most modern language models. Deep Residual Learning for Image Recognition A deceptively simple idea: skip connections. This paper made it possible to train very deep neural networks and ended up shaping how many modern architectures are built. Understanding Deep Learning Requires Rethinking Generalization This one is fascinating because it challenges a core assumption in machine learning. Neural networks can memorize random data perfectly, which raises big questions about why deep learning actually works. The Lottery Ticket Hypothesis A really intriguing idea: large neural networks might contain much smaller winning subnetworks that can train just as well as the full model. It changes how we think about efficiency and model size. Together these give a nice balance of architecture, theory, and insight into how deep learning actually works. Save this if you’re learning ML.
#Deep Learning Models 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
#Deep Learning Models Reel by @blurred_ai (verified account) - Confused on how LLMs work, then definitely read this book✨ ✅ Comment "Book" for Link + Repo in DM! [Large language models, LLM, agentic AI, transfor
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@blurred_ai
Confused on how LLMs work, then definitely read this book✨ ✅ Comment “Book” for Link + Repo in DM! [Large language models, LLM, agentic AI, transformer architecture, Deep learning, nlp, best book to read]
#Deep Learning Models Reel by @pikacodes (verified account) - 2025 machine learning roadmap - it's time to start prepping for AI's takeover 💡🤖 resources mentioned: VIDEO: Full Applied AI Lectures by Cassie Ko
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@pikacodes
2025 machine learning roadmap - it’s time to start prepping for AI’s takeover 💡🤖 resources mentioned: VIDEO: Full Applied AI Lectures by Cassie Kozyrkov Neural Networks: Zero to Hero by Andrej Karpathy Machine Learning Specialization by Andrew Ng BOOKS: An Introduction to Statistical Learning Mathematics for Machine Learninf Artificial Intelligence: A Modern Approach FOR PRACTICE: Machine Learning with PyTorch and Scikit-Learn AIML.com . . #machinelearning #ai #resources #tech #programming #womenintech #coder #programacao #latinasintech #swe

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