Scanned by

    Norton

    Norton™ Safe Web

    Reinforcement Learning from Human Feedback explained with math derivations and the PyTorch code.

    Reinforcement Learning from Human Feedback explained with math derivations and the PyTorch code.

    02:15:13 |
    Download Here

    You Might Also Like:

    Coding a Multimodal (Vision) Language Model from scratch in PyTorch with full explanation

    Coding a Multimodal (Vision) Language Model from scratch in PyTorch with full explanation

    5:46:05 |
    Coding a Transformer from scratch on PyTorch, with full explanation, training and inference.

    Coding a Transformer from scratch on PyTorch, with full explanation, training and inference.

    2:59:24 |
    Attention is all you need (Transformer) - Model explanation (including math), Inference and Training

    Attention is all you need (Transformer) - Model explanation (including math), Inference and Training

    58:04 |
    LLaMA explained: KV-Cache, Rotary Positional Embedding, RMS Norm, Grouped Query Attention, SwiGLU

    LLaMA explained: KV-Cache, Rotary Positional Embedding, RMS Norm, Grouped Query Attention, SwiGLU

    1:10:55 |
    Coding Stable Diffusion from scratch in PyTorch

    Coding Stable Diffusion from scratch in PyTorch

    5:03:32 |
    Retrieval Augmented Generation (RAG) Explained: Embedding, Sentence BERT, Vector Database (HNSW)

    Retrieval Augmented Generation (RAG) Explained: Embedding, Sentence BERT, Vector Database (HNSW)

    49:24 |
    Direct Preference Optimization (DPO) explained: Bradley-Terry model, log probabilities, math

    Direct Preference Optimization (DPO) explained: Bradley-Terry model, log probabilities, math

    48:46 |
    Kolmogorov-Arnold Networks: MLP vs KAN, Math, B-Splines, Universal Approximation Theorem

    Kolmogorov-Arnold Networks: MLP vs KAN, Math, B-Splines, Universal Approximation Theorem

    1:15:39 |
    The reign of Hazrat Umar bin Khattab______molana_tariq_jameel

    The reign of Hazrat Umar bin Khattab______molana_tariq_jameel

    26:24 |
    Distributed Training with PyTorch: complete tutorial with cloud infrastructure and code

    Distributed Training with PyTorch: complete tutorial with cloud infrastructure and code

    1:12:53 |
    Mistral / Mixtral Explained: Sliding Window Attention, Sparse Mixture of Experts, Rolling Buffer

    Mistral / Mixtral Explained: Sliding Window Attention, Sparse Mixture of Experts, Rolling Buffer

    1:26:21 |
    Quantization explained with PyTorch - Post-Training Quantization, Quantization-Aware Training

    Quantization explained with PyTorch - Post-Training Quantization, Quantization-Aware Training

    50:55 |
    Reinforcement Learning from Human Feedback explained with math derivations and the PyTorch code.

    Reinforcement Learning from Human Feedback explained with math derivations and the PyTorch code.

    2:15:13 |
    Coding LLaMA 2 from scratch in PyTorch - KV Cache, Grouped Query Attention, Rotary PE, RMSNorm

    Coding LLaMA 2 from scratch in PyTorch - KV Cache, Grouped Query Attention, Rotary PE, RMSNorm

    3:04:11 |
    ML Interpretability: feature visualization, adversarial example, interp. for language models

    ML Interpretability: feature visualization, adversarial example, interp. for language models

    1:00:15 |
    BERT explained: Training, Inference, BERT vs GPT/LLamA, Fine tuning, [CLS] token

    BERT explained: Training, Inference, BERT vs GPT/LLamA, Fine tuning, [CLS] token

    54:52 |
    LIUCC A V Pak America | Long Island United T20 Cup

    LIUCC A V Pak America | Long Island United T20 Cup

    1:09:14 |
    Mamba and S4 Explained: Architecture, Parallel Scan, Kernel Fusion, Recurrent, Convolution, Math

    Mamba and S4 Explained: Architecture, Parallel Scan, Kernel Fusion, Recurrent, Convolution, Math

    1:14:29 |
    LIUCC A V New Star : Long Island United T20 cup

    LIUCC A V New Star : Long Island United T20 cup

    4:18:36 |
    Umar Jamil is live!

    Umar Jamil is live!

    32:24 |