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

(1:10:55)

RMS Norm : The Detailed Explanation

(4:23)

Mistral Spelled Out : RMS Norm : Part 5

(9:9)

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

(3:4:11)

LLAMA vs Transformers: Exploring the Key Architectural Differences (RMS Norm, GQA, ROPE, KV Cache)

(12:59)

Geometry and Dynamics of LayerNorm

(20:12)

The Rise of the Ocean Liner | Evolution of Ocean Liners Documentary Part 1

(31:13)

10 Tips For Louder Masters (Without Distortion)

(43:13)

LlaMA 3 Architecture \u0026 Paper explained step by step

(13:50)

Oscilloscope: DC \u0026 AC, Signal Tracing in Amp Circuit, RMS Output Measurement

(27:17)

The Canceled 'Super Titanic' - RMMV Oceanic

(23:49)

Mixing loud: Managing your dynamic range using limiters

(10:20)

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

(1:26:21)

All About Normalizations! - Batch, Layer, Instance and Group Norm

(41:56)

L20B: Signal Measures

(9:29)

Re-Introducing LayerNorm: Geometric Meaning, Irreversibility and a Comparative Study with RMSNorm

(12:41)

Metric 1 RMS error

(1:33)

RMSProp (C2W2L07)

(7:42)

Why are Ships Called 'SS'?

(11:39)

Peak vs RMS volume: why should you care?

(6:8)

RMS and Overall Level

(2:11)

Standardization vs Normalization Clearly Explained!

(5:48)

Episode 60: The DeepSeek LLM Blueprint: Inside the LLM that is shaping AI's next chapter

(36:49)

How True RMS and Averaging multi-meters work: Part 2 - Wave form analyser

(19:49)

RMS- vs Vector-Averaging in Action

(2:50)