Udemy - Recursive Language Models and Advanced LLM Architectures torrent

Recursive Language Models and Advanced LLM Architectures
https://WebToolTip.com
Published 10/2026

MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch

Language: English | Duration: 11h 12m | Size: 2.74 GB
Build and measure RLMs, looped transformers, efficient attention, Mamba, hybrids, MoE, memory and RAG on a CPU
What you'll learn

Explain the three meanings of recursive in deep learning: tree networks, looped depth and Recursive Language Models

Build a small decoder-only transformer from scratch and use it as a measured baseline for every other architecture

Implement recursive networks, Tree-LSTM and a weight-tied looped transformer, and compare them at equal parameters

Build a Recursive Language Model loop by hand with a Python REPL and a local model, and read every step of its trace

Add sliding-window and linear attention to the baseline and measure memory, speed and quality against sequence length

Implement a selective state space layer, train a tiny Mamba-style model and compare pretrained Mamba with Pythia on CPU

Build mixture-of-experts layers, watch expert load with and without balancing, and inspect a real MoE router

Measure the KV cache, manage it with the transformers cache classes and keep generation stable with attention sinks

Build and evaluate a production-style RAG pipeline with chunking, FAISS, hybrid search, re-ranking and faithfulness checks

Run fair architecture comparisons and write an architecture selection report backed by a complete results table
Requirements

Working Python and basic PyTorch: you can read a training loop and run a script from the command line

A computer with a multi-core CPU. No GPU is needed: the course bench is an 8-CPU machine and every lab is measured on it