Udemy - Recursive Language Models and Advanced LLM Architectures [TorrentX]

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