Ful LLM / Deep Learning roadmap.

2026-02-19 · Source: Machine Learning on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, short

Summary

This roadmap outlines key concepts in Large Language Models (LLMs) and Deep Learning, structured into eight parts covering foundational to advanced topics. It begins with Attention and Transformer concepts, detailing Bidirectional Attention, Causal Masking, Absolute Attention (Positional Encoding), and the Query-Key-Value mechanism, complete with practical examples and conceptual Python code. The roadmap then progresses to Neural Network core concepts like pre/post-layer neurons, weights, backpropagation, gradient descent problems (vanishing gradients), and loss functions. Activation functions such as ReLU, Sigmoid, GELU, SiLU, and SwiGLU are explained. LLM-specific concepts like scaling, repetition penalty, nucleus sampling, tokenization, and auto-regressive models are covered, alongside data pipeline steps including ingestion, filtering, and sanitation. Finally, it addresses embeddings (dense, sparse, TF-IDF), retrieval, hybrid ranking, and a multi-stage pipeline for creating and deploying new models.

Key takeaway

For AI Engineers building or optimizing LLMs, understanding the interplay between attention mechanisms, positional encoding, and activation functions is crucial. Your choice of attention type (bidirectional vs. causal) dictates model capabilities, while managing gradient issues with techniques like ReLU or residual connections is vital for deep network stability. When developing, consider the full pipeline from data ingestion and tokenization to deployment and monitoring to ensure robust and performant models.

Key insights

Transformers leverage attention mechanisms and positional encodings to process sequences bidirectionally or causally.

Principles

• Scaling LLMs with more data, parameters, and compute improves performance.
• Gradient issues in deep networks are mitigated by ReLU, residual connections, and LayerNorm.

Method

The model-building pipeline involves data collection, cleaning, tokenization, training, evaluation, deployment, and monitoring, with specific steps for filtering and deduplication.

In practice

• Use `torch.softmax(scores, dim=-1)` for attention calculation.
• Apply `torch.tril(torch.ones(seq_len, seq_len))` for causal masking.
• Implement `F.cross_entropy(logits, labels)` for language model loss.

Topics

• Attention Mechanisms
• Transformer Architecture
• Neural Network Fundamentals
• Large Language Models
• Embeddings and Retrieval

Best for: AI Engineer, Machine Learning Engineer, AI Student

Related on AIssential

• Inside the Transformer: Attention Mechanisms Deep Dive — Transformers achieve deep learning stability and expressive power through specific architectural choices like residual connections and layer normalization.
• Build an LLM from Scratch 3: Coding attention mechanisms — Self-attention mechanisms enable LLMs to selectively process input sequences, crucial for generating contextually relevant outputs.
• The Big LLM Architecture Comparison — Modern LLM architectures prioritize inference efficiency and training stability through diverse attention mechanisms and Mixture of Experts.
• Building an LLM from Scratch: The Foundational Layer and Structural Stability — LLM foundational layers convert text to contextualized numerical representations via embeddings, attention, and normalization.
• LLMs (Part-01): The High-level Architecture of Transformers — The Transformer architecture, detailed in "Attention is All You Need," forms the basis for modern LLMs through its encoder-decoder structure and attention mechanisms.
• A new way to increase the capabilities of large language models — PaTH Attention improves LLM sequential reasoning by making positional encoding adaptive and context-aware, outperforming static methods.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning on Medium.