Large Language Model

📖 Executive Summary – Large Language Models (LLMs)

Large Language Models represent the cutting edge of AI, transforming how we interact with information, create content, and build intelligent systems. This page within AI Universe Courses curates the most valuable free learning resources focused on LLMs. The goal is to help learners understand the theory, architectures, and applications of models like GPT, LLaMA, PaLM, and beyond.

LLM education isn’t just about training giant networks — it’s about learning the full lifecycle: from tokenization and attention mechanisms to alignment, fine-tuning, deployment, and governance. By following the listed courses, students gain both the technical foundation and practical skills to apply LLMs responsibly in real-world domains.

🎯 What to Expect from LLM Courses

  • Foundations of LLMs

    • How they differ from traditional NLP models.
    • Core components: embeddings, transformers, self-attention.

  • Training & Fine-Tuning

    • Scaling laws, datasets, and optimization challenges.
    • Parameter-efficient fine-tuning (LoRA, adapters).

  • Applications

    • Text generation, summarization, reasoning, chatbots, code assistants.
    • Multi-modal extensions (text-to-image, text-to-audio).

  • Evaluation & Limitations

    • Benchmarks, accuracy vs hallucinations.
    • Bias, safety, and ethical considerations.

  • Deployment & Governance

    • Serving models efficiently (APIs, vector databases, RAG).
    • Privacy, compliance, and alignment with responsible AI practices.

Got it. You basically want one giant unified table where all three universes (ML, DL, LLM) sit together, cleanly aligned, instead of two half-baked versions. Here’s the draft in Markdown table form (which we can later render into a PDF or poster for your team):

🧠 Unified ML + DL + LLM Cheat Sheet (2025)

This unified cheat sheet merges classical machine learning, deep learning architectures, and the modern LLM stack into a single reference. It shows each method’s best use case, logic, assumptions, strengths, weaknesses, and real-world examples.

  • Classic ML → fast, interpretable, great for tabular/small data.
  • Deep Learning → excels at vision, sequences, and raw signals.
  • Generative Models → power synthetic data, creative tasks, and multimodal AI.
  • LLMs → transformers, scaling laws, retrieval, fine-tuning, and alignment.
  • Support Layers → embeddings, multimodality, RAG, safety guardrails.

Think of it as a timeline of progress: from regression → ensembles → neural nets → transformers → multimodal LLM ecosystems.

Algorithm / Approach Learning Type Best Use Case Core Logic Assumptions Pros Cons When NOT to Use Example
Linear Regression Supervised Predicting continuous values y = b₀ + b₁x + … Linearity, independence Simple, fast, interpretable Outlier sensitive Non-linear data House price prediction
Logistic Regression Supervised Binary classification Sigmoid on linear combo Log-odds linearity Probabilistic, interpretable Weak on complex boundaries Highly non-linear data Spam detection
Decision Tree Supervised Classification / regression Recursive binary split None Easy to interpret Overfits, unstable Very noisy data Loan default
Random Forest Supervised Ensemble accuracy Bagging + averaging trees Tree independence High accuracy, robust Slower, less interpretable Real-time needs Fraud detection
Gradient Boosting / XGBoost / LightGBM Supervised High-performance tabular Additive trees minimizing loss Sequential dependence SOTA accuracy, handles missing data Overfitting, complex tuning Very small datasets Credit scoring, Kaggle
SVM Supervised Max-margin classification Kernel trick Separability, scaling Works in high-dim Slow on large data Large/noisy datasets Facial recognition
KNN Supervised Few-shot / recommendation Distance-based majority vote Feature scaling Simple, no training Slow at inference High-dimensional noise Recommender systems
Naive Bayes Supervised Text classification Bayes + independent features Feature independence Fast, good for text Fails w/ correlated features Strong feature dependence Sentiment analysis
K-Means Unsupervised Customer segmentation Minimize intra-cluster distance Equal clusters Fast, simple Needs K, scale sensitive Non-spherical clusters Market segmentation
Hierarchical Clustering Unsupervised Structure discovery Dendrograms Distance metric No need for K Expensive on big data Very large datasets Gene expression
Gaussian Mixture Models (GMM) Unsupervised Soft clustering EM on mixtures Gaussian distribution Handles uncertainty Sensitive to init Non-Gaussian data Speaker ID
DBSCAN Unsupervised Arbitrary cluster shapes Density-based Cluster density Noise tolerant Struggles w/ varying density Sparse, high-dim Geo-spatial clustering
PCA Dim. Reduction Reduce dimensionality Covariance eigenvectors Variance matters Noise reduction, speed Hard to interpret All features important Image compression
t-SNE / UMAP Unsupervised Nonlinear reduction/vis Neighbor embedding Local structure Great for visualization Slow, non-deterministic Need exact distances Embedding visualization
MLP (Neural Net) Supervised Complex patterns Weighted sums + activations Smooth scaling Nonlinear learning Needs big data Small data, low compute Tabular DL
CNN Supervised Images, spatial Convolutions + pooling Local connectivity Excellent for vision Compute-heavy Sequential/text Self-driving vision
RNN Supervised Sequences Feedback loops Sequential structure Works for short sequences Vanishing gradients Long sequences Stock prediction
LSTM / GRU Supervised Long-sequence tasks Gated memory cells Sequential dependence Handles vanishing gradients Slower, compute-heavy Non-sequential data Machine translation (pre-LLM)
Autoencoders (VAE, Denoising) Unsupervised Anomaly, compression Encoder → Decoder Symmetry, latent code Denoising, representation Overfitting risk No need for compression Fraud detection
GANs Unsupervised / Self-sup Synthetic data, images Generator vs. Discriminator Training stability Realistic generation Mode collapse, unstable Limited compute Deepfakes, augmentation
Diffusion Models Generative Image/audio/video synthesis Iterative denoising Large data, compute SOTA realism Very slow, compute heavy Small data DALL·E, Stable Diffusion
Transformers (BERT, GPT) Supervised / Self-sup NLP, chat, multimodal Attention + positional encoding Large corpora Long context, parallelizable Huge compute Small projects ChatGPT, Translation
MoE (Mixture of Experts) Supervised Scalable LLMs Sparse expert activation Expert diversity Efficient scaling Routing complexity Tiny models DeepSeek, Gemini
RAG (Retrieval-Augmented Generation) Hybrid LLM + search Embeddings + vector DB retrieval High-quality corpus External knowledge injection Latency, pipeline complexity Tiny tasks LLM + pgvector
RLHF / DPO Reinforcement Aligning LLMs Reward models, prefs Human/AI feedback Alignment, safer outputs Expensive, noisy labels Low-stakes apps ChatGPT alignment
Q-Learning / Policy Gradient Reinforcement Sequential decision-making Bellman equation Markov structure Learns policies autonomously Sample inefficient Non-episodic Game AI, RLHF
Embeddings (FAISS, Milvus, pgvector) Support Layer Semantic search Vector similarity Embedding quality Great for retrieval Storage + scale Toy projects RAG, recommendations
Multimodal Fusion (text+img+audio) Supervised / Self-sup Unified AI Cross-attention Shared embedding space Flexible, future-proof Heavy compute Narrow domain GPT-4o, Gemini
Scaling Laws Meta LLM growth planning Loss ∝ (params, data, compute) Smooth scaling Predictable Expensive Hobby projects Kaplan curves
Guardrails / Tool Use Practical Layer Safety, API calls Policy layers Human-in-loop Prevents misuse Limits flexibility Toy research LLM agents

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