Deep learning is a subset of machine learning that uses neural networks with many hidden layers to automatically discover patterns in raw data. Unlike classical ML algorithms that rely on hand-crafted features, deep neural networks learn hierarchical representations — extracting simple features first (edges, syllables), then combining them into increasingly abstract concepts (faces, meaning). In … Read more
A neural network is a machine-learning model composed of layers of interconnected nodes (neurons) that learn to map inputs to outputs by adjusting numerical weights and biases during training. Each neuron computes a weighted sum of its inputs, adds a bias, and passes the result through a non-linear activation function. y = σ(Σ wᵢxᵢ + … Read more
LoRA (Low-Rank Adaptation) is a parameter-efficient fine-tuning method that freezes the base LLM and trains only small low-rank adapter matrices injected into selected layers. Instead of updating a full weight matrix W ∈ ℝd×k, LoRA learns two compact matrices A and B where the rank r ≪ min(d, k): W′ = W + B · A This reduces trainable parameters … Read more
Machine learning (ML) is a branch of artificial intelligence in which algorithms learn patterns from data — instead of following hard-coded rules — and improve their performance on a defined task as they process more examples. The three core paradigms are supervised learning (labeled data), unsupervised learning (unlabeled data) and reinforcement learning (reward-based interaction). In … Read more
Artificial intelligence (AI) is the branch of computer science focused on building systems that perform tasks associated with human intelligence — learning, reasoning, planning, perception, and communication. Modern AI spans everything from rule-based expert systems and classical machine learning to deep neural networks and large language models, and it is governed by an evolving regulatory landscape including … Read more