AI Inference: From Input to Output Inference is when a trained model makes predictions on new data. While training happens once, inference runs millions of times in production. Understanding the inference pipeline is crucial for deploying AI systems that are fast, efficient, and reliable. The pipeline involves preprocessing inputs (tokenizing text, resizing images, normalizing values),…
Fine-Tuning: Adapting Pre-trained Models Fine-tuning transfers knowledge from large pre-trained models to specific tasks with limited data. This paradigm revolutionized AI by making powerful models accessible without requiring massive datasets or compute budgets for each new application. The process involves taking a model pre-trained on massive general data, then continuing training on task-specific data with…
GPT: Inside Large Language Models GPT (Generative Pre-trained Transformer) uses a decoder-only architecture to generate text token by token. Understanding its internals reveals how modern AI achieves remarkable language capabilities through elegant architectural choices. Each token receives embedding plus positional encoding, then passes through stacked Transformer blocks. Each block contains masked self-attention (can only attend…
Dropout: Regularization Through Randomness Dropout prevents overfitting by randomly deactivating neurons during training. This forces the network to learn redundant representations and prevents co-adaptation where neurons rely too heavily on specific other neurons being present. During training, each neuron is kept with probability p, typically 0.5 for hidden layers and 0.8-0.9 for input layers. During…
Batch Normalization: Stabilizing Training Batch normalization dramatically improved training stability and speed when introduced in 2015. By normalizing layer inputs, it reduces internal covariate shift and allows much higher learning rates, accelerating convergence significantly. For each mini-batch, BatchNorm calculates mean and variance of activations, normalizes them to zero mean and unit variance, then applies learnable…
The Generalization Challenge The core challenge in machine learning is generalizing from training data to new examples. A model that memorizes training data perfectly but fails on new data is useless. Understanding overfitting and underfitting is essential for building models that actually work in production. Underfitting occurs when the model is too simple to capture…
Gradient Descent: Finding Optimal Solutions Gradient descent is the optimization engine powering machine learning. It finds minimum error by iteratively moving in the direction of steepest descent on the loss surface. Understanding this algorithm is fundamental to training any neural network effectively. Three main variants exist. Batch gradient descent uses the entire dataset per update…
Tokenization: Breaking Text for AI Before AI can process text, it must be split into tokens – the fundamental units the model works with. Tokenization strategy significantly impacts model performance, vocabulary size, and ability to handle rare or novel words. Getting it right matters more than many realize. Word-level tokenization splits on whitespace, creating intuitive…
Word Embeddings: Meaning in Vectors Word embeddings convert discrete tokens into dense vectors where semantic similarity corresponds to geometric proximity. This representation enables neural networks to understand that king and queen are related, or that Paris relates to France as Rome relates to Italy. Early methods like Word2Vec learn embeddings by predicting context from words…
Self-Attention: The Core of Transformers Self-attention allows each element in a sequence to attend to all other elements, capturing relationships regardless of distance. This mechanism revolutionized NLP by replacing recurrence with parallelizable attention operations that model long-range dependencies more effectively. For each token, three vectors are computed: Query (what am I looking for), Key (what…
