Artificial Intelligence

Artificial Intelligence / 04 Sep 2026 / 11 min read

Use Label Smoothing Without Hiding Classification Mistakes

A classifier trained with ordinary cross-entropy is usually given a hard target: the correct class has probability 1, and every other class has probability 0. For a three-class example: cat dog bird 1.0 0.0 0.0 That target is simple and often appropriate. But it also asks the model to keep increasing the correct-class logit relative to the others even after the prediction is already very confident.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 8 min read

Use Dropout Without Breaking Neural Network Inference

A neural network can fit its training data well while performing poorly on new examples. One way to reduce this kind of overfitting is dropout, a training technique that randomly removes some activations on each forward pass. The idea is simple, but one detail causes many implementation bugs: dropout is intentionally stochastic during training and normally disabled during inference. If those modes are confused, evaluation becomes noisy or predictions use the wrong activation scale.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 10 min read

Teacher Forcing in Autoregressive Models

An autoregressive model generates a sequence one element at a time. A language model predicts the next token from the tokens before it; a sequence model might similarly predict the next symbol, event, or value from an existing prefix. That creates a practical training question: when teaching the model to predict step 5, should the input contain the correct steps 1–4 from the dataset, or the model’s own earlier predictions?

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 9 min read

Stop Neural Network Training at the Right Time with Early Stopping

Training a neural network for more steps usually gives the optimizer more opportunities to reduce training loss. That does not mean the resulting model will perform better on unseen data. After useful patterns have been learned, continued training can increasingly fit details that are specific to the training set. Early stopping turns this observation into a practical training rule: evaluate the model on held-out validation data during training, remember the best checkpoint, and stop when meaningful validation improvement has not appeared for long enough.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 9 min read

Stabilize Neural Network Evaluation with EMA Weights

Neural network training does not usually move parameters smoothly toward one final point. Mini-batch gradients are noisy, learning-rate schedules change step sizes, and later updates can move a model between nearby parameter settings with noticeably different validation results. That creates a practical question: should deployment use the parameters from one particular training step, or a smoothed version of several recent parameter states? An exponential moving average, or EMA, provides the second option. During training, it maintains a separate copy of the model parameters that changes more slowly than the actively optimized parameters. The optimizer still trains the ordinary model. The EMA copy is typically used for evaluation or inference.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 8 min read

Reduce Training Memory with Gradient Checkpointing

Training a neural network can run out of accelerator memory even when the model parameters fit comfortably. The missing piece is often activations: intermediate values produced during the forward pass and retained because backpropagation needs them later. Gradient checkpointing, also called activation checkpointing, trades extra computation for lower activation memory. Instead of keeping every intermediate activation until the backward pass, training keeps selected checkpoints and recomputes missing forward values when their gradients are needed.

Artificial Intelligence Neural Networks

Artificial Intelligence / 04 Sep 2026 / 9 min read

Perplexity for Language Model Evaluation

A language model can assign high probability to likely text and low probability to unlikely text, but developers still need a compact way to summarize that behavior across many tokens. Perplexity is one common metric for this job. Perplexity is useful when comparing probabilistic language models on the same evaluation data under compatible tokenization and scoring rules. It is much less useful as a general score for whether generated answers are correct, helpful, safe, or well written.

Artificial Intelligence LLM

Artificial Intelligence / 04 Sep 2026 / 10 min read

Negative Sampling for Representation Learning

Some representation-learning problems have an awkward shape: each training example has one observed target, but the model could choose from thousands or millions of alternatives. Computing a score and normalization term for every alternative on every update can become a major training cost. Negative sampling changes the training problem. Instead of comparing the observed target with every possible alternative, the model learns from the observed positive pair and a small set of deliberately sampled negative pairs. The update becomes much cheaper, but it also optimizes a sampled discrimination objective rather than the exact full-class objective.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 11 min read

Mean Teacher for Semi-Supervised Learning with Unlabeled Data

Many machine learning projects have far more raw examples than labeled ones. A team may have millions of images, audio clips, or sensor readings, but only a small subset has been reviewed by people. Standard supervised training ignores the unlabeled remainder because it has no target labels to compare with the model’s predictions. Mean Teacher provides a way to use those unlabeled examples without pretending that their unknown labels are known. It trains a student model to make predictions that stay consistent with a more slowly changing teacher model. The teacher is not a separately trained expert: its parameters are an exponential moving average of the student’s parameters.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 10 min read

Macro F1 and Balanced Accuracy for Imbalanced Classifiers

A classifier can report impressive accuracy while failing on the cases you care about most. This happens easily when one class is much more common than another. Imagine a model that detects defective components on a production line. In a test set of 1,000 components, 950 are normal and 50 are defective. A model that predicts normal for every component is correct 95% of the time, yet it detects none of the defects.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 10 min read

Layer Normalization in Transformers

Transformer diagrams often contain small boxes labeled LayerNorm or Norm. They are easy to treat as plumbing between attention and feed-forward layers, but normalization has an important job: it controls the scale of hidden activations as information passes through many residual blocks. That matters because a transformer repeatedly adds new updates to an existing residual stream. If activation scales become poorly behaved, optimization can become harder and numerical problems can become more likely. Layer normalization gives each normalized hidden vector a predictable scale while preserving learnable degrees of freedom.

Artificial Intelligence Neural Networks

Artificial Intelligence / 04 Sep 2026 / 9 min read

Interpret Language Model Perplexity Correctly

A language model can improve on its training objective while still leaving an important question unanswered: how well does it predict text it did not train on? Perplexity is a compact way to measure that predictive fit for autoregressive language models, but the number is easy to misuse. A lower perplexity can mean that a model assigns higher probability to held-out text. It does not automatically mean that the model follows instructions better, reasons more reliably, hallucinates less, or produces more useful answers. Comparisons can also become misleading when tokenization, evaluation data, or context handling differs.

Artificial Intelligence LLM

Artificial Intelligence / 04 Sep 2026 / 9 min read

Handle Class Imbalance in Machine Learning

A classifier can achieve impressive accuracy while failing on the cases you care about most. If only 1% of transactions are fraudulent, a model that predicts “not fraud” for every transaction is 99% accurate and still useless for detecting fraud. This is the practical problem of class imbalance: some target classes appear much less often than others. Imbalance does not automatically make a dataset bad, and it does not imply that every model needs special treatment. It does mean that accuracy can hide important errors and that the training objective may give rare examples too little influence.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 10 min read

Gradient Noise in Mini-Batch Training

Neural network training usually updates model parameters from a small batch of examples rather than computing a gradient over the entire training set. That makes each update cheaper, but it also means the update direction depends on which examples happened to enter the batch. This variation is often called gradient noise. It is not necessarily a bug. It is a consequence of estimating a dataset-wide gradient from a sample, and it creates an important trade-off between computation per update, update variability, and training throughput.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 9 min read

Focus Classification Training with Focal Loss

A classifier can spend much of its training signal on examples it already handles confidently. This is especially noticeable when a dataset contains a large number of easy examples and a much smaller set of difficult ones: the easy cases can dominate the aggregate loss simply because there are so many of them. Focal loss changes that balance. It starts from cross-entropy and reduces the contribution of examples the model already predicts confidently, leaving difficult examples with greater relative influence. The idea is simple, but using it well requires understanding what “hard” means, how its parameters affect optimization, and why focusing too aggressively can amplify noisy labels.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 9 min read

Estimate Model Uncertainty with Monte Carlo Dropout

A neural network can produce a confident-looking prediction without telling you how sensitive that prediction is to uncertainty in the learned model. This matters when an application must decide whether to trust a prediction, request more information, or route a case for review. Monte Carlo dropout, often shortened to MC dropout, provides one practical uncertainty signal for networks trained with dropout. Instead of disabling dropout for inference, it keeps dropout stochastic and evaluates the same input repeatedly. Variation across those predictions reveals how strongly the result depends on the sampled dropout masks.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 10 min read

Detect Out-of-Distribution Inputs Before Trusting a Model

A model can produce a confident-looking prediction for an input that is unlike anything it was designed to handle. A product classifier trained on shoes, shirts, and bags still has to return some class when given a photo of a bicycle. The classifier’s output layer does not automatically gain an unknown class just because the input is unfamiliar. This is the problem addressed by out-of-distribution detection, usually shortened to OOD detection. The goal is to recognize inputs that differ meaningfully from the data the model is expected to handle, before the application treats an ordinary model prediction as trustworthy.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 10 min read

Detect Distribution Shift Before Model Quality Fails

A model can pass offline evaluation and still become less useful after deployment. The model may not have changed at all. Instead, the data reaching it may have changed. A fraud classifier trained on last year’s transactions may encounter a new payment pattern. A support-ticket model may see terminology introduced by a new product. An image model deployed to different hardware may receive images with different lighting or compression. These are forms of distribution shift: the statistical conditions seen in production differ from those represented by the data used to develop or evaluate the model.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 10 min read

Design Model Abstention for Uncertain Predictions

A model does not have to make a decision on every input. In many applications, forcing a prediction is exactly what turns an uncertain case into an expensive mistake. Consider a classifier that routes support tickets to billing, account, or technical teams. Most tickets are straightforward, but some are vague or combine several problems. If the application automatically accepts every prediction, the model must act even when its evidence is weak. A better system can automate clear cases and send uncertain ones to a fallback such as human review.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 9 min read

Contrastive Learning with Positive and Negative Pairs

An embedding model turns an input into a vector so that useful relationships can be measured numerically. The difficult part is not producing vectors. It is teaching the geometry of the vector space: which inputs should be close, which should be far apart, and what “similar” should mean for the application. Contrastive learning provides a practical answer. Instead of training only from a class label such as billing or technical support, it trains from relationships between examples. A positive pair contains examples that should have similar representations. A negative pair contains examples that should not.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 8 min read

Choose Between Causal and Masked Language Modeling

Two language models can use similar transformer components yet learn from text in very different ways. One may predict the next token from everything to its left. Another may hide selected tokens and reconstruct them from surrounding text. That training choice changes what information is available during learning and strongly influences which tasks the resulting model naturally supports. These objectives are called causal language modeling and masked language modeling. Understanding the distinction helps when choosing a pretrained model, interpreting its outputs, or designing a training objective for a language task.

Artificial Intelligence Machine Learning

Artificial Intelligence / 04 Sep 2026 / 9 min read

Batch Normalization in Neural Networks

A neural network can become harder to train when the scale and distribution of intermediate activations change as earlier layers update. One technique for controlling those activations is batch normalization, usually shortened to BatchNorm. BatchNorm looks simple: normalize a layer’s activations, then learn a scale and offset. The important detail is that its behavior depends on mode. During training it normally uses statistics from the current mini-batch. During inference it normally uses running statistics collected during training. Confusing those two paths can produce a model that trains normally but behaves poorly when deployed.

Artificial Intelligence Machine Learning

Artificial Intelligence / 03 Sep 2026 / 6 min read

Use Few-Shot Prompting with Effective Examples

A prompt can explain a task with instructions, but sometimes examples communicate the desired behavior more precisely. Few-shot prompting places a small number of input-output demonstrations in the model’s context before the real input. This technique is useful when a task has a specific output format, subtle classification boundary, naming convention, or transformation rule that is difficult to describe completely in prose. The model is not retrained by these examples. Instead, it uses the demonstrations as part of the current context when generating the next response.

Artificial Intelligence LLM

Artificial Intelligence / 03 Sep 2026 / 10 min read

Train Larger AI Models with Gradient Accumulation

Training a neural network often becomes memory-bound before it becomes compute-bound. You may want a batch of 64 examples for stable optimization, but the model, activations, optimizer state, and input tensors leave enough accelerator memory for only 8 examples at a time. Reducing the batch size to 8 may work, but it also changes the optimization process. Gradient accumulation provides another option: process several smaller microbatches, add their gradients together, and update the model only after the desired effective batch has been processed.

Artificial Intelligence Machine Learning