This post is divided into three parts; they are: • Why Linear Layers and Activations are Needed in Transformers • Typical Design of the Feed-Forward Network • Variations of the Activation Functions The attention layer is the core function of a transformer model.
This post is divided into five parts; they are: • Why Normalization is Needed in Transformers • LayerNorm and Its Implementation • Adaptive LayerNorm • RMS Norm and Its Implementation • Using PyTorch’s Built-in Normalization Normalization layers improve model quality in deep learning.
Machine learning practitioners spend countless hours on repetitive tasks: monitoring model performance, retraining pipelines, data quality checks, and experiment tracking.
This post is divided into four parts; they are: • Why Attention Masking is Needed • Implementation of Attention Masks • Mask Creation • Using PyTorch’s Built-in Attention In the
Artificial intelligence (AI) is an umbrella computer science discipline focused on building software systems capable of mimicking human or animal intelligence capabilities to solve a task.
The intersection of traditional machine learning and modern representation learning is opening up new possibilities.
This post is divided into three parts; they are: • Low-Rank Approximation of Matrices • Multi-head Latent Attention (MLA) • PyTorch Implementation Multi-Head Attention (MHA) and Grouped-Query Attention (GQA) are the attention mechanisms used in almost all transformer models.
Pandas DataFrames are powerful and versatile data manipulation and analysis tools.
Ever felt like trying to find a needle in a haystack? That’s part of the process of building and optimizing machine learning models, particularly complex ones like ensembles and neural networks, where several hyperparameters need to be manually set by us before training them.
This post is divided into three parts; they are: • Why Attention is Needed • The Attention Operation • Multi-Head Attention (MHA) • Grouped-Query Attention (GQA) and Multi-Query Attention (MQA) Traditional neural networks struggle with long-range dependencies in sequences.