AIdventure.

Speeding up Attention Layers

Multi-head, Multi-Query & Grouped-Query Attention layers clearly explained. How cache works in the Attention layers

TF-IDF & BM25 Explained

TF-IDF and BM25 are two of the most used algorithms in Information Retrieval. In this post we will explain how they work

Precision, Recall and F1 Score metrics

Dive into the intuition behind Precision, Recall, and F1 Score. Understand how these metrics balance quality and quantity, from binary problems to object detection.

Load your datasets in Label Studio with JSON

Configure local file serving and JSON imports to handle thousands of files in seconds—the production-ready approach

LoRA - Low-Rank Adaptation of Large Language Models

Fine-tuning large language models via trainable rank decomposition matrices

DINO - Emerging Properties in Self-Supervised Vision Transformers

Unsupervised visual feature learning using knowledge distillation and transformers

CLIP - Contrastive Language-Image Pre-training

Contrastive learning for unified vision-language representations in a shared embedding space

Vision Transformer - An Image is Worth 16x16 Words

Google shows how treating image patches as tokens can revolutionize computer vision

DistillBERT - A distilled version of BERT

Knowledge distillation compresses BERT: smaller, faster, with almost all performance

RoBERTa - Optimized BERT Pretraining Approach

Unlocking the true potential of BERT through rigorous optimization and strategic training choices

BERT - Bidirectional Encoder Representations from Transformers

Pre-training bidirectional by jointly conditioning on both left and right context

The Receptive Field in Convolutional Neural Networks

Why do architectures use 3x3 filters? It is because of something called Receptive Fields.

GPT1 - Improving Language Understanding by Pre-Training

Semi-supervised learning through generative pre-training on unlabeled text and task-specific fine-tuning

SENet - Squeeze and Excitation Networks

Introducing channel attention to improve the performance of image classification tasks

Transformer - Attention Is All You Need

Demystifying the Transformer architecture, explaining the Encoder, Decoder, and Attention mechanisms block by block with PyTorch implementation

MobileNet v1 - Efficient Convolutional Neural Networks

Efficient convolutional neural networks for mobile vision applications

Depthwise Separable Convolutions

Same results as standard convolutions with only a fraction of the computational cost. Explore the tricks behind MobileNet and efficient CNNs

DenseNet - Densely Connected Convolutional Networks

Connecting each layer to every other layer to maximize information flow and efficiency

ResNet - Deep Residual Learning for Image Recognition

Learn how residual blocks help solve the vanishing gradient problem to enable training of extremely deep neural networks

VGG - Very Deep Convolutional Networks

Explore how VGG revolutionized computer vision by using small 3x3 filters to build deeper networks