Introduction The vanilla ViT is problematic. If you take a look at the original ViT paper [1], you’ll notice that although this deep learning model proved to work extremely well, it requires hundreds…

Use LazyPredict and PyCaret to skip the grunt work and jump straight to performance.

We roll up our sleeves and start to deal with matrices

A recipe for building a portable soundscape monitoring app with AudioMoth, Raspberry Pi, and a decent dose of deep learning.

Exploring Titans: A new architecture equipping LLMs with human-inspired memory that learns and updates itself during test-time.

Complete guide to feature selection, threshold optimization, and neural network architecture for ML competitions The post I Won $10,000 in a Machine Learning Competition — Here’s My Complete Strategy…

Restoring speech with brain-computer interfaces: How close are we to real-time voice synthesis for those who can't speak?

Transforming Independent Models into Collaborative Intelligence

The Tokenizer Has Been a Necessary Evil, but This Radical Approach Shows That It Might Not Be Necessary Anymore. The post Why Your Next LLM Might Not Have A Tokenizer appeared first on Towards Data…

PyTorch Model Performance Analysis and Optimization — Part 9

How to save time and boost your models with these two approachable AutoML libraries.

How Meta’s latest breakthrough lets models learn, adapt, and improve — all on their own The post The Age of Self-Evolving AI Is Here appeared first on Towards Data Science. Generated by RSStT. The…

A practical approach to instance segmentation using SOLOv2 and TensorFlow

Building a tool to interactively visualize the forward pass of any Pytorch model from within notebooks. The post Torchvista: Building an Interactive Pytorch Visualization Package for Notebooks…

is a relatively new sub-field in AI, focused on understanding how neural networks function by reverse-engineering their internal mechanisms and representations, aiming to translate them into human-understandable algorithms and concepts. This is in contrast…

Solving the Heat Equation using DeepXDE. The post Physics-Informed Neural Networks for Inverse PDE Problems appeared first on Towards Data Science. Generated by RSStT. The copyright belongs to the original author. Source

Learn how to LLMs are benchmarked, and try out the newly released ARC AGI 3 The post How to Benchmark LLMs – ARC AGI 3 appeared first on Towards Data Science. Generated by RSStT. The copyright belongs to the original author. Source

POS tagging, dependency parser and named entity recognition. The post Mastering NLP with spaCy – Part 2 appeared first on Towards Data Science. Generated by RSStT. The copyright belongs to the original author. Source

What happens when you stop concatenating and start decomposing: a new way to think about attention. The post Mechanistic View of Transformers: Patterns, Messages, Residual Stream… and LSTMs appeared first on Towards Data Science. Generated by RSStT. The copyright…

Practical Neuroevolution: Reproducing NEAT’s Innovations and Code Walkthrough The post From Genes to Neural Networks: Understanding and Building NEAT (Neuro-Evolution of Augmenting Topologies) from Scratch appeared first on Towards Data Science. Generated…