Learn how to set up the Raspberry Pi AI Kit with the new Raspberry Pi 5. The kit allows you to explore machine learning and AI concepts using Python and TensorFlow.
Generate realistic sequential data with this easy-to-train model. This article explores using Variational Autoencoders (VAEs) to model and generate time series data. It details the specific architecture choices, like 1D convolutional layers and a seasonally dependent prior, used to capture the periodic and sequential patterns in temperature data.
This paper presents a method to accelerate the grokking phenomenon, where a model's generalization improves with more training iterations after an initial overfitting stage. The authors propose a simple algorithmic modification to existing optimizers that filters out the fast-varying components of the gradients and amplifies the slow-varying components, thereby accelerating the grokking effect.
An illustrated and intuitive guide on the inner workings of an LSTM, which are an improvement on Recurrent Neural Networks (RNNs) that struggle with retaining information over long distances.
Gemma Scope is an open-source, multi-scale, high-throughput microscope system that combines brightfield, fluorescence, and confocal microscopy, designed for imaging large samples like brain tissue.
This article explores some of the mysteries and unsolved phenomena in machine learning, focusing on concepts like Batch Normalization, overparameterized models, and the implicit regularization effects of gradient descent.
This article explores TimeMixer, a new time series forecasting model, and its implementation. The article delves into its inner workings and provides a benchmark comparison with other models.
An explanation of the backpropagation through time algorithm and how it helps Recurrent Neural Networks (RNNs) learn from sequence-based data
An article discussing the importance of explainability in machine learning and the challenges posed by neural networks. It highlights the difficulties in understanding the decision-making process of complex models and the need for more transparency in AI development.
Discusses the trends in Large Language Models (LLMs) architecture, including the rise of more GPU, more weights, more tokens, energy-efficient implementations, the role of LLM routers, and the need for better evaluation metrics, faster fine-tuning, and self-tuning.
