A detailed overview of the architecture, Python implementation, and future of autoencoders, focusing on their use in feature extraction and dimension reduction in unsupervised learning.
Researchers have mapped the complete neural connectome of a fruit fly, detailing all 139,255 nerve cells and their connections. This advance offers insights into how the brain processes information.
This article introduces the Bayesian Neural Field (BayesNF), a method combining deep neural networks with hierarchical Bayesian inference for scalable and flexible analysis of spatiotemporal data, such as environmental monitoring and cloud demand forecasting.
"We present a systematic review of some of the popular machine learning based email spam filtering approaches."
"Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering."
"Our review covers survey of the important concepts, attempts, efficiency, and the research trend in spam filtering."
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.
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.
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.
An article discussing the concept of monosemanticity in LLMs (Language Learning Models) and how Anthropic is working on making them more controllable and safer through prompt and activation engineering.
A deep dive into the theory and applications of diffusion models, focusing on image generation and other tasks, with examples and PyTorch code.
