The barrier to entry for learning artificial intelligence has never been lower, but the signal-to-noise ratio has never been worse. With thousands of "AI bootcamps" promising to teach you everything ...

This important study introduces a fully differentiable variant of the Gillespie algorithm as an approximate stochastic simulation scheme for complex chemical reaction networks, allowing kinetic ...

Machine learning is a branch of AI focused on building computer systems that learn from data. The breadth of ML techniques enables software applications to improve their performance over time. ML ...

Hugging Face Transformers is a state-of-the-art machine learning library that provides easy access to a wide range of pre-trained models for Natural Language Processing (NLP), Computer Vision, Audio ...

This work will be of interest to the motor control community as well as neuroAI researchers interested in how bodies constrain neural circuit function. The authors present "MotorNet", a useful ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. A critical bottleneck for the training of large neural networks (NNs) is communication ...

Tangent is a new, free, and open-source Python library for automatic differentiation. Existing libraries implement automatic differentiation by tracing a program's execution (at runtime, like PyTorch) ...

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from linear regression to deep ...

Unlock the power of Transformer Networks and learn how to build your own GPT-like model from scratch. In this in-depth guide, we will delve into the theory and provide a step-by-step code ...

Recurrent neural networks (RNNs) have been proved very successful at modeling sequential data such as language or motions. However, these successes rely on the use of the backpropagation through time ...

Emerging two-terminal nanoscale memory devices, known as memristors, have demonstrated great potential for implementing energy-efficient neuro-inspired computing architectures over the past decade. As ...