# Tag: Neural Networks by Synced 2022-04-04 5 ## Training Compute-Optimal Large Language Models: DeepMind’s 70B Parameter Chinchilla Outperforms 530B Parameter Megatron-Turing In the new paper Training Compute-Optimal Large Language Models, a DeepMind research team posits that current large language models are significantly undertrained and, based on empirical outcomes of over 400 training runs, proposes three predictive approaches for optimally setting model size and training duration. by Synced 2022-0

Erklärung von Recurrent Neural Networks, inklusive der verschiedenen Feedback Formen und der Abgrenzung zu LSTM

# Neural networks: representation. This post aims to discuss what a neural network is and how we represent it in a machine learning model. Subsequent posts will cover more advanced topics such as training and optimizing a model, but I've found it's helpful to first have a solid understanding of what it is we're actually building and a comfort with respect to the matrix representation we'll use. Prerequisites: - Read my post on logistic regression . - Be comfortable multiplying matrices together. ## Insp

This tutorial explains how to implement Sparse Convolutional Neural Networks in TensorFlow

Neural Networks 101 - Demystifying neural networks with intuitive explanations, real-world examples, and accessible insights for curious AI learners

The common terms used in Neural Networks are Convolution, Max Pooling, Fully Connected Layer, Softmax Activation Function and Rectified Linear Units

Discover how Self-Normalizing Neural Networks use SELU activations and tailored weight initialization to maintain stable activations and gradients for deeper models

Blog Sep 24, 2015 · post How do neural networks learn? Neural networks are generating a lot of excitement, as they are quickly proving to be a promising and practical form of machine intelligence. At Fast Forward Labs, we just finished a project researching and building systems that use neural networks for image analysis, as shown in our toy application Pictograph . Our companion deep learning report explains this technology in depth and explores applications and opportunities across industries. As we

Neural Networks news and updates covering the models underpinning much of current machine learning. Readers can learn about layers and activation functions, backpropagation and optimisation, architectures for vision, language, and sequences, regularisation and overfitting

Artificial neural networks are a crude abstraction of real neurons. There are various reasons that make AI researchers look at the human brain as an inspiration to develop such networks