A visual proof that neural nets can compute any function
A visual proof that neural nets can compute any function
9/21/2014
link
summary
This webpage is part of the online book "Neural Networks and Deep Learning" and specifically focuses on chapter 4. The chapter discusses the concept of gradient descent, which is an optimization algorithm commonly used in training neural networks. It explains how gradient descent works mathematically and provides intuitive explanations. The webpage also provides interactive visualizations to help readers understand the concept better. It covers topics like learning rate, local minima, and stochastic gradient descent. Overall, this chapter serves as a comprehensive guide to understanding and implementing gradient descent in the context of neural networks.
tags
neural networks ꞏ deep learning ꞏ machine learning ꞏ artificial intelligence ꞏ computer science ꞏ backpropagation ꞏ feedforward neural networks ꞏ gradient descent ꞏ activation functions ꞏ cost functions ꞏ optimization algorithms ꞏ training neural networks ꞏ error calculation ꞏ weight initialization ꞏ neural network architecture ꞏ neural network layers ꞏ neural network parameters ꞏ deep learning frameworks ꞏ convolutional neural networks ꞏ recurrent neural networks ꞏ supervised learning ꞏ unsupervised learning ꞏ reinforcement learning ꞏ pattern recognition ꞏ image classification ꞏ natural language processing ꞏ data preprocessing ꞏ data augmentation ꞏ overfitting ꞏ underfitting ꞏ cross-validation ꞏ model evaluation ꞏ regularization ꞏ dropout ꞏ hyperparameter tuning ꞏ transfer learning ꞏ deep reinforcement learning ꞏ neural network applications ꞏ deep learning algorithms ꞏ deep learning models ꞏ deep learning techniques ꞏ neural network libraries ꞏ neural network training ꞏ neural network performance ꞏ neural network convergence ꞏ neural network optimization ꞏ deep learning research