Picture by Retronator Deep learning is a very weird technology. It evolved over decades on a very different track than the mainstream of AI, kept alive by the efforts of a handful of believers. When I started using it a few years ago, it reminded me of the first time I played with an iPhone -…

← Back to homepage Weight initialization in neural networks: what is it? August 22, 2019 by Chris An important predictor for deep learning success is how you initialize the weights of your model, or weight initialization in short. However, for beginning deep learning engineers, it's not always clear at first what it is - partially due to the overload of initializers available in contemporary frameworks. In this blog, I will introduce weight initialization at a high level by looking at the structure of

Graph Attention Networks (GATs) introduce a neural network architecture for graph-structured data that leverages masked self-attention to learn node representations. The approach achieves strong

Neural networks are a type of machine learning program that learns from examples they’re given, rather than relying on a human programmer to invent rules. In an earlier experiment, I trained a neural network to write new names for Dungeons and Dragons spells based on a list of 365 examples. That’s a really small dataset for a neural network to work with, and I ended up struggling to find training parameters that would strike a balance between word-for-word mimicry of the original list of spells

- Learning Objectives - Key Concepts - 1. What is a Neural Network? - 2. Perceptron (Single Neuron) - 3. Activation Functions - 4. Forward Propagation - 5. Backpropagation - 6. Multi-Layer Perceptron (MLP) Implementation - 7. Loss Functions - 8. Optimizer - 9. Vanishing Gradient Problem - Keras/TensorFlow Code - Overfitting Prevention - Dropout - Early Stopping - L2 Regularization - Hyperparameter Guide - Interview Questions Preview - Practice Notebook 12. Neural Network # 12. Neural Network Fundamentals

Python, machine learning, neural networks

Python, machine learning, neural networks

The world of artificial intelligence is an ever-evolving landscape, and today we're delving into a fascinating development that draws inspiration from the human brain. You see, our brains have this incredible ability to consolidate memories during sleep, sifting through the day's experiences and dec...

In this blog post, we'll be building a simple neural network from scratch using Pytorch. We'll go through the necessary steps to construct our network and

## Neural Tree Indexers for Text Understanding Tsendsuren Munkhdalai , Hong Yu Recurrent neural networks (RNNs) process input text sequentially and model the conditional transition between word tokens. In contrast, the advantages of recursive networks include that they explicitly model the compositionality and the recursive structure of natural language. However, the current recursive architecture is limited by its dependence on syntactic tree. In this paper, we introduce a robust syntactic parsing-indepe