This is a great introduction.
Neural Networks are great identifying patterns in data. As a classic example, if you wanted to predict housing prices, you could build a data set that maps features about houses (square feet, location, proximity to Caltrain, etc) onto their actual price, and then train a network to recognize the complex relationship between features and pricing. Training happens by feeding the network features, letting it make a guess about the price, and then correcting the guess (backpropagation).
Convolutional Neural Networks work similarly, but with images. Instead of giving a CNN discrete features, you'll usually just use the pixels of the image itself. Through a series of layers, the CNN is able to build features itself (traditionally things like edges, corners) and learn patterns in image data. For example, a CNN might be trained on a dataset that maps images onto labels, and learn how to label new images on its own.
[A Beginner's Guide To Understanding Convolutional Neural Networks](https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/)
How I Shipped a Neural Network on iOS with CoreML, PyTorch, and React Native - Detailed and awesome article.
TensorSpace.js - Neural network 3D visualization framework, build interactive and intuitive model in browsers, support pre-trained deep learning models from TensorFlow, Keras, TensorFlow.js
UIS-RNN - Library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.
ONNX - Open Neural Network Exchange.
DyNet - Dynamic Neural Network Toolkit.
gonn - Building a simple neural network in Go.
MindsDB - Framework to streamline use of neural networks.
Learning and Processing over Networks (2019) - Workshop presented by Michaël Defferrard and Rodrigo Pena at the Applied Machine Learning Days in January 2019.