Neural Networks by Example

Explore the principles and applications of neural networks in this comprehensive conference talk. Delve into the history and fundamental concepts of neural networks, understanding how they differ from conventional algorithms and their unique problem-solving capabilities. Gain insights into real-world applications such as self-driving cars, image recognition, automated translation, and text analysis. Follow along with a practical example of handwritten digit recognition using the MNIST dataset and convolutional neural networks, implemented with Microsoft CNTK and TensorFlow frameworks. Learn about various neural network types, including convolutional networks, feature maps, and fully connected networks, while examining a NIST case study to solidify your understanding of these powerful machine learning tools.

Introduction
Neural networks history
Neurons
Neural Networks
Simple Neural Network
Hidden Neural Network
Neural Networks Types
Convolutional Networks
Feature Map
Fully Connected
Convolutional
NIST Case Study