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aiguideReading NCCU Yen-Lung Tsai Generative AI

Neural Network Concepts | NCCU Yen-Lung Tsai Generative AI Lecture 2

Lecture 2 opens up last week's "dopey AI robot." Inputs and outputs must become numbers (tensors). Classification uses one-hot labels and softmax to turn scores into probabilities. A neural network is neurons stacked layer by layer, and training means pushing the loss down with gradient descent. The lecture ends by building a first fully connected network on MNIST in Keras and wiring it to a Gradio sketchpad. Homework 2 asks you to design your own DNN, with one hard rule: it can't have three layers. The Chang Gung satellite rubric wants a screenshot of the parameters with the best validation accuracy and encourages keeping failed attempts.

#nccu-generative-ai#ai-course#neural-networks#deep-learning#keras#homework
techguideReading Harvard CS50 Introduction to Artificial Intelligence with Python

Neural Networks — Backpropagation, TensorFlow/Keras, CNN & Traffic Sign Classification | Harvard CS50 AI Week 5

Week 5 is about neural networks: a unit multiplies its inputs by weights, adds a bias and applies an activation function; gradient descent adjusts the weights one small step at a time, and backpropagation passes the error back so hidden layers can be trained; for images, convolution and pooling come first and an ordinary network follows, which is a CNN; for sequences, an RNN feeds its output back into itself. With time links for every section, plus the approach, common mistakes and checks for the Traffic project.

#harvard-cs50ai#ai#neural-networks#backpropagation#tensorflow#keras#cnn#traffic#python#cs50