Trịnh Tấn Đạt Khoa CNTT – Đại Học Sài Gòn Email: trinhtandat@sgu.edu.vn Website: https://sites.google.com/site/ttdat88/ Contents  Introduction  Perceptron  Neural Network  Backpropagation Algorithm Introduction ❖ What are artificial neural networks?  A neuron receives a signal, processes it, and      propagates the signal (or not) The brain is comprised of around 100 billion neurons, each connected to ~10k other neurons: 1015 synaptic connections ANNs are a simplistic imitation of a brain comprised of dense net of simple structures Origins: Algorithms that try to mimic the brain Very widely used in 80s and early 90s; popularity diminished in late 90s Recent resurgence: State-of-the-art technique for many applica1ons Comparison of computing power  Neural networks are designed to be massively parallel  The brain is effectively a billion times faster Applications of neural networks Medical Imaging Fake Videos Conceptual mathematical model  Receives input from sources  Computes weighted sum  Passes through an activation function  Sends the signal to m succeeding neurons Artificial Neural Network  Organized into layers of neurons  Typically or more: input, hidden and output  Neural networks are made up of nodes or units, connected by links  Each link has an associated weight and activation function Perceptron  Simplified (binary) artificial neuron Batch Perceptron Learning in NN: Backpropagation Cost Function Optimizing the Neural Network Forward Propagation Backpropagation Intuition Backpropagation Intuition Backpropagation Intuition Backpropagation Intuition Backpropagation Intuition Backpropagation: Gradient Computation Backpropagation Training  Training a Neural Network via Gradient Descent with Backpropagation Training a Neural Network Homework 1) Implement iris flower classification using neural network • Hint: - Using MLPClassifier from sklearn module https://www.python-course.eu/neural_networks_with_scikit.php - Keras model https://gist.github.com/NiharG15/cd8272c9639941cf8f481a7c4478d525 ...Contents  Introduction  Perceptron  Neural Network  Backpropagation Algorithm Introduction ❖ What are artificial neural networks?  A neuron receives a signal, processes it,... Sends the signal to m succeeding neurons Artificial Neural Network  Organized into layers of neurons  Typically or more: input, hidden and output  Neural networks are made up of nodes or units,... Comparison of computing power  Neural networks are designed to be massively parallel  The brain is effectively a billion times faster Applications of neural networks Medical Imaging Fake Videos