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Bài giảng Máy học nâng cao: Artificial neural network cung cấp cho người học các kiến thức: Introduction, perceptron, neural network, backpropagation algorithm. Mời các bạn cùng tham khảo nội dung chi tiết.
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 ... Network Architectures Example Image Recognition: classes ( one-hot encoding) Example Neural Network Classification Example: Perceptron - Representing Boolean Functions Example: Perceptron -. .. 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... 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,