tailieunhanh - Bài giảng Máy học nâng cao: Artificial neural network - Trịnh Tấn Đạt

Bài giảng "Máy học nâng cao: Artificial neural network" cung cấp cho người học các kiến thức: Introduction, perceptron, neural network, backpropagation algorithm. | Bài giảng Máy học nâng cao Artificial neural network - Trịnh Tấn Đạt Trịnh Tấn Đạt Khoa CNTT Đại Học Sài Gòn Email trinhtandat@ Website https 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 3 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 Perceptron Simplified binary artificial neuron with weights Perceptron Simplified binary artificial neuron no weights Perceptron Simplified binary artificial neuron add weights Perceptron Simplified binary artificial neuron add weights Introducing Bias Perceptron needs to take into account the bias o Bias is just like an intercept added in a linear equation. o It is an additional parameter in the Neural Network which is used to adjust the output along with the weighted sum of the inputs to the neuron. o Bias acts like a constant which helps the model to fit the given data Sigmoid Neuron The more .