Building Neural Networks - Neuron, Single Layer Perceptron, Multi Layer Perceptron [Lecture 5.2]

"What is a neuron and how does it work? From a single neuron to a layer of neurons to multiple layers of neurons." ___________________________________________ Subscribe the channel https://www.youtube.com/channel/UCgQlZ6kefvYeHDe__YkFluA/?sub_confirmation=1 ___________________________________________ Part 1: Why Neural Networks for Machine Learning? https://www.youtube.com/watch?v=NaYvohpr9No Part 2: Building Neural Networks - Neuron, Single Layer Perceptron, Multi Layer Perceptron https://www.youtube.com/watch?v=MqeGZcqkrn0 Part 3: Activation Function of Neural Networks - Step, Sigmoid, Tanh, ReLU, LeakyReLU, Softmax [ https://www.youtube.com/watch?v=rtinxohdo7Y Part 4: How Neural Networks Really Work - From Logistic to Piecewise Linear Regression https://www.youtube.com/watch?v=rtinxohdo7Y Part 5: Delta Rule for Neural Network Training as Basis for Backpropagation https://www.youtube.com/watch?v=wKbZkfuuQLw Part 6: Derive Backpropagation Algorithm for Neural Network Training https://www.youtube.com/watch?v=s1CFVeJHmQk Part 7: Gradient Based Training of Neural Networks https://www.youtube.com/watch?v=N8ZKqB19Vw4 ___________________________________________ In this section, we build neural networks starting from a signle neuron. The input variables are scaled with multiplicative weights. An added bias term leads to the linear in a neuron. This result is modified non-linearly by an activation function. For a sigmoid activation function, a signle neuron is equivalent to logistic regression. Placing neurons in parallel lead to a single layer perceptron (SLP). Stacking such layers of neurons together gives a multiple layer perceptron (MLP). The layers apart from the output layer are called hidden layers.The calculations in such neural networks can be written as matrix-vector multiplications. In this lecture, we consider fully connected feedforward neural networks without reconnecting neurons or shortcut connections.