<ul><li>Neurons in a Neural Network (NN) are variables that hold numeric values representing data inputs.</li><li>We adjust the importance of neurons through Weights and Biases, similar to knobs adjusted by a DJ.</li><li>Changing weights and biases influences the output of a neural network, which aims to minimize error.</li><li>Gradient Descent helps find the best set of weights by moving towards the minima of the error curve.</li><li>Stochastic Gradient Descent improves efficiency and helps avoid local minima in training neural networks.</li><li>Neural Network layers include Input, Hidden, and Output layers, with Hidden layers performing the core computations.</li><li>Softmax function converts NN outputs into probabilities, aiding in classification tasks.</li><li>Backpropagation adjusts weights and biases by comparing actual and expected outputs to increase accuracy.</li><li>Epoch in NN training refers to one cycle of forward and backward propagation to improve model accuracy.</li><li>Understanding neural networks and methodologies like backpropagation can lead to improved predictions and model accuracy.</li></ul>

What are Neural Networks? (All Basics Covered)

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