Understanding Epochs, Batches, and Iterations
Learn the difference between epochs, batches, and iterations in neural network training. Understand batch size, how to choose it, and its impact on training.
Introduction to Gradient Descent Optimization
Learn gradient descent, the optimization algorithm that trains machine learning models. Understand batch, stochastic, and mini-batch variants with examples.
Backpropagation Explained: How Networks Learn
Understand backpropagation, the algorithm that enables neural networks to learn. Learn how it calculates gradients and updates weights with clear examples.
Forward Propagation: How Neural Networks Make Predictions
Learn how forward propagation works in neural networks, from input to output. Understand the step-by-step process with clear examples and calculations.
Activation Functions: Why Neural Networks Need Non-Linearity
Learn why activation functions are essential in neural networks, how they introduce non-linearity, and explore popular functions like ReLU, sigmoid, and tanh.
