Neural Network Basics

Summary

Layers of connected 'neurons' that transform input data through weighted sums and activation functions — the foundational building block behind all modern deep learning.

How to Learn This

1Build a simple neural network from scratch (or near-scratch) to understand the forward pass.
2Learn the role of weights, biases, and layers in transforming input to output.
3Diagram a small network and trace how one data point flows through it.

Dimensionality Reduction (t-SNE, UMAP Overview)

Activation Functions

More topics in Deep Learning Fundamentals

Activation Functions Backpropagation & Gradient Descent Building Networks With TensorFlow / Keras Building Networks With PyTorch Convolutional Neural Networks (CNNs)Recurrent Neural Networks (RNNs) & LSTMs Transfer Learning Regularization in Deep Learning (Dropout, Batch Norm)GPUs & Training Basics

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