Gain a foundational understanding of neural network concepts and implementation. This course covers the essential components of neural networks, including neuron structure, activation functions, network architecture, and the training process involving forward and backward propagation. Learn how to prepare data, calculate loss, apply gradient descent, and build simple networks using common techniques.
Prerequisites Basic Python and Math
Level:
Neural Network Architecture
Describe the components of a neural network, including neurons, layers, weights, and biases.
Activation Functions
Explain the purpose and characteristics of common activation functions like Sigmoid, Tanh, and ReLU.
Data Preparation
Understand techniques for preparing data for neural network training, including scaling and encoding.
Forward Propagation
Implement the forward pass calculation to generate predictions from a neural network.
Backpropagation
Explain the process of backpropagation for calculating gradients.
Gradient Descent
Apply gradient descent and its variants to update network weights and minimize loss.
Model Training
Implement a basic neural network training loop.
Regularization
Understand common techniques like L1/L2 regularization and dropout to prevent overfitting.
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