Prerequisites: Familiarity with Python programming and basic machine learning principles (e.g., supervised learning, feature vectors, model training/evaluation) is recommended.

Level: Intermediate

What You'll Learn

• Neural Network Fundamentals

  Explain the structure and components of artificial neural networks, including neurons, layers, weights, and biases.

• Activation Functions

  Understand the purpose and characteristics of common activation functions like Sigmoid, Tanh, ReLU, and Leaky ReLU.

• Model Training

  Describe the process of training a neural network, including loss functions, gradient descent, and backpropagation.

• Optimization Algorithms

  Compare different optimization algorithms like SGD, Momentum, RMSprop, and Adam.

• Regularization Techniques

  Apply techniques like L1/L2 regularization and dropout to prevent overfitting.

• Building Feedforward Networks

  Implement and train basic feedforward neural networks using a common deep learning framework (e.g., TensorFlow/Keras or PyTorch).

• Introduction to CNNs and RNNs

  Recognize the basic structure and purpose of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).