Introduction to Deep Learning

• 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).

There are no prerequisite courses for this course.

There are no recommended next courses at the moment.