Welcome to the exercises section for our Advanced Deep Learning course! Here, you will find a variety of practical exercises to deepen your understanding of deep learning concepts.

Exercises Overview

Neural Network Architectures

Here are some popular neural network architectures you can explore:

  • Convolutional Neural Networks (CNNs): Great for image recognition tasks.
  • Recurrent Neural Networks (RNNs): Ideal for sequence data like time series or natural language.
  • Generative Adversarial Networks (GANs): Used for generating new data with similar statistics to real-world data.

CNNs

CNNs are particularly effective for image recognition tasks. They automatically and adaptively learn spatial hierarchies of features from input images.

CNN Architecture

For more information on CNNs, check out our CNN Tutorial.

Regularization Techniques

Regularization techniques are used to prevent overfitting in machine learning models.

  • L1 Regularization (Lasso): Adds a penalty equal to the absolute value of the magnitude of coefficients.
  • L2 Regularization (Ridge): Adds a penalty equal to the square of the magnitude of coefficients.

L1 Regularization

L1 regularization encourages sparsity in the weights, which can lead to simpler models.

L1 Regularization

To learn more about L1 regularization, visit our L1 Regularization Guide.

Optimization Algorithms

Optimization algorithms are used to minimize the loss function in machine learning models.

  • Stochastic Gradient Descent (SGD): Updates the model parameters using the gradient of the loss function with respect to the parameters.
  • Adam Optimizer: Combines the best properties of the AdaGrad and RMSprop optimization algorithms.

SGD

SGD is a popular optimization algorithm due to its simplicity and effectiveness.

SGD Optimization

For more details on SGD, read our SGD Optimization Guide.

Advanced Topics

Explore advanced topics in deep learning, such as:

  • Transfer Learning: Using pre-trained models on new tasks.
  • Autoencoders: Learning representations of data.
  • Reinforcement Learning: Learning to make decisions by taking actions in an environment.

Transfer Learning

Transfer learning allows you to leverage the knowledge gained from one task to improve performance on another related task.

Transfer Learning

To learn more about transfer learning, visit our Transfer Learning Tutorial.