Machine Learning Basics

Related lectures (32)

Page 3 of 4

Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.

Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.

Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

Covers a SWOT analysis of Machine Learning and Artificial Intelligence, exploring strengths, weaknesses, opportunities, and threats in the field.

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.