In today's lecture, we'll explore autoencoders - a specialized neural network architecture that learns to compress data into a lower-dimensional representation and then reconstruct it. We'll examine how these self-supervised models work, their various architectures, and their practical applications in dimensionality reduction, denoising, and generative modeling.
In today's lecture, we'll explore essential techniques for efficiently training and evaluating neural networks, with a particular focus on understanding and interpreting learning curves. We'll examine how to diagnose common training issues, optimize the learning process, and develop intuition for model behavior through careful analysis of loss and accuracy metrics throughout the training cycle.
In today's lecture, we'll dive into multilayer perceptrons (MLPs), understanding how these more sophisticated neural networks overcome the limitations of single-layer models by utilizing hidden layers, backpropagation for learning, and their ability to solve complex non-linear problems that form the foundation of modern deep learning architectures.
In today's lecture, we'll explore the foundational journey of neural networks, starting with the McCulloch-Pitts neuron model and progressing through Rosenblatt's perceptron, examining their capabilities with logic gates, understanding the XOR problem limitation, and discovering how these early challenges shaped the development of more advanced network architectures.
The review of the liner regression models in the context of machine learning and the usage of gradient descent method.
The review of the course syllabus, grading policy, assignment schedule, and available resources to ensure everyone understands the expectations and logistics for our journey into machine learning and reproducible research.
In the second part of our first lecture, we will embark on an exciting journey into the world of machine learning (ML) and the critical importance of reproducible research.
In today's lecture, we'll dive into Convolutional Neural Networks (CNNs) and their foundational role in computer vision. We'll explore how these specialized architectures leverage spatial relationships in visual data, their core building blocks, and how they've revolutionized image processing tasks from classification to segmentation.