Deep Neural Networks and Splines

Related lectures (32)

Page 3 of 4

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

Explores the multilayer perceptron model, training, optimization, data preprocessing, activation functions, backpropagation, and regularization.

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.

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

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Covers the perceptron model and backpropagation algorithm in neural networks.

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Covers the basics of neural networks, including the perceptron model and backpropagation.

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.