Deep Learning Paradigm

Related lectures (32)

Page 3 of 4

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Introduces feedforward neural networks, activation functions, and backpropagation for training, addressing challenges and powerful methods.

Explores the basics of neural networks, with a focus on deep neural networks and their architecture and training.

Explores machine learning for feature extraction, 3D vision, and neural networks in mobile robotics.

Delves into the trade-off between model complexity and risk, generalization bounds, and the dangers of overfitting complex function classes.

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

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

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

Explores bio-inspired learning with neural networks and genetic algorithms, covering structure, training, and practical applications.

Covers the No Free Lunch Theorem and the role of inductive bias in deep learning and reinforcement learning.