Deep Learning Fundamentals

Related lectures (32)

Covers the fundamentals of multilayer neural networks and deep learning.

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

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

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

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Discusses recurrent neural networks, their training challenges, and solutions like LSTMs and GRUs.

Explores neural network training, optimization, and environmental considerations, with insights into PCA and K-means clustering.

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

Covers key concepts in data mathematics, including automatic differentiation, linear layers, and attention layers.

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

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

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