Deep Learning: Convolutional Neural Networks

Related lectures (32)

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

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

Explores the regularized cross-entropy risk in neural networks, covering training processes and challenges in deep networks.

Covers PyTorch tensor data structure and training a CNN to classify images.

Covers the fundamentals of multilayer neural networks and deep learning.

Explores the historical development and training of multi-layered perceptrons, emphasizing the backpropagation algorithm and feature design.

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

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

Explores data representations, histograms, neural networks, and deep learning concepts.

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

Introduces Convolutional Neural Networks (CNNs) for autonomous vehicles, covering architecture, applications, and regularization techniques.

Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.

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