Keras

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Information engineering
Machine learning
Artificial neural networks

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Related lectures (7)

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

NFNets: Removing BatchNorm for High-Performance Image Recognition

Explores NFNets as an alternative to BatchNorm in ResNets, achieving high performance on ImageNet.

Convolutional Neural Networks: Training Tricks

Explores convolutional neural networks for image classification, focusing on weight challenges, overfitting prevention strategies, and fine-tuning pre-trained models.

Deep Learning Fundamentals

Introduces the fundamentals of deep learning, covering neural networks, CNNs, special layers, weight initialization, data preprocessing, and regularization.

Batch Normalization: Why It Works

Explores the aim and process of batch normalization in deep neural networks, emphasizing its importance in stabilizing mean input and solving the vanishing gradient problem.

Tricks of the Trade in Deep Learning: Aims

Covers practical questions and aims in deep learning, including neuron types, network architecture, optimization, and weight initialization.

Deep Learning Techniques: Recurring Networks and LSTM Models

Discusses the implementation and optimization of recurring networks using LSTM models in deep learning.

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