Recurrent Neural Networks: Training and Challenges

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 2 of 4

Deep Learning: Convolutional Neural Networks and Training Techniques

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

Neural Networks Optimization

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

Deep Learning: Multilayer Perceptron and Training

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

Deep Learning: Convolutional Networks

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Language Models: Fixed-context and Recurrent Neural Networks

Discusses language models, focusing on fixed-context neural models and recurrent neural networks.

Deep Learning for NLP

Explores deep learning for NLP, covering word embeddings, context representations, learning techniques, and challenges like vanishing gradients and ethical considerations.

Feedforward Neural Networks: Activation Functions and Backpropagation

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

Neural Networks: Two Layers Neural Network

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

Recurrent Neural Networks: Neural Language Models

Explores fixed-context neural language models, RNNs, vanishing gradients, and sequence labeling in NLP.

Deep Learning: Convolutional Neural Networks

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