L19.1 Sequence Generation with Word and Character RNNs Published 2021-04-29 Download video MP4 360p Recommendations 16:37 Recurrent Neural Networks (RNNs), Clearly Explained!!! 16:09 L19.4.2 Self-Attention and Scaled Dot-Product Attention 19:40 L8.8 Softmax Regression Derivatives for Gradient Descent 09:34 L15.4 Backpropagation Through Time Overview 22:19 L19.3 RNNs with an Attention Mechanism 16:11 L19.4.1 Using Attention Without the RNN -- A Basic Form of Self-Attention 21:02 The Attention Mechanism in Large Language Models 29:07 L15.6 RNNs for Classification: A Many-to-One Word RNN 22:36 L19.5.1 The Transformer Architecture 14:04 Battery Energy Revolution. What now? 16:08 L16.3 Convolutional Autoencoders & Transposed Convolutions 09:27 L17.2 Sampling from a Variational Autoencoder 07:35 L17.3 The Log-Var Trick 13:05 Transformer Neural Networks - EXPLAINED! (Attention is all you need) Similar videos 27:40 Lecture 32: Text Generation with RNNs 07:45 RNN #2: Notation | Sentence and Word Representation | Sequence Models 03:05 L19.0 RNNs & Transformers for Sequence-to-Sequence Modeling -- Lecture Overview 25:57 L19.2.2 Implementing a Character RNN in PyTorch --Code Example 1:15:59 MIT 6.S094: Recurrent Neural Networks for Steering Through Time 19:47 24. Recurrent Neural Networks 1:13:27 Lecture 12: Recurrent Networks 09:29 Recurrent Neural Networks - Embeddings 06:22 RNN - Based Language Model 57:42 Recurrent neural network (part 1): Basic RNN 13:40 L15.2 Sequence Modeling with RNNs 28:35 17- Recurrent Neural Networks Explained Easily 02:55 Deep Learning: Long Short-Term Memory Networks (LSTMs) More results