filmov
tv
Lesson 8 - Deep Learning for Coders (2020)
Показать описание
We finish this course with a full lesson on natural language processing (NLP). Modern NLP depends heavily on *self-supervised learning*, and in particular the use of *language models*.
Pretrained language models are fine-tuned, in order to benefit from transfer learning. Unlike computer vision, fine-tuning in NLP can take advantage of an extra step, which is the use of self-supervised learning on the target dataset.
Before we can do any modeling with text data, we first have to tokenize and numericalize it. There are a number of approaches to tokenization, and which you choose will depend on your language and dataset.
NLP models use the same basic approach of *entity embedding* that we've seen before, except that for text data it's called `word embedding`. The method, however, is nearly identical.
NLP models have to handle documents of varying sizes, so they require a somewhat different architecture, such as a *recurrent neural network* (RNN). It turns out that an RNN is basically just a regular deep net, which has been refactored using a loop.
However, simple RNNs suffer from exploding gradients, so we have to use methods such as the LSTM cell to avoid this problem.
Finally, we look at some tricks to improve the results of our NLP models, such as additional regularization approaches, including various types of *dropout*, and activation regularization, as well as looking at weight tying.
1:36:55
Lesson 8 - Practical Deep Learning for Coders 2022
1:56:58
Lesson 8 - Deep Learning for Coders (2020)
2:06:41
Lesson 8 (2019) - Deep Learning from the Foundations
1:47:09
Lesson 8: Cutting Edge Deep Learning for Coders
1:33:46
Machine Learning 1: Lesson 8
0:33:38
Neural Network Architecture in Machine Learning | Lesson 8
1:56:58
Deep Learning for Coders | Full Course | lesson 8
0:57:55
DL with Python: Introduction to deep learning for computer vision (Chapter 8)
0:10:08
Hackathon 1: Series 1 - Use Case 8 | Uses and benefits of AI in Excel | CA Kedar Pande
0:09:48
ZappyAI Academy: Deep Learning with PyTorch, Lesson 8 - Recurrent Neural Networks (RNNs)
1:27:54
Deep Learning With PyTorch Bookclub/Tutorial Chapter 8 - Using convolutions to generalize
0:12:55
Lesson 8: Learn how to solve your first Deep Learning task
0:46:01
Deep Learning With Pytorch Lesson 8
0:14:07
NLP - Deep Learning tutorial for beginners using FastAI 2020 [Urdu ] - Lesson 8
0:14:12
LESSON 8: DEEP LEARNING MATHEMATICS: Comparing the Kinds of Matrices
2:35:24
Introduction to Deep Learning - Lesson 8
2:07:30
Lesson 2: Deep Learning 2018
0:04:30
#8 Machine Learning Specialization [Course 1, Week 1, Lesson 2]
0:18:40
But what is a neural network? | Chapter 1, Deep learning
1:34:17
TWiML x Fast.ai v3 Deep Learning Part 2 Study Group - Lesson 8 - Spring 2019 1080p
0:21:28
Deep Learning with Keras for novices at python(Lesson8)-Build CNN
0:44:43
Hands on Machine Learning - Chapter 8 - Dimensionality Reduction
0:02:55
AI and Machine Learning - Lesson 8 Part 1
2:01:15
Lesson 8: Deep Learning Part 2 2018 - Single object detection
Комментарии