Об этом курсе: This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others. You will: - Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs. - Be able to apply sequence models to natural language problems, including text synthesis. - Be able to apply sequence models to audio applications, including speech recognition and music synthesis. This is the fifth and final course of the Deep Learning Specialization. deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content.
Для кого этот курс: - Learners that took course one, two, and four of the specialization. Course three is also recommended. - Anyone that already has a solid understanding of learning with neural networks, including convolutional networks, and wants to learn how to develop recurrent neural networks.
Преподаватели: Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
Преподаватели: Head Teaching Assistant - Kian Katanforoosh, Adjunct Lecturer at Stanford University, deeplearning.ai, Ecole Centrale Paris
Преподаватели: Teaching Assistant - Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University, deeplearning.ai
| Основные сведения | Course 5 of 5 in the Deep Learning Specialization |
| Уровень | Intermediate |
| Язык | English |
| Требования к аппаратным средствам | There is no hardware requirement |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
- Видео: Why sequence models
- Видео: Notation
- Видео: Recurrent Neural Network Model
- Видео: Backpropagation through time
- Видео: Different types of RNNs
- Видео: Language model and sequence generation
- Видео: Sampling novel sequences
- Видео: Vanishing gradients with RNNs
- Видео: Gated Recurrent Unit (GRU)
- Видео: Long Short Term Memory (LSTM)
- Видео: Bidirectional RNN
- Видео: Deep RNNs
- Notebook: Building a recurrent neural network - step by step
- Notebook: Dinosaur Island - Character-Level Language Modeling
- Notebook: Jazz improvisation with LSTM
- Видео: Word Representation
- Видео: Using word embeddings
- Видео: Properties of word embeddings
- Видео: Embedding matrix
- Видео: Learning word embeddings
- Видео: Word2Vec
- Видео: Negative Sampling
- Видео: GloVe word vectors
- Видео: Sentiment Classification
- Видео: Debiasing word embeddings
- Notebook: Operations on word vectors - Debiasing
- Notebook: Emojify
- Видео: Basic Models
- Видео: Picking the most likely sentence
- Видео: Beam Search
- Видео: Refinements to Beam Search
- Видео: Error analysis in beam search
- Видео: Bleu Score (optional)
- Видео: Attention Model Intuition
- Видео: Attention Model
- Видео: Speech recognition
- Видео: Trigger Word Detection
- Видео: Conclusion and thank you
- Notebook: Neural Machine Translation with Attention
- Notebook: Trigger word detection
Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.
Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.
Earn official recognition for your work, and share your success with friends, colleagues, and employers.
| Приобрести курс | |
|---|---|
| Получить доступ к материалам курса | Доступен |
| Получить доступ к оцениваемым материалам курса | Доступен |
| Получить итоговую оценку | Доступен |
| Earn a shareable Course Certificate | Доступен |
Excellent Course to get started with NLP
As the other courses in this series, this is definitely another great course, and explains to details the various sequence models. I gave it 4 stars because I believe it might need some improvements. Compared to the previous courses it felt a little rushed, and had too much new information and long programming exercises for a single week. Maybe it would have been better if it was 4 weeks instead of 3.
Great course !
Another great course by Andrew Ng! This course is part of the CS230 class currently being taught at Stanford University. Only reason for 4 rather than 5 stars is that at this stage (April 2018), there are few knowledgeable mentors and virtually no Instructors present in the Forum. Course provides little introduction to the syntax of Keras, which makes for some problems implementing models. Therefore one might spend a lot of time spinning one's wheels until finding a way forward.