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: 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: 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: 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
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| Приобрести курс | |
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| Получить доступ к материалам курса | Доступен |
| Получить доступ к оцениваемым материалам курса | Доступен |
| Получить итоговую оценку | Доступен |
| Earn a shareable Course Certificate | Доступен |
