About this course: 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.
Who is this class for: - 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.
Taught by: Andrew Ng, Co-founder, Coursera; Adjunct Professor, Stanford University; formerly head of Baidu AI Group/Google Brain
Taught by: Head Teaching Assistant - Kian Katanforoosh, Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
Taught by: Teaching Assistant - Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University, deeplearning.ai
| Basic Info | Course 5 of 5 in the Deep Learning Specialization |
| Level | Intermediate |
| Language | English, Subtitles: Chinese (Simplified) |
| Hardware Req | There is no hardware requirement |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
- Video: Notation
- Video: Recurrent Neural Network Model
- Video: Backpropagation through time
- Video: Different types of RNNs
- Video: Language model and sequence generation
- Video: Sampling novel sequences
- Video: Vanishing gradients with RNNs
- Video: Gated Recurrent Unit (GRU)
- Video: Long Short Term Memory (LSTM)
- Video: Bidirectional RNN
- Video: Deep RNNs
- Notebook: Building a recurrent neural network - step by step
- Notebook: Dinosaur Island - Character-Level Language Modeling
- Notebook: Jazz improvisation with LSTM
- Video: Using word embeddings
- Video: Properties of word embeddings
- Video: Embedding matrix
- Video: Learning word embeddings
- Video: Word2Vec
- Video: Negative Sampling
- Video: GloVe word vectors
- Video: Sentiment Classification
- Video: Debiasing word embeddings
- Notebook: Operations on word vectors - Debiasing
- Notebook: Emojify
- Video: Picking the most likely sentence
- Video: Beam Search
- Video: Refinements to Beam Search
- Video: Error analysis in beam search
- Video: Bleu Score (optional)
- Video: Attention Model Intuition
- Video: Attention Model
- Video: Speech recognition
- Video: Trigger Word Detection
- Video: 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.
| Purchase Course | |
|---|---|
| Access to course materials | Available |
| Access to graded materials | Available |
| Receive a final grade | Available |
| Earn a shareable Course Certificate | Available |
Well structured. Full of good and interesting content. Very enjoyable.
Andrew老师讲的很好，环环相扣，每个问题的原理讲的也很清楚，让我学到了很多知识，谢谢老师！
Best course to study deep learning!
Course content is really good! However, I found it quite difficult to truly understand deep learning algorithms. However, it provides good glimpse of of sequence models and intuitions behind various useful models.