关于此课程： 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.
此课程适用人群： - 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.
教学方： Andrew Ng, CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
教学方： Head Teaching Assistant - Kian Katanforoosh, Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
教学方： Teaching Assistant - Younes Bensouda Mourri, Mathematical & Computational Sciences, Stanford University, deeplearning.ai
| 基本信息 | 课程 5（共 5 门，Deep Learning Specialization ） |
| 级别 | 中级 |
| 语言 | 英语（English）, 字幕： 中文（简体） |
| 硬件要求 | There is no hardware requirement |
| 如何通过 | 通过所有计分作业以完成课程。 |
| 用户评分 |
- 视频: Notation
- 视频: Recurrent Neural Network Model
- 视频: Backpropagation through time
- 视频: Different types of RNNs
- 视频: Language model and sequence generation
- 视频: Sampling novel sequences
- 视频: Vanishing gradients with RNNs
- 视频: Gated Recurrent Unit (GRU)
- 视频: Long Short Term Memory (LSTM)
- 视频: Bidirectional RNN
- 视频: Deep RNNs
- Notebook: Building a recurrent neural network - step by step
- Notebook: Dinosaur Island - Character-Level Language Modeling
- Notebook: Jazz improvisation with LSTM
- 视频: Using word embeddings
- 视频: Properties of word embeddings
- 视频: Embedding matrix
- 视频: Learning word embeddings
- 视频: Word2Vec
- 视频: Negative Sampling
- 视频: GloVe word vectors
- 视频: Sentiment Classification
- 视频: Debiasing word embeddings
- Notebook: Operations on word vectors - Debiasing
- Notebook: Emojify
- 视频: Picking the most likely sentence
- 视频: Beam Search
- 视频: Refinements to Beam Search
- 视频: Error analysis in beam search
- 视频: Bleu Score (optional)
- 视频: Attention Model Intuition
- 视频: Attention Model
- 视频: Speech recognition
- 视频: Trigger Word Detection
- 视频: Conclusion and thank you
- Notebook: Neural Machine Translation with Attention
- Notebook: Trigger word detection
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The materials in this course were fantastic, and the problems were extremely interesting. There are still some typos and glitches in the programming assignments, but I'm sure these will disappear in time.
Good
Wonderful course! WONDERFUL Specialization!
刘
Finally, I finished all the courses of the specialization within 4 weeks! Great courses, and I will recommend everyone I met who wants to learn ML and DL to go and see this series of courses. Thank Andrew Ng and course assistant as lot!