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.
- 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.
Recurrent Neural Networks
-Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section.
Natural Language Processing & Word Embeddings
-Natural language processing with deep learning is an important combination. Using word vector representations and embedding layers you can train recurrent neural networks with outstanding performances in a wide variety of industries. Examples of applications are sentiment analysis, named entity recognition and machine translation.
Sequence models & Attention mechanism
-Sequence models can be augmented using an attention mechanism. This algorithm will help your model understand where it should focus its attention given a sequence of inputs. This week, you will also learn about speech recognition and how to deal with audio data.
Raivis Joksts completed this course, spending 6 hours a week on it and found the course difficulty to be medium.
This is the hardest course in the specialisation, and may take some extra effort. For practical assignments I recommend getting familiar with Keras syntax and workflow, as here there is little hand-holding here,. the focus is on actual model architecture and algorithms.
Anonymous completed this course.
Best RNN course out there. Great explanation, amazing practical examples and interesting quizes. Well prepared. Good to take earlier courses in the specialization.
Ronny De Winter completed this course, spending 5 hours a week on it and found the course difficulty to be medium.
Great final course of a world-class specialization on Deep Learning. Andrew Ng understands how to make difficult concepts understandable for a broad audience. The difficulty of the exercises builds up course after course, so ensure you built up your tensorflow/keras skills with the earlier courses or by other means.
If you get stuck in one of the exercises do not hesitate to go to the discussion forum, most probably somebody else had similar problems before and you can find worthy advice and save precious time.