PJ
4.0Reviewed Apr 3, 2019
The previous courses raised the bar and expectations. The assignments for Week 1 and Week 2 were a bit unclear. Lectures for Week 1 and Week 2 can be improved as well. Besides, this is a great course!
Sequence Models
In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more. By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career.
Status: Deep Learning
Status: Fine-tuning
IntermediateCourse37 hours
Featured reviews
SN
5.0Reviewed Mar 30, 2020
It's an exciting course to learn about sequence models. The assignment illustrate the concept via step by step to understand how to implement how to implement the models and process the input data.
PS
5.0Reviewed Jul 22, 2020
Such a nice instructor and very good course material to understand the basics of Deep learning. I really enjoyed this course , Thanks for making such online course for us. Once again a big thanks.
MI
5.0Reviewed Oct 15, 2019
This is one of the most comprehensive yet enjoyable courses in the whole specialization! There are several assignments of practical applications. Thanks for the time and effort put into this course.
CF
5.0Reviewed Feb 14, 2020
One of the best thing from this class is not only we can understand the concept of RNN, LSTM, etc, but also I also get the idea about how these technique can be used in many daily life applications
AM
5.0Reviewed Jun 30, 2019
The course is very good and has taught me the all the important concepts required to build a sequence model. The assignments are also very neatly and precisely designed for the real world application.
MK
5.0Reviewed Mar 13, 2024
Cant express how thankful I am to Andrew Ng, literally thought me from start to finish when my school didnt touch about it, learn a lot and decided to use my knowledge and apply to real world projects
CD
5.0Reviewed Sep 27, 2018
Great hands on instruction on how RNNs work and how they are used to solve real problems. It was particularly useful to use Conv1D, Bidirectional and Attention layers into RNNs and see how they work.
PG
5.0Reviewed Jan 25, 2019
This was a tough one. The specialization is well structured and slowly progresses in terms of complexity. Having worked on RNN, i thought I would ace the projects. Different story though at the end
GS
5.0Reviewed Apr 26, 2019
So many possibilities will be presented in front of you after this course. The only limit is the boundary of my imagination and creativity, that is how I feel now upon the completion of this course.
GA
5.0Reviewed Jul 14, 2021
the assignments were a really good format for someone who hasn't learned how to derive wrt multiple variables. It made sense to have the formulas provided to introduce a context for me: a developer.
NM
5.0Reviewed Feb 20, 2018
Hope can elaborate the backpropagation of RNN much more. BP through time is a bit tricky though we do not need to think about it during implementation using most of existing deep learning frameworks.