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.
Multi-Head Attention
Skills You'll Learn
Artificial Intelligence and Machine Learning (AI/ML), PyTorch (Machine Learning Library), Artificial Neural Networks, Deep Learning, Tensorflow, Natural Language Processing, Applied Machine Learning, Large Language Modeling
Reviews
4.8 (31,030 ratings)
- 5 stars83.76%
- 4 stars12.89%
- 3 stars2.57%
- 2 stars0.47%
- 1 star0.30%
AM
Jul 1, 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.
NM
Feb 21, 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.
From the lesson
Transformer Network
Taught By
Andrew Ng
Instructor
Kian Katanforoosh
Senior Curriculum Developer
Younes Bensouda Mourri
Curriculum developer