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.
Recurrent Neural Networks
Discover recurrent neural networks, a type of model that performs extremely well on temporal data, and several of its variants, including LSTMs, GRUs and Bidirectional RNNs,
Natural Language Processing & Word Embeddings
Natural language processing with deep learning is a powerful combination. Using word vector representations and embedding layers, train recurrent neural networks with outstanding performance across a wide variety of applications, including sentiment analysis, named entity recognition and neural machine translation.
Sequence Models & Attention Mechanism
Augment your sequence models using an attention mechanism, an algorithm that helps your model decide where to focus its attention given a sequence of inputs. Then, explore speech recognition and how to deal with audio data.
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.
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.