Sequence Models

Sequence Models

This course is part of Deep Learning Specialization

Instructors: Andrew Ng

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4 assignments

Taught in English

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There are 4 modules in this course

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.

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,

What's included

12 videos5 readings1 assignment3 programming assignments

12 videosTotal 111 minutes

Why Sequence Models?2 minutesPreview module
Notation8 minutes
Recurrent Neural Network Model16 minutes
Backpropagation Through Time6 minutes
Different Types of RNNs9 minutes
Language Model and Sequence Generation12 minutes
Sampling Novel Sequences8 minutes
Vanishing Gradients with RNNs6 minutes
Gated Recurrent Unit (GRU)16 minutes
Long Short Term Memory (LSTM)9 minutes
Bidirectional RNN8 minutes
Deep RNNs5 minutes

5 readingsTotal 10 minutes

[IMPORTANT] Have questions, issues or ideas? Join our Forum!2 minutes
Clarifications about Upcoming Gated Recurrent Unit (GRU) Video1 minute
Clarifications about Upcoming Long Short Term Memory (LSTM) Video1 minute
Lecture Notes W11 minute
(Optional) Downloading your Notebook, Downloading your Workspace and Refreshing your Workspace5 minutes

1 assignmentTotal 50 minutes

Recurrent Neural Networks 50 minutes

3 programming assignmentsTotal 540 minutes

Building your Recurrent Neural Network - Step by Step180 minutes
Dinosaur Island-Character-Level Language Modeling 180 minutes
Jazz Improvisation with LSTM180 minutes

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.

What's included

10 videos2 readings1 assignment2 programming assignments

10 videosTotal 98 minutes

Word Representation10 minutesPreview module
Using Word Embeddings9 minutes
Properties of Word Embeddings11 minutes
Embedding Matrix3 minutes
Learning Word Embeddings10 minutes
Word2Vec12 minutes
Negative Sampling11 minutes
GloVe Word Vectors11 minutes
Sentiment Classification7 minutes
Debiasing Word Embeddings11 minutes

2 readingsTotal 2 minutes

Clarifications about Upcoming GloVe Word Vectors Video 1 minute
Lecture Notes W21 minute

1 assignmentTotal 50 minutes

Natural Language Processing & Word Embeddings 50 minutes

2 programming assignmentsTotal 360 minutes

Operations on Word Vectors - Debiasing180 minutes
Emojify180 minutes

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.