Why sequence models

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Из курса от партнера deeplearning.ai

Sequence Models

14618 оценки

deeplearning.ai

Sequence Models

14618 оценки

Курс 5 из 5 — Specialization Deep Learning

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. You will: - 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.

Из урока

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.

Why sequence models2:59

Recurrent Neural Network Model16:31

Backpropagation through time6:10

Different types of RNNs9:33

Language model and sequence generation12:01

Sampling novel sequences8:38

Vanishing gradients with RNNs6:28

Gated Recurrent Unit (GRU)17:06

Long Short Term Memory (LSTM)9:53

Bidirectional RNN8:19

Познакомьтесь с преподавателями

Andrew Ng
CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
Head Teaching Assistant - Kian Katanforoosh
Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
Teaching Assistant - Younes Bensouda Mourri
Mathematical & Computational Sciences, Stanford University, deeplearning.ai
Computer Science

Welcome to this fifth course on deep learning.

In this course, you learn about sequence models,

one of the most exciting areas in deep learning.

Models like recurrent neural networks or RNNs have transformed speech recognition,

natural language processing and other areas.

And in this course, you learn how to build these models for yourself.

Let's start by looking at a few examples of where sequence models can be useful.

In speech recognition you are given

an input audio clip X and asked to map it to a text transcript Y.

Both the input and the output here are sequence data,

because X is an audio clip and so that plays out over time and Y,

the output, is a sequence of words.

So sequence models such as a recurrent neural networks and other variations,

you'll learn about in a little bit have been very useful for speech recognition.

Music generation is another example of a problem with sequence data.

In this case, only the output Y is a sequence,

the input can be the empty set,

or it can be a single integer,

maybe referring to the genre of music you want to

generate or maybe the first few notes of the piece of music you want.

But here X can be nothing or maybe just an integer and output Y is a sequence.

In sentiment classification the input X is a sequence,

so given the input phrase like,

"There is nothing to like in this movie" how many stars do you think this review will be?

Sequence models are also very useful for DNA sequence analysis.

So your DNA is represented via the four alphabets A, C, G,

and T. And so given a DNA sequence can you label

which part of this DNA sequence say corresponds to a protein.

In machine translation you are given an input sentence,

voulez-vou chante avec moi?

And you're asked to output the translation in a different language.

In video activity recognition you might be given

a sequence of video frames and asked to recognize the activity.

And in name entity recognition you might be given

a sentence and asked to identify the people in that sentence.

So all of these problems can be addressed as supervised learning with label data X,

Y as the training set.

But, as you can tell from this list of examples,

there are a lot of different types of sequence problems.

In some, both the input X and the output Y are sequences,

and in that case,

sometimes X and Y can have different lengths,

or in this example and this example,

X and Y have the same length.

And in some of these examples only either X or only the opposite Y is a sequence.

So in this course you learn about sequence models are applicable,

so all of these different settings.

So I hope this gives you a sense of the exciting set of

problems that sequence models might be able to help you to address.

With that let us go on to the next video where we start to define

the notation we use to define these sequence problems.

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