课程

Machine Translation with Keras

高级技能水平

更新时间 2024年11月

Are you curious about the inner workings of the models that are behind products like Google Translate?

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PythonArtificial Intelligence4 小时16 视频58 练习4,950 经验值4,987成就声明

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课程描述

The need to pack a bilingual dictionary for your European holiday or keeping one on your desk to complete your foreign language homework is a thing of the past. You just hop on the internet and make use of a language translation service to quickly understand what the street sign means or finding out how to greet and thank a foreigner in their language. Behind the language translation services are complex machine translation models. Have you ever wondered how these models work? This course will allow you to explore the inner workings of a machine translation model. You will use Keras, a powerful Python-based deep learning library, to implement a translation model. You will then train the model to perform an English to French translation, and you will be shown techniques to improve your model. At the end of this course, you would have developed an in-depth understanding of machine translation models and appreciate them even more!

先决条件

Introduction to Deep Learning with Keras

1

Introduction to Machine Translation

In this chapter, you'll understand what the encoder-decoder architecture is and how it is used for machine translation. You will also learn about Gated Recurrent Units (GRUs) and how they are used in the encoder-decoder architecture.

Introduction to machine translation

50 经验值

Understanding one-hot vectors

100 经验值

Part 1: Exploring the to_categorical() function

100 经验值

Part 2: Exploring the to_categorical() function

100 经验值

Encoder decoder architecture

50 经验值

Part 1: Text reversing model - Encoder

100 经验值

Part 2: Text reversing model - Encoder

100 经验值

Complete text reversing model

100 经验值

Understanding sequential models

50 经验值

Part 1: Understanding GRU models

100 经验值

Part 2: Understanding GRU models

100 经验值

Understanding sequential model output

100 经验值

2

Implementing an Encoder-Decoder Model with Keras

In this chapter, you will implement the encoder-decoder model with the Keras functional API. While doing so, you will learn several useful Keras layers such as RepeatVector and TimeDistributed layers.

Implementing the encoder

50 经验值

Part 1: Exploring the dataset

100 经验值

Part 2: Exploring the dataset

100 经验值

Defining the encoder

100 经验值

Implementing the decoder

50 经验值

Understanding the RepeatVector layer

100 经验值

The shape of a RepeatVector layer output

50 经验值

Defining the decoder

100 经验值

Dense and TimeDistributed layers

50 经验值

Part 1: Enter to win amazing prizes

100 经验值

Part 2: Let's play a few more games

100 经验值

Implementing the full encoder decoder model

50 经验值

Part 1: Defining the full model

100 经验值

Part 2: Defining the full model

100 经验值

3

Training and Generating Translations

In this chapter, you will train the previously defined model and then use a well-trained model to generate translations. You will see that our model does a good job when translating sentences.

Part 1: Preprocessing the Data

50 经验值

Tokenizing sentences with Keras

100 经验值

Controlling the vocabulary with the Tokenizer

100 经验值

Part 2: Preprocessing the Data

50 经验值

Adding special tokens

100 经验值

Padding sentences

100 经验值

Reversing sentences

100 经验值

Training the NMT model

50 经验值

Training the model

100 经验值

Splitting data to training and validation sets

100 经验值

Training the model with validation

100 经验值

Generating translations with the NMT

50 经验值

Part 1: Treasure hunt

100 经验值

Part 2: Treasure hunt

100 经验值

Generating English-French translations

100 经验值

4

Teacher Forcing and Word Embeddings

In this chapter, you will learn about a technique known as Teacher Forcing, which enables translation models to be trained better and faster. Then you will learn how you can use word embeddings to make the model even better.

Introduction to Teacher Forcing

50 经验值

Defining the Teacher Forcing model layers

100 经验值

Defining the Teacher Forcing model

100 经验值

Preprocessing data

100 经验值

Training the model with Teacher Forcing

50 经验值

Training the model

100 经验值

Splitting training and validation data

100 经验值

Training the model with validation

100 经验值

Generating translations from the model

50 经验值

Defining the decoder of the inference model

100 经验值

Link between the trained and inference model

100 经验值

Generating translations

100 经验值

Using word embedding for machine translation

50 经验值

Measuring word vector similarity

100 经验值

Defining the embedding model

100 经验值

Training the word embedding based model

100 经验值

Wrap-up and the final showdown

50 经验值

Machine Translation with Keras

课程完成

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