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curso

Machine Translation with Keras

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Actualizado 01/2025

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

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PythonArtificial Intelligence4 horas16 vídeos58 exercícios4,950 XP4,326Declaração de Realização

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Descrição do curso

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!

Pré-requisitos

Introduction to Deep Learning with Keras

1

Introduction to Machine Translation

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Introduction to machine translation

Understanding one-hot vectors

Part 1: Exploring the to_categorical() function

Part 2: Exploring the to_categorical() function

Encoder decoder architecture

Part 1: Text reversing model - Encoder

Part 2: Text reversing model - Encoder

Complete text reversing model

Understanding sequential models

Part 1: Understanding GRU models

Part 2: Understanding GRU models

Understanding sequential model output

2

Implementing an Encoder-Decoder Model with Keras

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Implementing the encoder

Part 1: Exploring the dataset

Part 2: Exploring the dataset

Defining the encoder

Implementing the decoder

Understanding the RepeatVector layer

The shape of a RepeatVector layer output

Defining the decoder

Dense and TimeDistributed layers

Part 1: Enter to win amazing prizes

Part 2: Let's play a few more games

Implementing the full encoder decoder model

Part 1: Defining the full model

Part 2: Defining the full model

3

Training and Generating Translations

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Part 1: Preprocessing the Data

Tokenizing sentences with Keras

Controlling the vocabulary with the Tokenizer

Part 2: Preprocessing the Data

Adding special tokens

Padding sentences

Reversing sentences

Training the NMT model

Training the model

Splitting data to training and validation sets

Training the model with validation

Generating translations with the NMT

Part 1: Treasure hunt

Part 2: Treasure hunt

Generating English-French translations

4

Teacher Forcing and Word Embeddings

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Introduction to Teacher Forcing

Defining the Teacher Forcing model layers

Defining the Teacher Forcing model

Preprocessing data

Training the model with Teacher Forcing

Training the model

Splitting training and validation data

Training the model with validation

Generating translations from the model

Defining the decoder of the inference model

Link between the trained and inference model

Generating translations

Using word embedding for machine translation

Measuring word vector similarity

Defining the embedding model

Training the word embedding based model

Wrap-up and the final showdown

Machine Translation with Keras

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