Create a Superhero Name Generator with TensorFlow

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提供方
在此免费指导项目中,您将:

Natural language generation with a deep learning model

Using tokenizer in TensorFlow

在面试中展现此实践经验

2 hours
中级
无需下载
分屏视频
英语(English)
仅限桌面

In this guided project, we are going to create a neural network and train it on a small dataset of superhero names to learn to generate similar names. The dataset has over 9000 names of superheroes, supervillains and other fictional characters from a number of different comic books, TV shows and movies. Text generation is a common natural language processing task. We will create a character level language model that will predict the next character for a given input sequence. In order to get a new predicted superhero name, we will need to give our model a seed input - this can be a single character or a sequence of characters, and the model will then generate the next character that it predicts should after the input sequence. This character is then added to the seed input to create a new input, which is then used again to generate the next character, and so on. You will need prior programming experience in Python. Some experience with TensorFlow is recommended. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, Recurrent Neural Networks, and optimization algorithms like gradient descent but want to understand how to use the TensorFlow to start performing natural language processing tasks like text classification or text generation. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

必备条件

Prior programming experience in Python. Conceptual understanding of Neural Networks. Prior experience with TensorFlow and Keras is recommended.

您要培养的技能

  • Natural Language Processing

  • Deep Learning

  • Machine Learning

  • Tensorflow

  • Natural Language Generation

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  1. Introduction

  2. Data and Tokenizer

  3. Names and Sequences

  4. Creating Examples

  5. Training and Validation Sets

  6. Creating the Model

  7. Training the Model

  8. Generating Names

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