Deep Learning for Natural Language Processing LiveLessons, 2nd Edition

Deep Learning for Natural Language Processing LiveLessons, 2nd Edition
本教程是使用深度学习开发自然语言模型的入门教程。教程使用生活中的例子和实验Jupyter的实操,直观地讲解基础性理论。案例主要是Python和Keras,用于TensorFlow 2的高阶API,这是最流行的深度学习类库。在教程的前期,会讲到对自然语言数据的处理,包括如何将自然语言转换为可被机器学习方便处理的数字。在后面的教程中,会通过自然语言数据讲到深度学习架构被用于预测。 主要内容:预处理用于机器学习应用的自然语言;使用word2vec将自然语言转换为数字符号;使用经过自然训练的深度学习模型做预测;使用Keras、用于Tensorflow 2的高阶API应用最新的NLP;通过选择合适的模型架构和调试模型超参数改善深度学习模型性能。
Deep Learning for Natural Language Processing LiveLessons, 2nd Edition
ISBN: 0136620043 | .MP4, AVC, 1280×720, 30 fps | English, AAC, 2 Ch | 4h 59m | 10 GB
Instructor: Jon Krohn

An intuitive introduction to processing natural language data with TensorFlow-Keras deep learning models.

Overview

Deep Learning for Natural Language Processing LiveLessons, Second Edition, is an introduction to building natural language models with deep learning. These lessons bring intuitive explanations of essential theory to life with interactive, hands-on Jupyter notebook demos. Examples feature Python and Keras, the high-level API for TensorFlow 2, the most popular Deep Learning library. In early lessons, specifics of working with natural language data are covered, including how to convert natural language into numerical representations that can be readily processed by machine learning approaches. In later lessons, state-of-the art Deep Learning architectures are leveraged to make predictions with natural language data.

Skill Level

Intermediate

Learn How To

Preprocess natural language data for use in machine learning applications
Transform natural language into numerical representations with word2vec
Make predictions with Deep Learning models trained on natural language
Apply state-of-the-art NLP approaches with Keras, the high-level API for TensorFlow 2
Improve Deep Learning model performance by selecting appropriate model architectures and tuning model hyperparameters

Who Should Take This Course

These LiveLessons are perfectly suited to software engineers, data scientists, analysts, and statisticians with an interest in applying Deep Learning to natural language data. Code examples are provided in Python, so familiarity with it or another object-oriented programming language would be helpful.

Deep Learning for Natural Language Processing LiveLessons, 2nd Edition
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