| Sumario: | Nearly 4 Hours of Video Instruction 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. About the Instructor Jon Krohn is Chief Data Scientist at the machine learning company untapt. He presents a popular series of deep learning tutorials published by Addison-Wesley and is the author of the bestselling book Deep Learning Illustrated . Jon teaches his deep learning curriculum in-classroom at the New York City Data Science Academy, as well as guest lecturing at Columbia University and New York University. He holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading journals since 2010. 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. Course Requirements The author’s Deep Learning with TensorFlow, Keras, and PyTorch LiveLessons , or familiarity with the topics covered in Chapters 5 through 9 of his book Deep Learning Illustrated , are a prerequisite. Lesson Descriptions ...
|
|---|
