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Natural Language Processing in TensorFlow

This course is a part of TensorFlow in Practice, a 4-course Specialization series from Coursera.

If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In Course 3 of the deeplearning.ai TensorFlow Specialization, you will build natural language processing systems using TensorFlow. You will learn to process text, including tokenizing and representing sentences as vectors, so that they can be input to a neural network. You’ll also learn to apply RNNs, GRUs, and LSTMs in TensorFlow. Finally, you’ll get to train an LSTM on existing text to create original poetry! The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.

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Rating Not enough ratings
Length 6 weeks
Effort 4 weeks of study, 4-5 hours/week
Starts Aug 14 (7 days ago)
Cost $49
From deeplearning.ai via Coursera
Instructor Laurence Moroney
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Software Development Machine Learning

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Coursera

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deeplearning.ai

Rating Not enough ratings
Length 6 weeks
Effort 4 weeks of study, 4-5 hours/week
Starts Aug 14 (7 days ago)
Cost $49
From deeplearning.ai via Coursera
Instructor Laurence Moroney
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Software Development Machine Learning

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