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05/29/2022 08:11 AM

Natural Language Understanding with Python, Neural Networks-TensorFlow and ROS Integration

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(223 ratings)
  • access_time 6 hours
  • trending_up Intermediate
  • label Artificial Intelligence

This course comes with a certification

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About This Course

Skills you'll learn from this course

  • done_all Creating a dataset of application-specific expected outcomes for Natural Language Understanding
  • done_all Training a TensorFlow-Keras based model for Natural Language Understanding
  • done_all Matching text inputs with expected intents with Natural Language Understanding
  • done_all Model accuracy/performance calculations and optimization methods
  • done_all Model modularization for integration with Robot Operating System

Join "Natural Language Understanding with Python, Neural Networks-TensorFlow and ROS Integration" on Robociti to improve your Speech Processing skills and learn how to create a Natural Language Understanding (NLU) module that can act as the final stage in a Speech Recognition pipeline on a live or pre-recorded audio stream, matching recognized words or sentences to a set of expected outcomes for use in a specific application. To achieve this, popular frameworks like Python programming for dataset creation, TensorFlow and Keras for Neural Network model construction and fast deployment, and ROS (Robot Operating System) for further integration with other modules in larger AI & Robotics projects are utilized. Firstly, the process of creating a dataset of the expected outcomes based on a specific application is presented, along with any data processing required. Then, the method for using the dataset constructed to train a suggested Neural Network Model based on TensorFlow and Keras is analyzed, along with the way to calculate the accuracy of the model produced and tips about how to optimize the training process. Finally, the module is wrapped in the ROS framework for the integration in a larger Speech Recognition application and students work on improving the final result.

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Natural Language Understanding with Python, Neural Networks-TensorFlow and ROS Integration Certificate

Course Features

  • check_circle Programming Environment
  • check_circle Jupyter Notebook
  • check_circle Forum & Support

Course Chapters

Dataset Creation

  • Robot Management
  • WorkSpace Setup
  • NLP introduction
  • Dataset generation
  • Dataset preparation
  • Running inferences
  • Ros integration
  • Course Completion
Requirements
Students are required to have basic knowledge of Python programming and Machine Learning concepts, as well as basic understanding of Speech Processing. Robociti courses such as "Introduction to Speech Recognition with Neural Networks", "Introduction to Machine Learning" and "Python Basics for AI & Robotics I & II" are considered as prerequisites for this course. Additionally, since this module can be part of a bigger Speech Recognition pipeline, students should also study the "Hot Word Detection with Python, Neural Networks-TensorFlow Lite and ROS Integration" and "Automatic Speech Recognition with DeepSpeech, Python and ROS Integration" Robociti courses before taking on this NLU module.

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Natural Language Understanding with Python, Neural Networks-TensorFlow and ROS Integration Certificate

Courses Contributor

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Robociti Team

Course Reviews

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Robociti is the go-to platform to learn robotics from scratch. Its teaching methodology equips you with knowledge that is relevant, with lesson progressions that build foundation upon foundation.

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