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Biswanath Halder
This course will teach you how to build a system for email auto-completion from scratch using Python and Keras. You'll learn the internal intricacies of LSTM networks and how they can be used to build systems for the task of text autocompletion.
Read more
This course will teach you how to build a system for email auto-completion from scratch using Python and Keras. You'll learn the internal intricacies of LSTM networks and how they can be used to build systems for the task of text autocompletion.
Read more
This course will teach you how to build a system for email auto-completion from scratch using Python and Keras. You'll learn the internal intricacies of LSTM networks and how they can be used to build systems for the task of text autocompletion.
Have you ever wondered how your favorite messaging app suggests possible next words when you are writing a message or how your email application suggests possible endings of the sentences when you are composing an email? All these are examples of text auto-completion systems which are data-driven systems that assist their users in writing texts. In this course, Implement Text Auto Completion with LSTM, you'll learn how to build an LSTM-based email auto-completion system from scratch using Python and Keras. First, you'll learn in detail how LSTM networks work. Next, You'll discover how LSTMs can be used to build network architectures for various natural language processing tasks and specifically, the task of sentence auto-completion. Finally, you'll explore an open-source email dataset and build a system for email auto-completion using LSTM networks. By the end of this course you’ll have an in-depth knowledge of text auto-completion systems and the capability of implementing one such system using Python and Keras.
Register for this course and see more details by visiting:
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What's inside
Syllabus
Course Overview
Long Short Term Memory Networks (LSTM)
Data Preparation for Assisted Smart Writing
Implementation of Auto-completion for Assisted Smart Writing
Read more
Syllabus
Course Overview
Long Short Term Memory Networks (LSTM)
Data Preparation for Assisted Smart Writing
Implementation of Auto-completion for Assisted Smart Writing
Read more
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides solid foundation for building text auto-completion systems using Python and Keras
Taught by Biswanath Halder, an experienced instructor in LSTM and text auto-completion systems
Emphasizes practical application through hands-on implementation of an LSTM-based email auto-completion system
Covers the fundamentals of LSTM networks and their suitability for text auto-completion tasks
Suitable for individuals interested in developing text auto-completion systems for various applications, such as messaging and email
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Implement Text Auto Completion with LSTM with these
activities:
Gather course materials in one location
Show steps
Organize course materials and notes to improve study efficiency and enhance comprehension.
Show steps
-
Create a dedicated folder or digital notebook.
-
Collect and store lecture notes, slides, and assignments.
-
Organize materials by topic or module.
Review LSTM models
Show steps
Review the basics of Long Short Term Memory models to ensure a solid foundation for the course.
Show steps
-
Read the online tutorial on LSTM models.
-
Complete the online coding exercises on LSTM models.
Read 'Natural Language Processing with Python'
Show steps
Supplemental reading on Natural Language Processing can deepen the understanding of techniques and algorithms, including LSTMs.
View
Natural Language Processing with Python:...
on Amazon
Show steps
-
Locate a copy of the book.
-
Read chapters related to LSTM models.
-
Complete exercises and examples.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Follow a tutorial on email auto-completion
Show steps
Find an online tutorial that guides you through building an email auto-completion system to reinforce course concepts.
Show steps
-
Search for a tutorial on building an email auto-completion system.
-
Follow the tutorial and build the project.
Practice writing code for LSTM networks using Keras
Show steps
Practice coding LSTM networks to strengthen your understanding and reinforce the concepts presented in the course.
Show steps
-
Set up a development environment with Python and Keras.
-
Implement basic LSTM network architectures.
-
Experiment with different hyperparameters to optimize network performance.
Implement an LSTM model in Python
Show steps
Practice implementing an LSTM model in Python to gain a deeper understanding of its architecture and functionality.
Show steps
-
Find a dataset suitable for LSTM training.
-
Build a simple LSTM model using Keras.
-
Train and evaluate the LSTM model.
Join a study group
Show steps
Engage with peers and discuss course material to improve understanding and identify areas needing additional support.
Show steps
-
Find or create a study group with other course participants.
-
Meet regularly to discuss the course material.
-
Collaborate on assignments and projects.
Build a simple email auto-completion system using LSTM networks
Show steps
Build a project to apply the concepts learned in the course to a practical scenario, reinforcing your understanding and developing practical skills.
Show steps
-
Gather a dataset of emails.
-
Preprocess the data and prepare it for training.
-
Implement an LSTM network for text auto-completion using Keras.
-
Evaluate the performance of your system.
Create a presentation on LSTM applications
Show steps
Demonstrate a deep understanding of LSTM applications by creating a presentation that explores different use cases and industry examples.
Show steps
-
Research and identify different applications of LSTM models.
-
Gather case studies and examples.
-
Design and deliver a presentation.
Gather course materials in one location
Show steps
Organize course materials and notes to improve study efficiency and enhance comprehension.
Show steps
Show steps
Show steps
- Create a dedicated folder or digital notebook.
- Collect and store lecture notes, slides, and assignments.
- Organize materials by topic or module.
Review LSTM models
Show steps
Review the basics of Long Short Term Memory models to ensure a solid foundation for the course.
Show steps
Show steps
Show steps
- Read the online tutorial on LSTM models.
- Complete the online coding exercises on LSTM models.
Read 'Natural Language Processing with Python'
Show steps
Supplemental reading on Natural Language Processing can deepen the understanding of techniques and algorithms, including LSTMs.
View
Natural Language Processing with Python:...
on Amazon
Show steps
Show steps
Show steps
- Locate a copy of the book.
- Read chapters related to LSTM models.
- Complete exercises and examples.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Follow a tutorial on email auto-completion
Show steps
Find an online tutorial that guides you through building an email auto-completion system to reinforce course concepts.
Show steps
Show steps
Show steps
- Search for a tutorial on building an email auto-completion system.
- Follow the tutorial and build the project.
Practice writing code for LSTM networks using Keras
Show steps
Practice coding LSTM networks to strengthen your understanding and reinforce the concepts presented in the course.
Show steps
Show steps
Show steps
- Set up a development environment with Python and Keras.
- Implement basic LSTM network architectures.
- Experiment with different hyperparameters to optimize network performance.
Implement an LSTM model in Python
Show steps
Practice implementing an LSTM model in Python to gain a deeper understanding of its architecture and functionality.
Show steps
Show steps
Show steps
- Find a dataset suitable for LSTM training.
- Build a simple LSTM model using Keras.
- Train and evaluate the LSTM model.
Join a study group
Show steps
Engage with peers and discuss course material to improve understanding and identify areas needing additional support.
Show steps
Show steps
Show steps
- Find or create a study group with other course participants.
- Meet regularly to discuss the course material.
- Collaborate on assignments and projects.
Build a simple email auto-completion system using LSTM networks
Show steps
Build a project to apply the concepts learned in the course to a practical scenario, reinforcing your understanding and developing practical skills.
Show steps
Show steps
Show steps
- Gather a dataset of emails.
- Preprocess the data and prepare it for training.
- Implement an LSTM network for text auto-completion using Keras.
- Evaluate the performance of your system.
Create a presentation on LSTM applications
Show steps
Demonstrate a deep understanding of LSTM applications by creating a presentation that explores different use cases and industry examples.
Show steps
Show steps
Show steps
- Research and identify different applications of LSTM models.
- Gather case studies and examples.
- Design and deliver a presentation.
Career center
Learners who complete Implement Text Auto Completion with LSTM will develop knowledge and skills
that may be useful to these careers:
Text Mining Analyst
Text Mining Analyst
A Text Mining Analyst uses natural language processing techniques to extract knowledge from text data. They often use machine learning and artificial intelligence to build systems that can identify patterns and trends in text data. This course may be useful to a Text Mining Analyst who wants to learn more about LSTM networks and how to use them to build a system for email auto-completion.
Machine Translation Engineer
Machine Translation Engineer
A Machine Translation Engineer designs, develops, and maintains machine translation systems. They often use machine learning and artificial intelligence to build systems that can translate text from one language to another. This course may be useful to a Machine Translation Engineer who wants to learn more about LSTM networks and how to use them to build a system for email auto-completion.
Information Retrieval Engineer
Information Retrieval Engineer
An Information Retrieval Engineer designs, develops, and maintains information retrieval systems. They often use machine learning and artificial intelligence to build systems that can search and retrieve information from large datasets. This course may be useful to an Information Retrieval Engineer who wants to learn more about LSTM networks and how to use them to build a system for email auto-completion.
Natural Language Processing Researcher
Natural Language Processing Researcher
A Natural Language Processing Researcher develops new techniques and algorithms for processing and understanding human language. They often use machine learning and artificial intelligence to build systems that can learn from data and make predictions. This course may be useful to an NLP Researcher who wants to learn more about LSTM networks and how to use them to build a system for email auto-completion.
Natural Language Generation Engineer
Natural Language Generation Engineer
A Natural Language Generation Engineer designs, develops, and maintains natural language generation systems. They often use machine learning and artificial intelligence to build systems that can generate human-like text. This course may be useful to a Natural Language Generation Engineer who wants to learn more about LSTM networks and how to use them to build a system for email auto-completion.
Conversational AI Engineer
Conversational AI Engineer
A Conversational AI Engineer designs, develops, and maintains conversational AI systems. They often use machine learning and artificial intelligence to build systems that can interact with humans in a natural way. This course would help a Conversational AI Engineer learn about LSTM networks and how to use them to build an autocompletion system.
Speech Recognition Engineer
Speech Recognition Engineer
A Speech Recognition Engineer designs, develops, and maintains speech recognition systems. They often use machine learning and artificial intelligence to build systems that can recognize and understand spoken language. This course may be useful to a Speech Recognition Engineer who wants to learn more about LSTM networks and how to use them to build a system for email auto-completion.
Artificial Intelligence Researcher
Artificial Intelligence Researcher
An Artificial Intelligence Researcher develops new techniques and algorithms for artificial intelligence. They often use machine learning and deep learning to build systems that can learn from data and make predictions. This course may be useful to an AI Researcher who wants to learn more about LSTM networks and how to use them to build a system for email auto-completion.
Computational Linguist
Computational Linguist
A Computational Linguist uses natural language processing techniques to analyze and understand human language. They often use machine learning and artificial intelligence to build systems that can communicate with humans in a natural way. This course would help a Computational Linguist learn about LSTM networks and how to use them to build an autocompletion system.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. They often use large datasets and complex algorithms to build systems that can learn from data and make predictions. This course may be useful to a Machine Learning Engineer who wants to learn more about LSTM networks and how to use them to build a system for email auto-completion.
Natural Language Processing Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer develops, deploys, and maintains natural language processing models. They often use machine learning and artificial intelligence techniques to build systems that can process and understand human language. This course may be useful to an NLP Engineer who wants to learn more about LSTM networks and how to use them to build a system for email auto-completion.
Data Scientist
Data Scientist
A Data Scientist uses scientific methods and techniques to extract knowledge from data. They often use statistics, machine learning, and artificial intelligence to build models that can predict future trends and outcomes. This course may be useful to a Data Scientist who wants to learn more about LSTM networks and how to use them to build a system for email auto-completion.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software systems. They often use programming languages and software development tools to build systems that meet specific needs. This course would help build a foundation for a Software Engineer who wants to specialize in natural language processing, NLP.
Computer Scientist
Computer Scientist
A Computer Scientist researches and develops new computer technologies. They often use theoretical and practical knowledge to build new systems and solve problems. This course would help build a foundation for a Computer Scientist who wants to specialize in LSTM networks or autocompletion systems.
For more career information including salaries, visit:
OpenCourser.com/course/fknj3m/implement
Reading list
We've selected 13 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Implement Text Auto Completion with LSTM.
Provides a comprehensive overview of deep learning with Python, including a chapter on RNNs and LSTMs. It valuable resource for anyone who wants to learn more about deep learning with Python.
Provides a comprehensive overview of machine learning with Python, including a chapter on RNNs and LSTMs. It valuable resource for anyone who wants to learn more about machine learning with Python.
Save
Provides a comprehensive overview of machine learning, including a chapter on RNNs and LSTMs. It valuable resource for anyone who wants to learn more about machine learning.
Save
Provides a comprehensive overview of deep learning, including a chapter on RNNs and LSTMs. It valuable resource for anyone who wants to learn more about deep learning.
Provides a comprehensive overview of deep learning for natural language processing (NLP), covering topics such as recurrent neural networks, convolutional neural networks, and transformers.
Provides a comprehensive overview of NLP with deep learning, including a chapter on RNNs and LSTMs. It valuable resource for anyone who wants to learn more about NLP with deep learning.
Provides a practical guide to using PyTorch for NLP tasks, including a chapter on RNNs and LSTMs. It great resource for anyone who wants to learn how to use PyTorch for NLP.
Provides a comprehensive overview of information retrieval, covering topics such as text preprocessing, indexing, and ranking.
Provides a comprehensive overview of statistical learning, covering topics such as linear regression, logistic regression, and decision trees.
Provides a comprehensive overview of pattern recognition and machine learning, covering topics such as supervised learning, unsupervised learning, and Bayesian methods.
Provides a comprehensive overview of natural language processing (NLP) techniques in Python, covering topics such as tokenization, stemming, lemmatization, parsing, and machine learning for NLP.
Provides a comprehensive overview of data mining techniques, covering topics such as data preprocessing, feature selection, classification, and clustering.
Provides a comprehensive overview of speech and language processing, covering topics such as phonetics, phonology, morphology, syntax, semantics, and pragmatics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/fknj3m/implement
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Time
94 hours
Level
Intermediate
Via
Pluralsight
Instructor
Biswanath Halder
Language
English
Good to know
Provides solid foundation for building text auto-completion systems using Python and Keras
Taught by Biswanath Halder, an experienced instructor in LSTM and text auto-completion systems
Emphasizes practical application through hands-on implementation of an LSTM-based email auto-completion system
Covers the fundamentals of LSTM networks and their suitability for text auto-completion tasks
Suitable for individuals interested in developing text auto-completion systems for various applications, such as messaging and email
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