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Janani Ravi
Recurrent Neural Networks (RNN) performance and predictive abilities can be improved by using long memory cells such as the LSTM and the GRU cell. In this course, Language Modeling with Recurrent Neural Networks in Tensorflow, you will learn how RNNs are a natural fit for language modeling because of their inherent ability to store state. RNN performance and predictive abilities can be improved by using long memory cells such as the LSTM and the GRU cell. First, you will learn how to model OCR as a sequence labeling problem. Next, you will explore how you can architect an RNN to predict the next character based on past...
Read more
Recurrent Neural Networks (RNN) performance and predictive abilities can be improved by using long memory cells such as the LSTM and the GRU cell. In this course, Language Modeling with Recurrent Neural Networks in Tensorflow, you will learn how RNNs are a natural fit for language modeling because of their inherent ability to store state. RNN performance and predictive abilities can be improved by using long memory cells such as the LSTM and the GRU cell. First, you will learn how to model OCR as a sequence labeling problem. Next, you will explore how you can architect an RNN to predict the next character based on past...
Read more
Recurrent Neural Networks (RNN) performance and predictive abilities can be improved by using long memory cells such as the LSTM and the GRU cell. In this course, Language Modeling with Recurrent Neural Networks in Tensorflow, you will learn how RNNs are a natural fit for language modeling because of their inherent ability to store state. RNN performance and predictive abilities can be improved by using long memory cells such as the LSTM and the GRU cell. First, you will learn how to model OCR as a sequence labeling problem. Next, you will explore how you can architect an RNN to predict the next character based on past sequences. Finally, you will focus on understanding advanced functions that the TensorFlow library offers, such as bi-directional RNNs and the multi-RNN cell. By the end of this course, you will know how to apply and architect RNNs for use-cases such as image recognition, character prediction, and text generation; and you will be comfortable with using TensorFlow libraries for advanced functionality, such as the bidirectional RNN and the multi-RNN cell.
Register for this course and see more details by visiting:
OpenCourser.com/course/dg3s7s/language
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches RNNs, which are vital for NLP and related programming
Specifically focuses on RNNs for use with Tensorflow
Describes RNNs as useful for image recognition, character prediction, and text generation, which are very relevant today
Offers opportunities to apply advanced TensorFlow functionality, such as bidirectional RNNs and the multi-RNN cell
Taught by Janani Ravi, who is recognized for their work in RNNs and NLP
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Language Modeling with Recurrent Neural Networks in TensorFlow.
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Career center
Learners who complete Language Modeling with Recurrent Neural Networks in TensorFlow will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build, deploy, and manage machine learning systems. The skills learned in this course in language modeling with recurrent neural networks in TensorFlow will help you build a foundation for developing and deploying machine learning systems that can process and generate text data. This course will give you the skills you need to succeed as a Machine Learning Engineer, especially if you are interested in working with natural language processing or text-based applications.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers design and develop systems that can understand and generate human language. This course will introduce you to the fundamentals of natural language processing and provide you with the skills you need to build and deploy NLP systems. You will learn how to use recurrent neural networks to model language, and you will gain experience with advanced TensorFlow libraries for natural language processing.
Data Scientist
Data Scientist
Data Scientists use data to solve problems and make decisions. This course will help you build a foundation in data science and provide you with the skills you need to use recurrent neural networks for natural language processing tasks. You will learn how to use TensorFlow to build and train machine learning models, and you will gain experience with advanced data science techniques.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course will introduce you to the fundamentals of software engineering and provide you with the skills you need to build and deploy software systems. You will learn how to use recurrent neural networks to solve problems in natural language processing, and you will gain experience with advanced software engineering techniques.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including natural language processing. This course will introduce you to the fundamentals of research in natural language processing and provide you with the skills you need to conduct research in this field. You will learn how to use recurrent neural networks to model language, and you will gain experience with advanced research techniques.
Computational Linguist
Computational Linguist
Computational Linguists use computers to study language. This course will introduce you to the fundamentals of computational linguistics and provide you with the skills you need to use computers to study language. You will learn how to use recurrent neural networks to model language, and you will gain experience with advanced computational linguistics techniques.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course will introduce you to the fundamentals of product management and provide you with the skills you need to develop and launch new products. You will learn how to use recurrent neural networks to solve problems in natural language processing, and you will gain experience with advanced product management techniques.
Business Analyst
Business Analyst
Business Analysts use data to help businesses make decisions. This course will introduce you to the fundamentals of business analysis and provide you with the skills you need to use data to help businesses make decisions. You will learn how to use recurrent neural networks to solve problems in natural language processing, and you will gain experience with advanced business analysis techniques.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. This course will introduce you to the fundamentals of technical writing and provide you with the skills you need to create documentation for software and other technical products. You will learn how to use recurrent neural networks to solve problems in natural language processing, and you will gain experience with advanced technical writing techniques.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for developing and executing marketing campaigns. This course will introduce you to the fundamentals of marketing and provide you with the skills you need to develop and execute marketing campaigns. You will learn how to use recurrent neural networks to solve problems in natural language processing, and you will gain experience with advanced marketing techniques.
Sales Manager
Sales Manager
Sales Managers are responsible for leading and motivating sales teams. This course will introduce you to the fundamentals of sales and provide you with the skills you need to lead and motivate sales teams. You will learn how to use recurrent neural networks to solve problems in natural language processing, and you will gain experience with advanced sales techniques.
Customer Success Manager
Customer Success Manager
Customer Success Managers are responsible for ensuring that customers are satisfied with their products and services. This course will introduce you to the fundamentals of customer success and provide you with the skills you need to ensure that customers are satisfied with their products and services. You will learn how to use recurrent neural networks to solve problems in natural language processing, and you will gain experience with advanced customer success techniques.
Operations Manager
Operations Manager
Operations Managers are responsible for the day-to-day operations of a business. This course will introduce you to the fundamentals of operations management and provide you with the skills you need to manage the day-to-day operations of a business. You will learn how to use recurrent neural networks to solve problems in natural language processing, and you will gain experience with advanced operations management techniques.
Human Resources Manager
Human Resources Manager
Human Resources Managers are responsible for managing the human resources of a business. This course will introduce you to the fundamentals of human resources management and provide you with the skills you need to manage the human resources of a business. You will learn how to use recurrent neural networks to solve problems in natural language processing, and you will gain experience with advanced human resources management techniques.
Financial Analyst
Financial Analyst
Financial Analysts use data to make investment decisions. This course will introduce you to the fundamentals of financial analysis and provide you with the skills you need to use data to make investment decisions. You will learn how to use recurrent neural networks to solve problems in natural language processing, and you will gain experience with advanced financial analysis techniques.
For more career information including salaries, visit:
OpenCourser.com/course/dg3s7s/language
Reading list
We've selected 16 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
Language Modeling with Recurrent Neural Networks in TensorFlow.
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This comprehensive textbook provides a thorough foundation in deep learning and is often used as a reference by practitioners. It delves into the mathematical and theoretical underpinnings of deep learning models, providing a solid background for understanding the concepts covered in the course.
This comprehensive textbook introduces the fundamental concepts of speech and language processing, covering topics such as phonetics, phonology, morphology, syntax, semantics, and pragmatics. It provides a strong foundation for understanding the field and its applications.
Focuses specifically on natural language processing with Python and covers topics such as tokenization, stemming, parsing, and machine learning algorithms for NLP. It practical guide with hands-on exercises and examples.
This specialized book focuses specifically on recurrent neural networks, providing a comprehensive overview of their architecture, training methods, and applications. It valuable resource for gaining a deeper understanding of the concepts introduced in the course.
Provides an in-depth look at deep learning techniques for natural language processing, covering topics such as recurrent neural networks, convolutional neural networks, and transformer models. It explores the latest advancements and applications in the field.
Focuses specifically on TensorFlow, the open-source machine learning library, and provides a comprehensive guide to building and training deep learning models. It covers topics such as data preprocessing, model building, and evaluation.
While this book focuses on PyTorch rather than TensorFlow, it provides valuable insights into natural language processing (NLP) techniques, including language modeling and sequence prediction, which are covered in the course.
Provides a practical introduction to recurrent neural networks with Python and covers topics such as LSTM and GRU networks, sequence modeling, and applications in natural language processing and time series analysis.
Save
Provides a comprehensive overview of neural networks and deep learning, covering topics such as perceptrons, backpropagation, convolutional neural networks, and recurrent neural networks. It good resource for understanding the theoretical foundations of these techniques.
Provides a practical guide to machine learning concepts and algorithms, with a focus on intuitive explanations and real-world examples. It covers topics such as supervised learning, unsupervised learning, and deep learning.
Provides a comprehensive introduction to statistical learning, covering topics such as linear regression, logistic regression, decision trees, and support vector machines. It valuable resource for understanding the statistical foundations of machine learning.
Provides a practical guide to machine learning with Python libraries such as Scikit-Learn, Keras, and TensorFlow. It covers topics such as data preprocessing, model building, and evaluation.
Provides a comprehensive introduction to machine learning with R, covering topics such as supervised learning, unsupervised learning, and deep learning. It valuable resource for understanding the statistical foundations of machine learning.
Provides a gentler introduction to statistical learning than The Elements of Statistical Learning, covering topics such as linear regression, logistic regression, and decision trees. It good resource for beginners who want to get started with machine learning.
Provides a comprehensive introduction to machine learning from a probabilistic perspective, covering topics such as Bayesian inference, graphical models, and reinforcement learning. It good resource for understanding the theoretical foundations of machine learning.
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Provides a comprehensive introduction to pattern recognition and machine learning, covering topics such as supervised learning, unsupervised learning, and Bayesian inference. It good resource for understanding the theoretical foundations of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/dg3s7s/language
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Time
2 hours
Via
Pluralsight
Instructor
Janani Ravi
This course belongs to a
collection
called
Building Machine Learning Solutions with Tensorflow. It's comprised of 11 courses:
- TensorFlow 1: Getting Started
- Understanding the Foundations of TensorFlow
- Building Regression Models Using TensorFlow 1
- Building Classification Models with TensorFlow
- Building Unsupervised Learning Models with TensorFlow
- Debugging and Monitoring TensorFlow Programs
- Deploying TensorFlow Models to AWS, Azure, and the GCP
- Language Modeling with Recurrent Neural Networks in TensorFlow (viewing)
- Implementing Image Recognition Systems with TensorFlow 1
- Implementing Predictive Analytics with TensorFlow
- Sentiment Analysis with Recurrent Neural Networks in TensorFlow
Good to know
Teaches RNNs, which are vital for NLP and related programming
Specifically focuses on RNNs for use with Tensorflow
Describes RNNs as useful for image recognition, character prediction, and text generation, which are very relevant today
Offers opportunities to apply advanced TensorFlow functionality, such as bidirectional RNNs and the multi-RNN cell
Taught by Janani Ravi, who is recognized for their work in RNNs and NLP
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