Deep Learning - Recurrent Neural Networks with TensorFlow
Master Recurrent Neural Networks (RNNs) with TensorFlow 2 for sequence data and time series analysis. Hands-on implementation.
Master Recurrent Neural Networks (RNNs) with TensorFlow 2 for sequence data and time series analysis. Hands-on implementation.
This comprehensive course focuses on Recurrent Neural Networks (RNNs) and their implementation using TensorFlow 2. Students learn to build and apply RNNs for sequence data processing, time series prediction, and natural language processing. The curriculum covers fundamental concepts and advanced architectures like GRUs and LSTMs. Through practical exercises, learners implement autoregressive models, handle stock return predictions, and develop text classification systems. The course combines theoretical knowledge with hands-on coding, making it ideal for those interested in applying RNNs to real-world problems.
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What you'll learn
Implement Recurrent Neural Networks using TensorFlow 2
Build autoregressive models for time series prediction
Develop GRU and LSTM models for complex sequences
Apply RNNs to stock market prediction and image classification
Create text classification systems using LSTMs
Master sequence data processing techniques
Skills you'll gain
This course includes:
244 Minutes PreRecorded video
1 assignment
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FullTime access
Shareable certificate
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There are 3 modules in this course
This course provides a comprehensive introduction to Recurrent Neural Networks (RNNs) and their implementation using TensorFlow 2. The curriculum covers fundamental concepts of RNNs, time series prediction, and sequence data processing. Students learn to implement various RNN architectures, including GRUs and LSTMs, for tasks like stock price prediction and text classification. The course emphasizes practical implementation through hands-on coding exercises, combining theoretical knowledge with real-world applications.
Welcome
Module 1 · 9 Minutes to complete
Recurrent Neural Networks (RNNs), Time Series, and Sequence Data
Module 2 · 3 Hours to complete
Natural Language Processing (NLP)
Module 3 · 1 Hours to complete
Fee Structure
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Instructor
Enhancing IT Education Through Expert-Led Learning
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