Deep Learning – Recurrent Neural Networks with TensorFlow
English | Tutorial | Size: 914.5 MB
In this 4 hr course, you will delve into the fascinating world of recurrent neural networks (RNNs) using TensorFlow 2. Explore the various types of RNN architectures like Simple RNN, GRU, and LSTM, and apply them to real-world tasks such as time series forecasting and NLP projects.
What I will be able to do after this course
Understand and implement foundational RNN architectures like Simple RNN, GRU, and LSTM using TensorFlow 2.
Gain skills in processing and analyzing sequential data such as time series and text.
Perform practical projects in text classification and natural language processing (NLP) applications.
Apply advanced RNNs to forecast trends, including practical stock price prediction exercises.
Avoid common mistakes in RNN projects and optimize models effectively for better accuracy.
Course Instructor(s)
Lazy Programmer is an experienced deep learning educator with a passion for simplifying complex concepts into practical and actionable knowledge. With extensive experience in working with neural networks and TensorFlow, they bring a wealth of expertise in teaching RNNs effectively through hands-on, real-world examples. Learners appreciate the structured and approachable teaching style.
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