Deep Learning: Recurrent Neural Networks with Python

Preview this course

This course starts with the basics of Recurrent Neural Networks (RNNs) with Python and then teaches you how to build them by taking you through various exercises and projects. You will be able to test your skills by completing two exciting projects: creating an automatic book writer and a stock price prediction application.

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147 on-demand videos & exercises
Level: All Levels
English
15hrs 35mins
Access on mobile, web and TV

What to know about this course

With the exponential growth of user-generated data, there is a strong need to move beyond standard neural networks in order to perform tasks such as classification and prediction. Here, architectures such as RNNs, Gated Recurrent Units (GRUs), and Long Short Term Memory (LSTM) are the go-to options. Hence, for any deep learning engineer, mastering RNNs is a top priority. 

This course begins with the basics and will gradually equip you with not only the theoretical know-how but also the practical skills required to successfully build, train, and implement RNNs. This course contains several exercises on topics such as gradient descents in RNNs, GRUs, LSTM, and so on. This course also introduces you to implementing RNNs using TensorFlow.  The course culminates in two exciting and realistic projects: creating an automatic book writer and a stock price prediction application.

By the end of this course, you will be equipped with all the skills required to confidently use and implement RNNs in your applications.  The code bundle for this course is available at https://github.com/AISCIENCES/mastering_recurrent_neural_networks

Who's this course for?

As this course begins with the basics, no prior knowledge in RNNs is required. However, prior experience in Python would be beneficial. Whether you are a beginner, a seasoned data scientist looking to get started with RNNs, business analysts, or if you simply want to implement RNNs in your projects, this course is for you.

What you'll learn

  • Gain an overview of deep neural networks Understand the fundamentals of RNN architectures.
  • Train real-world datasets using different RNN architectures Implement RNNs, LSTM, and GRUs through hands-on exercises.
  • Create and compile RNN models in TensorFlow.
  • Perform text classification using RNNs and TensorFlow.

Key Features

  • Understand and apply fundamentals of recurrent neural networks.
  • Implement RNNs and related architectures on real-world datasets.
  • Train RNNs for real-world applications—automatic book writer and stock price prediction.

Course Curriculum

About the Author

AI Sciences

AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.  AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.  Their courses have successfully helped more than 100,000 students master AI and data science. AI Sciences are experts, PhDs, and artificial intelligence practitioners, including computer science, machine learning, and Statistics. Some work in big companies such as Amazon, Google, Facebook, Microsoft, KPMG, BCG, and IBM.  AI sciences produce a series of courses dedicated to beginners and newcomers on techniques and methods of machine learning, statistics, artificial intelligence, and data science. They aim to help those who wish to understand techniques more easily and start with less theory and less extended reading. Today, they publish more comprehensive courses on specific topics for wider audiences.  Their courses have successfully helped more than 100,000 students master AI and data science.