Mastering Recurrent Neural Networks with TensorFlow Online Course

Mastering Recurrent Neural Networks with TensorFlow Online Course

Recurrent Neural Networks (RNNs) are powerful deep learning architectures designed to process sequential data such as time series, text, speech, and video. Unlike traditional models, RNNs have a memory mechanism that allows them to leverage past inputs to improve predictions. In this compact course, you will learn to use TensorFlow 2 to build and train RNNs. We will cover key architectures including the Simple RNN (Elman unit), GRU, and LSTM, and compare their strengths in handling nonlinear relationships and long-term dependencies. You’ll apply RNNs to time series forecasting and NLP tasks, and explore a unique approach to using LSTMs for stock price predictions—focusing not only on what works but also on common pitfalls and mistakes to avoid. By the end of the course, you will be able to confidently build your own RNNs with TensorFlow 2 for real-world applications.

Who should take this Course?

The Mastering Recurrent Neural Networks with TensorFlow Online Course is ideal for data scientists, AI/ML engineers, software developers, and researchers who want to specialize in sequence modeling and time-series analysis. It is also valuable for students and professionals working in fields like natural language processing, speech recognition, and financial forecasting who wish to gain hands-on experience in building and deploying RNN models using TensorFlow.

What you will learn

• Learn about simple RNNs (Elman unit)
• Covers GRU (gated recurrent unit)
• Learn how to use LSTM (long short-term memory unit)
• Learn how to preform time series forecasting
• Learn how to predict stock price and stock return with LSTM
• Learn how to apply RNNs to NLP

Course Outline

Welcome

• Introduction
• Outline

Recurrent Neural Networks (RNNs), Time Series, and Sequence Data

• Sequence Data
• Forecasting
• Autoregressive Linear Model for Time Series Prediction
• Proof That the Linear Model Works
• Recurrent Neural Networks (Elman Unit Part 1)
• Recurrent Neural Networks (Elman Unit Part 2)
• RNN Code Preparation
• RNN for Time Series Prediction
• Paying Attention to Shapes
• GRU and LSTM (Part 1)
• GRU and LSTM (Part 2)
• A More Challenging Sequence
• Demo of the Long-Distance Problem
• RNN for Image Classification (Theory)
• RNN for Image Classification (Code)
• Stock Return Predictions Using LSTMs (Part 1)
• Stock Return Predictions Using LSTMs (Part 2)
• Stock Return Predictions Using LSTMs (Part 3)
• Other Ways to Forecast
• Suggestion Box

Natural Language Processing (NLP)

• Embeddings
• Code Preparation (NLP)
• Text Preprocessing
• Text Classification with LSTMs