Mastering Recurrent Neural Networks with TensorFlow Online Course
Mastering Recurrent Neural Networks with TensorFlow Online Course
This course teaches you how to use TensorFlow 2 to build Recurrent Neural Networks (RNNs) for sequential data processing, such as time series, text, and speech. You will explore different RNN units, including Simple RNNs (Elman unit), GRU, and LSTM, and understand their abilities to detect nonlinear relationships and handle long-term dependencies. The course covers applying RNNs for time series forecasting and Natural Language Processing (NLP), with a unique approach to stock price prediction. By the end, you'll be able to build your own RNNs using TensorFlow 2.
Who is this Course for?
This course is ideal for those interested in deep learning and machine learning, or anyone looking to implement recurrent neural networks (RNNs) in TensorFlow 2. A solid understanding of Python programming is required, along with experience in building feedforward ANNs in TensorFlow 2 and familiarity with data science libraries like NumPy and Matplotlib.
What you will learn
- Understand Simple RNNs (Elman unit)
- Explore GRU (Gated Recurrent Unit)
- Learn to use LSTM (Long Short-Term Memory)
- Perform time series forecasting
- Predict stock prices and returns with LSTM
- Apply RNNs to NLP
Course Table of Contents
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
