Deep Learning: Recurrent Neural Networks with Tensorflow 2

In this self-paced course, you will learn how to use Tensorflow 2 to build recurrent neural networks (RNNs). We'll study the Simple RNN (Elman unit), the GRU, and the LSTM. We'll investigate the capabilities of the different RNN units in terms of their ability to detect nonlinear relationships and long-term dependencies. We'll apply RNNs to both time series forecasting and natural language processing (NLP). We'll apply LSTMs to stock "price" predictions, but in a different way compared to most other resources. It will mostly be an investigation about what not to do, and how not to make the same mistakes that most blogs and courses make when predicting stocks. The course includes video presentations, coding lessons, hands-on exercises, and links to further resources.

This course is intended for:

• Anyone interested in deep learning and machine learning
• Anyone who wants to implement recurrent neural networks in Tensorflow 2

Suggested prerequisites:

• Decent Python programming skill
• Know how to build a feedforward ANN in Tensorflow 2
• Experience with data science libraries like Numpy and Matplotlib

In this course, we will cover:

• Simple RNNs (Elman unit)
• GRU (gated recurrent unit)
• LSTM (long short-term memory unit)
• time series forecasting
• stock price predictions and stock return predictions
• how to apply RNNs to natural language processing (NLP)