Recurrent Neural Networks are a type of deep learning architecture designed to process sequential data, such as time series, text, speech, and video. RNNs have a memory mechanism, which allows them to preserve information from past inputs and use it to inform their predictions.
TensorFlow 2 is a popular open-source software library for machine learning and deep learning. It provides a high-level API for building and training machine learning models, including RNNs.
In this compact course, you will learn how to use TensorFlow 2 to build RNNs. We will study the Simple RNN (Elman unit), the GRU, and the LSTM, followed by investigating the capabilities of the different RNN units in terms of their ability to detect nonlinear relationships and long-term dependencies. We will apply RNNs to both time series forecasting and NLP. Next, we will 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.
By the end of this course, you will be able to build your own build RNNs with TensorFlow 2.
publication date
2023-02-28
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
key features
Build your own RNNs with TensorFlow 2 * Explains RNNs, time series, and sequence data * Preform text preprocessing and text classification with LSTMs
approach
In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks. The course is well-balanced with theory that explains the RNN concepts and hands-on coding exercises for practical understanding. The course includes video presentations, coding lessons, hands-on exercises, and links to further resources.
audience
This course is designed for anyone interested in deep learning and machine learning or for anyone who wants to implement recurrent neural networks in TensorFlow 2. One must have decent Python programming skills, should know how to build a feedforward ANN in TensorFlow 2, and must have experience with data science libraries such as NumPy and Matplotlib.
short description
In this self-paced course, you will learn how to use TensorFlow 2 to build recurrent neural networks (RNNs). You will learn about sequence data, forecasting, Elman Unit, GRU, and LSTM. You will also learn how to work with image classification and how to get stock return predictions using LSTMs. We will also cover Natural Language Processing (NLP) and learn about text preprocessing and classification.
subtitle
Learn how to use TensorFlow 2 to build recurrent neural networks (RNNs)
keywords
Recurrent Neural Networks, TensorFlow 2, Machine Learning, Neural Networks, Data Science, Data Learning, Simple RNNs, Elman unit, GRU, gated recurrent unit, LSTM, long short-term memory unit, time series, forecasting, stock price predictions, stock return predictions, natural language processing (NLP)
Product ISBN
9781803242828
