Artificial Intelligence #6 : LSTM Neural Networks with Keras

Learn how to create Recurrent Neural Network and LSTMs by using Keras Libraries and Python
Instructed by: Sobhan N. | Subject: Academics, Math & Science

Description

Do you like to learn how to forecast economic time series like stock price or indexes with high accuracy? Do you like to know how to predict weather data like temperature and wind speed with a few lines of codes? If you say Yes so read more ... Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. A recurrent neural network (RNN) is a class of artificial neural network where connections between nodes form a directed graph along a sequence. This allows it to exhibit temporal dynamic behavior for a time sequence. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs. In this course you learn how to build RNN and LSTM network in python and keras environment. I start with basic examples and move forward to more difficult examples. In the 1st section you'll learn how to use python and Keras to forecast google stock price . In the 2nd section you'll know how to use python and Keras to predict NASDAQ Index precisely. In the 3rd section you'll learn how to use python and Keras to forecast New York temperature with low error. In the 4th section you'll know how to use python and Keras to predict New York Wind speed accurately.

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Course Info

• 28 Lectures
• 2 Hours
• Language: English
• Subject: Academics, Math & Science
• Instructed by: Sobhan N.
• Platform: Udemy