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Keep shoppingArtificial Intelligence #6 : LSTM Neural Networks with Keras
1,620 students
Artificial Intelligence #6 : LSTM Neural Networks with Keras
Learn how to create Recurrent Neural Network and LSTMs by using Keras Libraries and Python
Created bySobhan N.
Last updated 11/2018
English
What you'll learn
- You'll know how recurrent neural networks work.
- You'll know how to make simple neural network in Keras environment.
- You'll learn how to create LSTM networks using python and Keras
- You'll know how to increase accuracy and decrease error of recurrent neural networks
- You'll know how to forecast google stock price with high accuracy
- You'll learn how to use power of neural networks to forecast temperature of New York.
- You'll learn how to predict NASDAQ Index by using LSTMs.
- You'll know how to use power of neural networks to forecast wind speed of New York.
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Course content
6 sections • 27 lectures • 2h 3m total length
Requirements
- All you need is a decent PC/Laptop (2GHz CPU, 4GB RAM). You will get the rest from me.
- You should know about basic statistics
- You must know basic python programming
- Install Sublime and required library for python
- You should have a great desire to learn programming and do it in a hands-on fashion, without having to watch countless lectures filled with slides and theory.
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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Important information before you enroll:
In case you find the course useless for your career, don't forget you are covered by a 30 day money back guarantee, full refund, no questions asked!
Once enrolled, you have unlimited, lifetime access to the course!
You will have instant and free access to any updates I'll add to the course.
You will give you my full support regarding any issues or suggestions related to the course.
Check out the curriculum and FREE PREVIEW lectures for a quick insight.
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Click the "Take This Course" button at the top right now!
...Don't waste time! Every second of every day is valuable...
I can't wait to see you in the course!
Best Regrads,
Sobhan
Who this course is for:
- Anyone who wants to learn Recurrent Neural Networks and LSTMs
- Anyone who want to forecast stock market time series.
- Anyone who wants to learn Keras
- Learners who want to work in data science and big data field
- students who want to learn machine learning
- Data analyser, Researcher, Engineers and Post Graduate Students