Artificial Intelligence #6 : LSTM Neural Networks with Keras

Learn how to create Recurrent Neural Network and LSTMs by using Keras Libraries and Python

2.50 (105 reviews)

Udemy

platform

English

language

Data Science

What you will 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.

Why take this course?

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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It's time to take Action!

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

Reviews

Ali

March 18, 2022

Mala calidad de audio de video, el conocimiento que comparte es muy basico y es el mismo codigo y misma dinamica para todas las secciones, solo cambia el data set, y el pronostico a futuro que realiza es muy malo

Steve

May 25, 2021

Great, well explained content. 100% worth a watch for beginners wanting to learn more about building LSTMs

Nikhil

November 20, 2020

There are lots of things to be explained in codes like the function of Dense(), Sequential(), why LSTM requires 3D data, etc.

Pablo

June 21, 2020

Monothematic. It does not give any introduction for the algorithm used. I chose this course because I wanted to learn how to make REAL predictions with stock prices using LSTM. Worst course I've ever taken.

Tharindu

July 13, 2019

This course is amazing and above my expectations! Very good exercises, good speed, well communicated. The instructor made me feel very comfortable and was able to take many things away. Excellent content and very knowledgeable instructor!

Tushar

June 13, 2019

I need some explanation of the syntax statting why exactly we are using such code. Similar codes are available online but they lack an explanation. Kindly add the explanation of the code in the beginning. That will be very helpful.

Stavan

June 9, 2019

Your explanation was vague at many places and you didn't explained thoroughly each codes. I liked the examples on LSTM but I am still hardly able to understand the code.

Paul

April 19, 2019

Covers how to build a sequential LSTM network and how to train it. Shows how to preprocess the data using scipy. Seems to repeat this for 3 data sets. Quite a slow drawn out process, however perhaps that is good for total beginners. Perhaps a shorter course covering the same material would be better.

Fahad

February 9, 2019

Dear Dr. Sobhan, Thank you for the great course, really I learned a lot of things from it. I hope you will give more courses of LSTM and Neural Network. Thank you.

Cristiana

January 31, 2019

Ripasso di keras e concetti ripetuti su come presentare i dati alla rete, qualche imprecisione e nessun riferimento al concetto di stato e contesto ne alle loro impostazioni nel training (stateful). Detto ciò è un ripasso del codice :)

Marcel

December 29, 2018

some code lines are not really explained why they must exist for the code to work. the datastructures are of as much importance as understanding what the neural network does internally. the input datastructures to the network must be explained in more detail. For me the tempo is good and the quality of the example taken also good. I have seen my questions asked by others, and answered by you, in the comments. And they were really answered very extensively and well, therefore I give you 1 star more.

Jose

December 18, 2018

Axel, First of all thank you. I'm not new in machine learning but yes in Tensorflow and Keras. I would like to suggest you can add an introduction explaining briefly Keras' API, for a better understanding. The code is clean and works as expected, this is a hands on training where it is supposed the student has the experience required. One additional chapter could be "how to improve" all the previous examples and the last in particular (wind) where changing epochs and LSTM's parameters is not enough. José Valerio

Amir

November 24, 2018

The instructor has very poor language and very poor knowledge. He does not explain the material behind the scene.

Joshua

October 10, 2018

This course is boring and doesn’t do a good job of conveying the information. I received this course for free, and I still want my money back! The only reason this course is rated so highly is that the instructor tells students to not leave anything other than a 5 Star review in his course. He is clearly inflating his ratings. Buyer be warned...

Fred

October 1, 2018

Great course I really like way of teaching. Completely learn the basic concept you are teaching . Nice job.

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