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AI Academy #3: Learn Artificial Neural Networks from A-Z


👉 AI Academy #3: Learn Artificial Neural Networks from A-Z | Udemy NED 👈
the mentioned students used the so-called multilayer (deep) neural networks. This innovative approach has completely crushed competition. All three were immediately hired by Google, deep learning was created, and an unprecedented boom in artificial intelligence began. Today, I will explain how in-depth learning works very simply.
by Sobhan N.

What you'll learn
  • Learn how Neural Networks work.
  • Learn how Gradient Descent trained a neural network.
  • Program Multilayer Perceptron Network from scratch in python.
  • Learn how to use MLPClassifier for their purposes.
  • You'll know how recurrent neural networks work.
  • You'll learn how to create LSTM networks using python and Keras
  • You'll learn how to predict NASDAQ Index by using LSTMs.
  • You'll know how to forecast Google stock price with high accuracy
  • Classify datasets by using Support Vector Machine method
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.  

In this Course you learn multilayer perceptron (MLP) neural network by using Scikit learn & Keras  libraries and Python.You learn how to classify datasets by MLP Classifier to find the correct classes for them. Next you go further. You will learn how to forecast time series model by using neural network in Keras  environment.

Next you are going to 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 first section you learn how to use python and sklearn MLPclassifier to forecast output of different datasets. 

  • Logic Gates

  • Vehicles Datasets

  • Generated Datasets

In second section you can forecast output of different datasets using Keras library

  • Random datasets

  • Forecast International Airline passengers

  • Los Angeles temperature forecasting


Next you are going further and learn how to make Recurrent Neural Networks:

  • In the 3rd section you'll learn how to use python and Keras to forecast google stock price .  


  • In the 4th section you'll know how to use python and Keras to predict NASDAQ Index precisely.


  • In the 5th section you'll learn how to use python and Keras to forecast New York temperature with low error. 


  • In the 6th section you'll know how to use python and Keras to predict New York Wind speed accurately.





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