Artificial Intelligence #5: MLP Networks with Scikit & Keras

Learn how to create Multilayer Perceptron Neural Network by using Scikit learn and Keras Libraries and Python

4.00 (18 reviews)
Udemy
platform
English
language
Data Science
category
instructor
Artificial Intelligence #5: MLP Networks with Scikit & Keras
1,724
students
2.5 hours
content
Oct 2019
last update
$44.99
regular price

What you will learn

Learn how Neural Networks work.

Learn how Gradient Descent trained a neural network.

Program Multilayer Perceptron Network from scratch in python.

Predict output of model easily and precisely.

Make program that able detect Bus and car.

Learn how to use MLPClassifier for their purposes.

Basic commands of Keras library to create Multilayer Perceptron Network.

Use power of neural networks to forecast temperature of Los Angeles.

Make forecasting model to estimate total airline passengers.

Description

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. 

For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the results to identify cats in other images. They do this without any prior knowledge about cats, e.g., that they have fur, tails, whiskers and cat-like faces. Instead, they automatically generate identifying characteristics from the learning material that they process.

An ANN is based on a collection of connected units or nodes called artificial neurons which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it.

In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called 'edges'. Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.

The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. ANNs have been used on a variety of tasks, including computer vision, speech recognition, machine translation, playing board and video games and medical diagnosis.

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.

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

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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


Content

Introduction

Introduction
Required Softwares and Libraries

Multilayer Perceptron Neural Networks Using Scikit Learn

Neural Networks Theory
Make MLP neural network to create Logic Gates
Make MLP neural network to create Logic Gates Source Codes
How to Write Your Valuable Review
Using MLP to Detect Vehicles Precisely Part 1
Using MLP to Detect Vehicles Precisely Part 1
Using MLP to detect vehicles precisely Source Code
Classify random data using Multilayer Perceptron Part 1
Classify random data using Multilayer Perceptron Part 2
Classify random data using Multilayer Perceptron Source Code

Multilayer Perceptron Neural Networks Using Keras

Using Keras to forecast 1000 data with 100 features in a few seconds Part 1
Using Keras to forecast 1000 data with 100 features in a few seconds Part 2
Using Keras to forecast 1000 data with 100 features in a few seconds Source Code
Forecasting international airline passengers using keras Part1
Forecasting international airline passengers using keras Part2
Forecasting international airline passengers using keras Source Code
Los Angeles Temperature Forecasting Part 1
Los Angeles Temperature Forecasting Part 2
Los Angeles Temperature Forecasting Part 3
Los Angeles Temperature Forecasting Source code

Reviews

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!
Vipul
July 1, 2018
Good work. Loved the way you explained. Clearly understood the concept you are trying to explain. Nice job buddy. Time and money worthy even to go through your video. Thanks. Notify me if you post any new tutorials.
Fred
June 27, 2018
I have enrolled to this course and really like this instructor courses because his courses are simple and practical. I highly recommend you to enroll.

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1764004
udemy ID
6/24/2018
course created date
7/3/2019
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