Artificial Intelligence #5- MLP Networks with Scikit Keras

Artificial Intelligence #5: MLP Networks with Scikit & Keras, available at $44.99, has an average rating of 4.05, with 22 lectures, based on 20 reviews, and has 1727 subscribers.

You will learn about 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. This course is ideal for individuals who are Anyone who wants to make the right choice when starting to learn Multilayer Perceptron Neural Network or Anyone who wants to learn Keras or Learners who want to work in data science and big data field or students who want to learn machine learning or Data analyser, Researcher, Engineers and Post Graduate Students need accurate and fast regression method. or Modelers, Statisticians, Analysts and Analytic Professional. It is particularly useful for Anyone who wants to make the right choice when starting to learn Multilayer Perceptron Neural Network or Anyone who wants to learn Keras or Learners who want to work in data science and big data field or students who want to learn machine learning or Data analyser, Researcher, Engineers and Post Graduate Students need accurate and fast regression method. or Modelers, Statisticians, Analysts and Analytic Professional.

Enroll now: Artificial Intelligence #5: MLP Networks with Scikit & Keras

Summary

Title: Artificial Intelligence #5: MLP Networks with Scikit & Keras

Price: $44.99

Average Rating: 4.05

Number of Lectures: 22

Number of Published Lectures: 22

Number of Curriculum Items: 22

Number of Published Curriculum Objects: 22

Original Price: ￡94.99

Quality Status: approved

Status: Live

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.

Who Should Attend

Anyone who wants to make the right choice when starting to learn Multilayer Perceptron Neural Network

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 need accurate and fast regression method.

Modelers, Statisticians, Analysts and Analytic Professional.

Target Audiences

Anyone who wants to make the right choice when starting to learn Multilayer Perceptron Neural Network

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 need accurate and fast regression method.

Modelers, Statisticians, Analysts and Analytic Professional.

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 Scikitlearn & 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:

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Once enrolled, you have?unlimited, lifetime access to the course!

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You will give you?my full support?regarding any issues or suggestions related to the course.

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Sobhan

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Required Softwares and Libraries

Chapter 2: Multilayer Perceptron Neural Networks Using Scikit Learn

Lecture 1: Neural Networks Theory

Lecture 2: Make MLP neural network to create Logic Gates

Lecture 3: Make MLP neural network to create Logic Gates Source Codes

Lecture 4: How to Write Your Valuable Review

Lecture 5: Using MLP to Detect Vehicles Precisely Part 1

Lecture 6: Using MLP to Detect Vehicles Precisely Part 1

Lecture 7: Using MLP to detect vehicles precisely Source Code

Lecture 8: Classify random data using Multilayer Perceptron Part 1

Lecture 9: Classify random data using Multilayer Perceptron Part 2

Lecture 10: Classify random data using Multilayer Perceptron Source Code

Chapter 3: Multilayer Perceptron Neural Networks Using Keras

Lecture 1: Using Keras to forecast 1000 data with 100 features in a few seconds Part 1

Lecture 2: Using Keras to forecast 1000 data with 100 features in a few seconds Part 2

Lecture 3: Using Keras to forecast 1000 data with 100 features in a few seconds Source Code

Lecture 4: Forecasting international airline passengers using keras Part1

Lecture 5: Forecasting international airline passengers using keras Part2

Lecture 6: Forecasting international airline passengers using keras Source Code

Lecture 7: Los Angeles Temperature Forecasting Part 1

Lecture 8: Los Angeles Temperature Forecasting Part 2

Lecture 9: Los Angeles Temperature Forecasting Part 3

Lecture 10: Los Angeles Temperature Forecasting Source code

Instructors

Sobhan N.
AI Developer|Electrical Engineer (PhD)|21,000+ Students

Rating Distribution

1 stars: 2 votes

2 stars: 0 votes

3 stars: 5 votes

4 stars: 3 votes

5 stars: 10 votes

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