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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
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.
Who this course is for:
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.
Requirements
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.
All you need is a decent PC/Laptop (2GHz CPU, 4GB RAM). You will get the rest from me.
Last updated 6/2018
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