samirlmch - Tumblr blog

samirlmch · 5 years ago

Text

K- MEANS INTRODUCTION

In this article, we will cover k-means clustering. In general, Clustering is defined as the grouping of data points such that the data points in a group will be similar or related to one another and different from the data points in another group. The goal of clustering is to determine the intrinsic grouping in a set of unlabelled data.

K- means is an unsupervised partitional clustering algorithm that is based on grouping data into k - numbers of clusters by determining centroid using the Euclidean or Manhattan method for distance calculation. It groups the object based on minimum distance.

Euclidean Distance

INTRODUCTION TO ARTIFICIAL INTELLIGENCE & MACHINE LEARNING https://aihubprojects.com/introduction-to-machine-learning/

ALGORITHM

First, initialize the number of clusters, K (Elbow method is generally used in selecting number of clusters )

Randomly select the k data points for centroid. A centroid is the imaginary or real location representing the center of the cluster.

Categorize each data items to its closest centroid and update the centroid coordinates calculating the average of items coordinates categorized in that group so far

Repeat the process for a number of iterations till successive iterations clusters data items into same group

Difference between Machine learning and Artificial Intelligence https://aihubprojects.com/difference-between-machine-learning-and-artificial-intelligence/

HOW IT WORKS ?

In the beginning, the algorithm chooses k centroids in the dataset randomly after shuffling the data. Then it calculates the distance of each point to each centroid using euclidean distance calculation method. Each centroid assigned represents screen recorder in python a cluster and the points are assigned to the closest cluster. At the end of the first iteration, the centroid values are recalculated, usually taking the arithmetic mean of all points in the cluster. In every iteration, new centroid values are calculated until successive iterations provide the same centroid value.

IS AI OVERHYPED? REALITY VS EXPECTATION https://aihubprojects.com/is-ai-overhyped-reality-vs-expectation/

#kmeans #aihubprojects

0 notes

samirlmch · 5 years ago

Text

Python Screen Recorder

In theory, an artificial general intelligence could carry out any task a human could, and likely many that a human couldn't.

AlphaGo's domination of the notoriously complex game of Go, can give the impression society is on the fast track to developing AGI.

Yet the systems in use today are generally rather one-note, excelling at a single task after extensive training, but useless for anything else

While machines may exhibit stellar performance on screen recorder a certain task, performance may degrade dramatically if the task is modified even slightly

Studying animal cognition and its neural implementation also has a vital role to play, as it can provide a window into various important aspects of higher-level general intelligence

YAn Lecun He believes the path towards general AI lies in developing systems that can build models of the world they can use to predict future outcomes. A good route to achieving this, he said in a talk last year, could be using generative adversarial networks (GANs)

Hard-coding morality into machines seems too immense a challenge, given the impossibility of predicting every situation a machine could find itself in. If a collision is unavoidable, should a self-driving car knock down someone in their sixties or a child? What if that child had a terminal illness? What if the person in their sixties were the sole carer of their partner?

once AI reaches human-level intelligence, it will rapidly improve itself through a bootstrapping process to reach levels of intelligence far exceeding those of any human. But in order to accomplish this self-improvement, AI systems will have to rewrite their own code. This level of introspection will require an AI system to understand the vast amounts of code that humans cobbled together, and identify novel methods for improving it.

CAN WE ACHIEVE AGI ?

Artificial general intelligence systems are designed with the human brain as their reference. Since we ourselves don’t have the comprehensive knowledge of our brains and its functioning, it is hard to model it and replicate it working. However, it is theoretically possible--that given infinite time and memory, any kind of problem can be solved algorithmically.

LINEAR REGRESSION FROM SCRATCH

We will build a linear regression model to predict the salary of a person on the basis of years of experience from scratch. You can download the dataset from the link given below. Let’s start with importing required libraries:

%matplotlib inline

import numpy as np

import matplotlib.pyplot as plt

import pandas as pd

We are using a dataset of 30 data items consisting of features like years of experience and salary. Let’s visualize the dataset first.

dataset = pd.read_csv('salaries.csv')

#Scatter Plot

X = dataset['Years of Experience']

Y = dataset['Salary']

plt.scatter(X,Y,color='blue')

plt.xlabel('Years of Experience')

plt.ylabel('Salary')

plt.title('Salary Prediction Curves')

plt.show()

CONCLUSION

We need to be able to measure how good our model is (accuracy). There are many methods to achieve this but we would implement Root mean squared error and coefficient of Determination (R² Score).

Try a Model with Different error metrics for Linear Regression like Mean Absolute Error, Root mean squared error.

Try an algorithm with a large data set, imbalanced & balanced dataset so that you can have all flavors of Regression.

AI COURSE

INTRODUCTION TO ARTIFICIAL INTELLIGENCE & MACHINE LEARNING https://aihubprojects.com/introduction-to-machine-learning/

Difference between Machine learning and Artificial Intelligence https://aihubprojects.com/difference-between-machine-learning-and-artificial-intelligence/

IS AI OVERHYPED? REALITY VS EXPECTATION https://aihubprojects.com/is-ai-overhyped-reality-vs-expectation/

BEGINNERS GUIDE TO MACHINE LEARNING https://aihubprojects.com/beginners-guide-to-machine-learning/

KEY TERMS USED IN MACHINE LEARNING https://aihubprojects.com/key-terms-used-in-machine-learning/

Performance Metrics in Machine Learning Classification Model https://aihubprojects.com/performance-metrics-in-machine-learning-model/

Performance Metrics: Regression Model https://aihubprojects.com/performance-metrics-regression-model/

LINEAR REGRESSION FROM SCRATCH https://aihubprojects.com/linear-regression-from-scratch/

LOGISTIC REGRESSION FROM SCRATCH https://aihubprojects.com/logistic-regression-from-scratch/

NAIVE BAYES ALGORITHM FROM SCRATCH https://aihubprojects.com/naive-bayes-algorithm-from-scratch/

DECISION TREE FROM SCRATCH https://aihubprojects.com/decision-tree-from-scratch/

RANDOM FOREST FROM SCRATCH PYTHON https://aihubprojects.com/random-forest-from-scratch-python/

#ai aiprojects screenrecorderpython

0 notes

samirlmch · 5 years ago

Text

AI-assisted Sudoku Solver

Video Case

When user chose to use video as input, the algorithm will act quite differently

For grids extraction, we use position of last grids to deduce if 2 grids in 2 pictures are actually the same

For grids solving, we wait to have meet the same extracted numeric grid twice, to be confident on digits extraction

It do not resolve a grid if it a grid is detected as same as an already solved grid

How to use

AI Sudoku

?

The best way to use my algorithm is to firstly clone the git repo where you want to. Then, open a terminal & go in the repository

cd /path/to/the/folder/

You can see the options by typing python3 sudoku_solver_main.py -h

You will need some libraries before running it

Numpy

OpenCV

Keras

Tensorflow

To Install : pip install <library_name>

CREATE YOUR SCREEN RECORDER

Easy way

Take your own picture/video of your process target and use the command

python3 sudoku_solver_main.py -i path/to/your/file

You also want to add -s to save the output result In both cases, the result will be display on your monitor during processing

If you want to use your webcam, you can choose the profile n°2 by adding -p 2 to your command. Nevertheless, the algorithm is too long to give a real-time pleasant experience (mainly cause of the Hough Transform)

Advanced way

The user can use his own Keras model to identify digits. For that use the parameter -mp path/to/your/model

ARTICLE

Beginners Guide To Natural Language Processing https://aihubprojects.com/beginners-guide-to-natural-language-processing/

WHY SMART HOME ? – IOT COMPANION https://aihubprojects.com/why-smart-home-iot-companion/

WHY SMART BULBS – IOT COMPANION https://aihubprojects.com/why-smart-bulbs-iot-companion/

Data Mining For Beginners : Gentle Introduction https://aihubprojects.com/data-mining-for-beginners-gentle-introduction/

Elon Musk to organize ‘party hackathon’ https://aihubprojects.com/elon-musk-organizes-party-hackathon/

Will you date an AI ? What about Relationship ?? https://aihubprojects.com/will-you-date-an-ai/

AMAZON HAS MADE MACHINE LEARNING COURSE PUBLIC https://aihubprojects.com/amazon-has-made-machine-learning-course-public/

0 notes

samirlmch · 5 years ago

Text

AIHUBPROJECTS.COM

Over the years, mankind has made great progress with technology. In today’s context the internet has been a great source of everything such as entertainment, work, classes communication and the list goes on. But there are consequences to them as our data is being compromised in the process. There is a problem of how secure our data Is and how it is being used. The traditional security system can no longer be used to detect the intrusion due to complex intrusion behavior. Data mining is the process of finding the important data from a large dataset which can be used with machine learning techniques to build an efficient model. In this project we will be using NSL KDD dataset. We will use various classifiers and compare them to analyze NSL KDD dataset and the classifier which will have

greatest accuracy will be considered the best.

Dataset Description

Various drawbacks of KDD CUP 99 which was the main cause to decrease in the

performance of various IDS [7] led to the invention of NSL KDD dataset. NSL KDD is the refined version and also called the successor of KDD CUP dataset. It consists of all the needed attributes from KDD CUP dataset. It is an open source data and can be downloaded easily [2]. The advantage of using this dataset is redundant record is removed and sufficient number of records is present for train and test data. It consists of 41 attributes which is classified under Nominal, Binary and Numeric 1.

One more attribute is added as class which is 42nd attribute. There are two types of class called Normal and Anomaly. Anomaly class can be further divided into DOS, PROBE, R2L and U2R. For experiment purpose only two classes are considered: Normal and Anomaly.

OBJECTIVE

To compare and analyze the accuracy of different algorithms for intrusion detection.