ahmed-hassan97/regression-using-one-variable

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ahmed-hassan97/regression-using-one-variable

ahmed-hassan97

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regression using one variables

Data-Engineer-Nanodegree

implement All project in Data Engineer Nanodegree

Jupyter Notebook

FreeRtos_examples

FreeRtos_examples using stm32 and freertos on system workbensh ide

lcd_driver_4bit

lcd_driver_4bit using atmega16

Image-Data-Augmentation-with-Keras

Image Data Augmentation with Keras

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setup-Big-data-cluster

setup big data cluster on docker

Image_Captioning

Image_Captioning using coco dataset and pretrained model

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Udacity-Data-streaming-Nanodegree

Implement Data Streaming Project And Exercise in this nanodegree

-Advanced-Lane-line-Finding

Advanced Lane Detection Project which includes advanced image processing to detect lanes irrespective of the road texture, brightness, contrast, curves etc. Used Image warping and sliding window approach to find and plot the lane lines. Also determined the real curvature of the lane and vehicle position with respect to center

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ahmed-hassan

shell using operating system linux

multiplication-two-array

deploy_machine_learning_model

Deploy machine learning model using Streamlit

sentiment-analysis_using_nlp

sentiment-analysis-keras using nlp algorithms

dynamic-programming-fibonanci

pretrained_model-

Transfer learning using pretrained model(inception_v3)

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insertion-sort-algorithm

data-analysis-project

TV, Halftime Shows, and the Big Game dataset from datacamp

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classification-using-data-regulrization

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YOLO

using yolo liprary you can detect object with high accuracy by usind dnn which use in this liprary

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Investigate_TMDb_Movies

investigate movie dataset to analysis it using data analysis library

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build_neural_network

build_neural_network using Mathematical equations and numby library

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first_project_for_classification

project classify disease into ten ouput

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Amazon_SageMaker_project

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algorithm-code

bluetooth_driver

interface bluetooth_driver with atmega16A

Behavioral-Cloning-Project

Behavioral Cloning Project for Self-Driving Car Nano Degree . The project includes designing a neural network and then training the car on the road in unity simulator. The CNN learns and clones the driving behavior

traffic-light-recognition

heep-sort

gpio_driver

stack-with-struct

merge-sort

classification-using-keras

ai project built in how can neural network able to classifiy between images

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keras_example

mini project from deep learning specialization (andrew course from coursera)

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-ahmedhasRegression_using_multiple-variable

ahmedhasRegression_using_multiple-variable based on library in python using numpy and pandas and matplotlib

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quick-sort-

uart_driver-using-atmega16

uart_driver using avr_microcontroller

linear-search

linked-list

search_algorithm

search_algorithm using ai (dfs-bfs-ucs-gready-a*) in graph and tree

iot_project

iot project using dweet in java script and html and css

data_visulization

make data visulization using matplotlib and numby and pandas library using python

Jupyter Notebook

fruit_ninga-game

fruit ninga game using javafx

queue-with-struct

lcd_driver

lcd_driver_using 8bit and avr microcontroller

pretrained_model-in-keras-for-image-classification

this kernel is comparison between accuracy to pretrained model in keras

Jupyter Notebook

Machine-Translation-from-English-to-France

build a deep neural network that functions as part of an end-to-end machine translation pipeline. Your completed pipeline will accept English text as input and return the French translation

Natural-Language-Processing-in-TensorFlow

This repositories to load data in json and tokenize it to learn Rnn to complete pattern

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project_for_classify_emails

this project to classify email into (spam and good) email using support vector machine(svm) classify

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Traffic-Sign-Classifier

you will use what you've learned about deep neural networks and convolutional neural networks to classify traffic signs. You will train and validate a model so it can classify traffic sign images using the German Traffic Sign Dataset. After the model is trained, you will then try out your model on images of German traffic signs that you find on the web

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applying-neural_network-for-embeded-system

applying neural_network for arduino microcontroller using

classification_for_two_variables

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