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$20 USD / 小时

zagazig,

egypt

$20 USD / 小时

目前这里是6:01 下午

十一月 5, 2015已加入

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Ahmed K.

@akh563b3ad5a03c8

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$20 USD / 小时

zagazig,

egypt

$20 USD / 小时

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Machine Learning Engineer

1+ professional experience Computer Engineering school student who started coding 9 years ago, AI scholarship winner from Bertelsmann, a former mentor at Google-Andela Android Development track. My specialties are: - Machine Learning - Deep Learning - Data Analysis - Computer Vision - Android Java Development - Embedded Systems Boards Technical Knowledge: - Programming languages: Python, Java, C, C++ - Libraries: PyTorch, Keras, Tensorflow, Sklearn, Numpy, Pandas - Deep Learning: CNNs, RNNs, LSTMs, GANs - Machine Learning algorithms for classification and clustering: Random Forest, SVM, k-means, etc... - Object Detection: Yolov5 - Hyperparameter optimizations: KerasTuner, Optuna, AutoKeras - Android Java - Raspberry Pis, Arduino, STM32, Tiva C - Linux - Git Check out my Github account for more details. [login to view URL]

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HCIA

Mastered data analysis through three projects:
- Project 1: IMDB dataset exploration
- Project 2: A/B testing
- Project 3: US Flights data analysis and communication

Advanced Data Analysis Nanodegree

Being one of the top scholars of Bertelsmann's scholarship of Artificial Intelligence, this course introduced deeply dived topics on FCNs, CNNs, RNNs, LSTMs, GANs.

Throughout this project, I've built:
- Predicting Bike-Sharing Data: builds an FCN for classification.
- Convolutional Neural Network (CNN): builds a custom CNN network for classification.
- Generate TV Scripts: builds an LSTM network, this project can generate two-people scripts.
- Face Generation: builds a GAN network, this project can generate human faces.

Deep Learning Nanodegree

This course introduced the concepts of ML algorithms whether they're supervised, unsupervised, semisupervised such as SVM, KNN, k-means, deep neural networks.

Throughout this course, I built three projects:
- Project 1 – Find Donors for CharityML: finds the best doners using supervised learning methods.
- Project 2 – Create an Image Classifier, this a script that can train DL networks using several architectures and transfer learning from RESNET18 & VGG16.
- Project 3 – Creating Customer Segments: Using clustering methods to segment customers.

Machine Learning Introduction - Nanodegree

One of the most widely know courses and introduced me to computer vision's practical applications such as classification, object detection and computer vision techniques.

Throughout this content-rich nanodegree, I managed to build three project:
- Project 1: Facial Keypoint Detection, using Haarcascade and CNNs, I managed to highlight facial points.
Project 2: Image Captioning, using an LSTM merged with a CNN network, this project can describe what images contain.
Project 3: Landmark Detection, applying SLAM algorithm.

Computer Vision Nanodegree

HCIA

Advanced Data Analysis Nanodegree

Deep Learning Nanodegree

Machine Learning Introduction - Nanodegree

Computer Vision Nanodegree

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经验

DevisionX

9月 2020 - 11月 2020 (2 个月, 1 天)

My roles were developing hyperparameter optimized classification models and object detection models for industrial datasets. - Worked on many Auto Machine Learning platforms such as Auto Keras, Keras Tuner, Optuna. - Used YOLOv5, and Detectron2 for object detection.