zaghlol94 Goto Github PK

followers: 27.0 following: 33.0 repos: 131.0 gists: 1.0

Type: User

Company: Netconomy

Bio: Computer science major passionate about Computer Vision,NLP, AI and machine learning

Location: Paderborn, NRW, Germany

Hi there, it's ZAG 👋

Hello! I'm ZAG, a Data Scientist and Machine Learning Engineer with a passion for bringing cutting-edge research in the fields of Machine Learning and Deep Learning to life in the software industry. I hold a Master's degree in Data Science (Computer Science) from Paderborn University and have dedicated myself to the mission of integrating the latest theoretical advancements into practical, scalable, and reliable data project pipelines.

My expertise lies at the intersection of academic theory and real-world implementation. I thrive on turning state-of-the-art models into robust solutions that excel in the dynamic landscape of data projects. With an extensive background in both the theoretical foundations and hands-on application of Machine Learning and Deep Learning techniques, I bring a unique blend of knowledge and experience to the table.

Over the course of my career, which spans five years, I have successfully deployed ML/DL solutions across a wide array of domains. Whether it's computer vision, natural language processing, recommendation systems, or any other area, I have consistently delivered results that meet the demands of modern data-driven industries.

My mission is to make the complex world of Machine Learning and Deep Learning accessible and practical for businesses and projects of all sizes. I believe that the power of data should be harnessed to its fullest potential, and I am here to help you navigate the journey from theory to scalable, reliable solutions.

Explore my GitHub repositories and discover how I can assist you in transforming your data challenges into opportunities for success. Together, we can bridge the gap between cutting-edge research and the practical implementation that drives innovation in today's data-driven world.

🔭 Paper that I have implemented from scratch ...
- Yolo V3, Show, Attend and Tell: Neural Image Caption Generation with Visual Attention, Convolutional Sequence to Sequence Learning, Sequence to Sequence Learning with Neural Networks, Attention Is All You Need, Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation, Neural Machine Translation by Jointly Learning to Align and Translate, U-Net: Convolutional Networks for Biomedical Image Segmentation, A Neural Algorithm of Artistic Style, Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks(DCGAN),
- Improved Training of Wasserstein GANs (WGANGP), Wasserstein GAN (WGAN), Conditional Generative Adversarial Nets (cGAN), Image-to-Image Translation with Conditional Adversarial Networks (PIX2PIX), Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (CycleGAN), Progressive Growing of GANs for Improved Quality, Stability, and Variation (ProGAN), Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (SRGAN).
🌱 I’m currently learning ...
- Generative AI
- Audio Signal Processing
📫 How to reach me: ...
- Linkedin
- Twitter
- Facebook
- Mail: [email protected]
😄 Pronouns: ...
- He/Him/His

zaghlol94's Projects

deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

deep_white_balance

Reference code for the paper: Deep White-Balance Editing, CVPR 2020 (Oral). Our method is a deep learning multi-task framework for white-balance editing.

dimensionality-reduction

The main idea of principal component analysis (PCA) is to reduce the dimensionality of a data set consisting of many variables correlated with each other, either heavily or lightly, while retaining the variation present in the dataset, up to the maximum extent.

dl-visuals

Over 200 figures and diagrams of the most popular deep learning architectures and layers FREE TO USE in your blog posts, slides, presentations, or papers.

dlwpt-code

Code for the book Deep Learning with PyTorch by Eli Stevens and Luca Antiga.

dnn

Contains Deep Learning Code implemented on generic CPUs & Intel Xeon Phi Coprocessors

ds_datascience_challenge

egypt-population-visualization-with-tableau

Interactive data visualisation with tableau software demonstrate the proper population for the egyptian city

emergency-call-analysis

Data analysis using pandas and numpy to answer some important question and visualization about emergency call in US

extendedsumm

On Generating Extended Summaries of Long Documents

face_classification

Real-time face detection and emotion/gender classification using fer2013/imdb datasets with a keras CNN model and openCV.

fastseg

🚀 A PyTorch implementation of MobileNetV3 for real-time semantic segmentation, with state-of-the-art performance

few-shot

Repository for few-shot learning machine learning projects

fgvc

[ECCV 2020] Flow-edge Guided Video Completion

fiery

PyTorch code for the paper "FIERY: Future Instance Segmentation in Bird's-Eye view from Surround Monocular Cameras"

fraud-detection

Fraud detection in credit card application using Self-Organizing-Maps

gan-mnist

gfpgan

google-maket-stock-prediction-using-rnn

Google Market Stock Prediction using RNN "Keras+Tensorflow"

hufman-code

Hufman code is a losless data compression Algorithm ,The idea is to assign variable-length codes to input characters, lengths of the assigned codes are based on the frequencies of corresponding characters. The most frequent character gets the smallest code and the least frequent character gets the largest code.

zaghlol94 Goto Github PK

Hi there, it's ZAG 👋

zaghlol94's Projects

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