Yoav Raytsfeld

Background

About Yoav Raytsfeld

I'm an MSc in Machine Learning and Data Science student. As a deep learning engineer, I specialized in developing computer vision solutions for various applications. I'm highly motivated, a team player, and a fast learner with excellent communication and problem-solving skills. Deep Learning Engineer, Neuronic, 2021 - 2023-* Developed customized end to end computer vision solutions in Python to meet client's requirements.* Specialized in developing and deploying deep learning models for various tasks like classification, object detection, segmentation, and tracking. Used PyTorch, TensorFlow, Nvidia-TAO, and TensorRT, combining image processing with OpenCV skills.* Experience working with Ubuntu environments, such as dGpu, Jetson IoT devices, and EC2 service on AWS. Utilizing Docker containers for efficient deployment and managing projects on GitHub.* Designed video stream pipelines using the GStreamer package and DeepStream elements."C.U."- Final Project, Afeka (2021)-* Implemented in Python using PyTorch, Sklearn, and OpenCV * Developed a semi-supervised emotion recognition algorithm based on the OpenAI CLIP model.* Evaluate the effects of multi-sentence inputs and extend the FER-based class with synonyms. Computational Learning Seminar (2021)-* Team leader, worked in three members group and made two lectures an hour each on deep learning methodology and how CNN works.* Our lectures were based on The Plant Pathology 2021 dataset and demonstrated a multi-label and multi-class classification problem with an imbalanced dataset. “Wheat Heads Detection”, Kaggle (2019)-* competed in an object detection competition to detect wheat heads in different shapes, colors, and scales.* Trained a variety of models, ensembled them for the final detection, and apply TTA for better results.* Achieved the Top 20%(433rd place out of 2,245 competitors).