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Self-Supervised-Deep-Clustering
December 30, 2021 – December 30, 2021
Pytorch implementation of many self-supervised deep clustering methods
View ProjectTransformer-based-Self-supervised-Learning
December 18, 2021 – December 19, 2021
Implementation of several transformer-based networks which learn in a self-supervised manner.
View ProjectObject-Detection-Algorithms
December 16, 2021 – July 9, 2022
Pytorch implementation of seven state-of-the-art algorithms for object detection.
View ProjectAttention-Is-All-You-Need
November 30, 2021 – December 19, 2021
Implmenetation of self-attention, multi-head Attention and Transformer networks and testing on a toy dataset.
View ProjectTransformers-Nets
November 29, 2021 – November 29, 2021
Implmentation of several transformer-based architectures compared on a classification task.
View ProjectMasked_AEs
November 28, 2021 – November 28, 2021
Implementation of Masked-AutoEncoder architecture on Pytorch
View ProjectLearning-disentanglement-for-Sequential-Data
September 10, 2021 – September 11, 2021
Implementation of VAE-based methods to learn disentangled representations for sequential data, e.g. Videos and Speech.
View ProjectBackground-Subtraction-Unsupervised-Learning
December 7, 2020 – July 15, 2022
Background Subtraction for complex scenes such as intersections from surveillance cameras
View ProjectGenerative_Architectures
April 15, 2020 – July 9, 2022
Implementation of several GANs and Auto-encoders models
View ProjectPBAS
February 6, 2020 – February 6, 2020
Implementation of one of Background substraction algorithm on python and c++
View ProjectCultural Fit Analysis
The candidate's projects are heavily focused on deep learning, computer vision, and self-supervised learning, primarily using Python. While there is some C++ and Assembly experience, the overall project portfolio does not strongly align with a typical 'Embedded Systems Engineer' role which often requires more direct hardware interaction, real-time operating systems, low-level driver development, and specific embedded frameworks. The candidate's experience level is listed as 0, which suggests entry-level, but the projects demonstrate advanced technical capabilities in specific ML domains. This creates a mismatch for a senior embedded role without further evidence of embedded systems expertise.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. The candidate's profile primarily showcases technical project work.