Computer Vision 3D 2D Bildverarbeitung C++ Python KI Deep Learning Embedded Entwickler Mathematiker
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- 0 Referenzen
- 90‐120€/Stunde
- 13055 Lichtenberg
- auf Anfrage
- de | en
- 26.06.2023
Kurzvorstellung
Qualifikationen
Projekt‐ & Berufserfahrung
5/2023 – 7/2023
Tätigkeitsbeschreibung
Project: Deploy and optimize OpenAI services
Deploy and optimize OpenAI services for a big customer. Cloud migration in Azure and Azure Cognitive Services. Deploy GPT4, advanced prompt engineering, GPT4 fast API computation with vectorized embeddings using ML. Project ongoing.
Tech Stack: NLP, Azure Cloud, OpenAI, GPT4.0, Python, Prompt Engineering, Gitlab CI/CD, Azure DevOps.
DevOps, Microsoft Azure, Natural Language Processing, Python
11/2022 – 3/2023
Tätigkeitsbeschreibung
Project: Smart Trunk Opener
As a Senior AI Engineer, I led the development of a Smart Trunk Opener project, which aimed to accurately predict kick gestures for hands-free trunk opening using the BGT24***** radar based on the Doppler effect and using Neural Networks on embedded devices.
- Developed algorithms to detect abnormal signals and validate them as kicks using Python and Matlab
- Created NN models for better predictions, and optimised them using TensorRT for efficient use on embedded devices
- Improved data reception and pre-processing on the radar using the C programming language
- Compiled complete production code as C code using Atmel Studio and deployed it on the embedded system
Tech Stack: Python, C++, C, Neural Networks, NumPy, Pandas, Pytest, Pytorch, Linux, Bash, Git, Matlab, Ifxdaq (Infineon Data Acquisition), Matplotlib, Time Series analysis, ARM Cortex, RTOS, ATmega, Atmel Studio.
ARM-Architektur, C++, Simulink, Neuronale Netze, Pandas, Python
6/2022 – 1/2023
Tätigkeitsbeschreibung
Project: Robot Manipulator Optimization
Determination of the optimal trajectory for the robot arm. I have
developed a custom neural network to solve this optimization problem
using physically modeled helper computations.
- Conducted research on optimal control problems via neural networks.
- Generated simulated data based on classical mechanics, performed data processing and normalization, and converted original Matlab files to Numpy.
- Constructed LSTM networks
- Enriched data with cubic splines and implemented objectoriented
programming to produce productive and efficient code.
Tech Stack: Neural Networks (Recurrent Nets), Supervised Learning,
Python, C++, Pytorch, Cuda, Numpy, Matplotlib, Torchdiffeq, MLFlow, Tensorboard, Autograd, Integrate, Torchsummary, Scipy, Torchcubicspline, Git, Jupyter Notebook, OOP, Pytest.
Neuronale Netze, Objektorientierte Software-Entwicklung, Python, Pytorch
6/2022 – 1/2023
Tätigkeitsbeschreibung
Project: Bacteria Detection
Development of a bacteria detection project using microscopic images,
achieving 99% accuracy in detecting the location and number of bacteria.
- Developed and optimized computer vision algorithms using OpenCV techniques to detect bacteria and their numbers with high accuracy.
- Trained yolo5 deep neural networks to extract relevant bacteria bounding boxes using IoUs.
- Accelerated the AI on Jetson Nano by converting to TensorRT (CUDA) and developed the final C++ code interference with pre- and post-processors.
- Optimized the yolo network decoder from Python to C++ and programmed tensors directly on the GPU using CUDA C++.
- Reduced the size of the AI utilizing Knowledge Distillation techniques to achieve high performance processing on the Jetson Nano.
Tech Stack: Python, C++, C, Neural Networks, Machine Learning, Object Detection, yolo, OpenCV, Pytest, Pytorch, CUDA, Numpy, Matplotlib, TensorRT, Git, Jupyter Notebook, OOP, Linux/Bash, AWS, Sagemaker, PyTest, Unittest, Jetson Nano
Amazon Web Services (AWS), C++, CUDA, Git, Maschinelles Lernen, Neuronale Netze, Objektorientierte Software-Entwicklung, Opencv, Python, Pytorch
1/2022 – 6/2022
Tätigkeitsbeschreibung
Project: Breast Cancer Detection on MRI
Development of a neural network-based algorithm to detect, classify
and segment breast tumors in mammography X-ray images to improve
radiologists' performance in breast cancer screening.
- Used segmentation techniques to train Mask R-CNNs to detect the tumors.
- Improving the inferenc via pretraining of the Mask R-CNN with the bounding box mask of the Yolo detector. Then training on qualitatively better but few masked data.
- Integrated sensitivity and precision metrics, and achieved stateof-the-art results.
- Deployed the finished code on AWS endpoint for professional level breast cancer detection.
Tech Stack: Python, C++, Mask RCNN, Segmentation Networks, PyTorch, NumPy, Machine Learning, OpenCV, AWS, Pandas, Git, Jupyter Notebook, Unittest.
Amazon Web Services (AWS), C++, Git, Maschinelles Lernen, Opencv, Pandas, Python, Pytorch
5/2020 – 11/2021
Tätigkeitsbeschreibung
Project: Fall Detection
Development of Fall detection Machine Learning Algorithms for a Smart Vision Assistant.
- Collect data of human skeleton in various fall scenarios via motion capture and skeleton detection algorithms.
- Balance the dataset with fall and non-fall scenarios to ensure that the machine learning algorithm can distinguish between normal and abnormal movements.
- Feature Extraction: relevant features extracted from skeleton data to represent the movement patterns: joint angles, joint velocities, and acceleration
- Data saved on AWS S3 bucket.
- Extracted features used to create matrices which were fed into a machine learning algorithm.
- Used AWS AutoML to find best ML model.
Tech Stack: Python, Scikit-learn, OpenCV, Pytorch, Image-Pose, AWS, Sagemaker, AutoML, AutoPilot, Cuda, Linux, Git, Pytest.
Project: Voice ReID, Voice to Text
Development of speech recognition system that can identify the speaker and transcribe the language from audio data.
- Audio data divided into 4-second segments sampled at 16kHz.
- Feature extraction techniques on audio data, such as MFCCs, Mel-scale spectrogram, chromagram, spectral contrast, and tonnetz, based on STFT, utilizing Kaldi.
- Utilizing Scikit-learn for feature clustering.
- Utilizing FFT (Fast Fourier Transformation) for denoising and feature extraction.
- Creation of VoiceReID custom model via PyTorch and TensorFlow.
- NLP algorithms for speech-to-text transcription, utilizing part-of-speech tagging and word sense disambiguation.
- Utilizing Gensim and NLTK for text summarization, tokenization, stemming, lemmatization, part-of-speech tagging, parsing
- Applied named entity recognition to identify named entities in text data and categorize them into predefined classes
Tech Stack: Python, pyannote, Kaldi, NLP, AWS Sagemaker, EC2, S3, Lambda, NumPy, TensorFlow, LSTM, Scikit-learn, NLTK, Spacy, Gensim, CoreNLP, Transformers, Hugging Face, Pandas, SQL, FastAPI, Git, Bash, VS Code, Unittest, Poetry.
Amazon Web Services (AWS), C++, Docker, Git, Opencv, Pytorch, Tensorflow
1/2020 – 5/2020
Tätigkeitsbeschreibung
Project: Box Optimization
Minimizing the amount of empty space in packages. This reduced not
only CO2 emissions but also transport costs. My solution achieved a 10% improvement in packing efficiency.
- Utilized data sampling, dimensionality reduction and data
approximation techniques to reduce the data size.
- Modelled the packages as matrices to rotate and modify easily via numpy.
- Developed evolutionary algorithms with efficient mutation and breeding methods as tensor operations on CUDA to have very fast parallel breedings.
- Benchmarked the results using MILP optimiser.
Tech Stack: Python, Numpy, Torch, Cuda C++, Pandas, SQL, Pytorch, Cuda, Cudnn, AWS, Sagemaker, Docker, Git, Jupyter Notebook, OOP, Clustering, NP-hard problems.
CUDA, Docker, Pandas, Pytorch, SQL
1/2019 – 12/2019
Tätigkeitsbeschreibung
Project: Invoice Recognition, OCR
Development of an OCR for invoice recognition software that checks
for invoice errors.
- Built an efficient SQL command search function to simplify the work.
- Used OpenCV to apply image recognition techniques.
- Implemented deep learning techniques for text recognition on invoices.
- Developed deep auto-encoders for data dimension reduction and noise reduction.
- Used Convolutional Neural Networks to improve logo recognition.
Tech Stack: Machine Learning, NLP, Python, Tensorflow, Numpy BigData, PyTesseract, Git, Bash, AWS, VPN, SQL.
Amazon Web Services (AWS), Maschinelles Lernen, Natural Language Processing, Python, Tensorflow, VPN
Ausbildung
Berlin
Berlin
Über mich
ich bin S., bin deutscher Staatsbuerger, habe in Berlin mein Abitur und an der Humboldt Universitaet zu Berlin Bachelor Physik (2013-2018) und Bachelor+Master Mathematik (zusammen 2014-2019) mit 1.0 absolviert. Bin im Bereich Numerische Mathematik und Optimierung vertieft, was mir ermoeglicht mithilfe der mathematischen Theorie sehr effiziente Programme zu schreiben, inklusive KI und Deep Learning, was sehr nuetzlich ist um schlaue KI und Programme auf kleine IoT devices zu integrieren. Habe bereits eine 4 jaegrige Freiberuflererfahrung und viele Projekte bei namhaften grossen Firmen gehabt. Verfüge sowohl über starke analytischen Fähigkeiten als auch bis hin zu production level Programmierung mit Python und C. Kann meine entworfenen Programmstrategien genauso gut selbst programmieren. Ich stecke 30-50% meiner Zeit in Selbstentwicklung in meiner Freizeit. Kann ebenso die Data Science und ML Probleme lösen.
Weitere Kenntnisse
Machine Learning, Cloud Computing, NLP, OpenAI, GPT4, OCR, Neural Networks, Computer Vision, Image Processing, Embedded, Time Series Analysis.
Programming:
Python, C++, C, Matlab, Azure, AWS
Software & Tools:
Scikit-learn, OpenCV, Numpy, Tensorflow, PyTorch, PyTesseract, Docker, Pandas, SQL, Spark, AWS Sagemaker, Linux, CUDA, C++, Git, Power BI
IoT & Embedded:
Jetson Nano, ARM Cortex, AT- mega
Production Code:
Unittest, Pytest, Poetry
Persönliche Daten
- Deutsch (Muttersprache)
- Englisch (Fließend)
- Europäische Union
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