Deep Learning Experts in Nuremberg
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Meet FRATCH Experts in Nuremberg, who have recently used Deep Learning
David O.
Last position:
Machine Learning Researcher at Pattern Recognition Lab, FAU
- Fine-tuned and evaluated alternative backbone architectures for the AFFGAN handwriting style encoder to better capture writer-specific features, benchmarking against the original baseline architecture and replacing the legacy VGG19 architecture with a custom Transformer-based encoder.
- Extended the system beyond word-level synthesis to line-level handwriting generation, integrating and adapting an existing line-generation architecture to support full-sentence output.
- Deployed the handwriting generation pipeline as a web application, enabling users to select a writing style, input custom text, and instantly generate a corresponding handwriting image.
- Integrated TrOCR into a teacher–student distillation framework to evaluate and improve readability of generated handwriting, improving OCR accuracy by 20% over the baseline.
- Boosted user-study pick-rates by 40%, demonstrating a significant leap in the perceptual quality and realism of the generated handwriting compared to the baseline model.
Tech: PyTorch, Python, GANs, CNN, ViT, TrOCR, Streamlit, CUDA.
Muntaha S.
Last position:
AI Engineer (Freelance) at Upwork
- Delivered 40+ AI projects and 23 strategic consultations for international clients (US, Europe, Middle East), achieving a 98% job success rate and building long-term partnerships.
- Developed and deployed production-grade AI solutions in computer vision, NLP, deep learning, and generative AI (LLMs, RAG pipelines, Stable Diffusion, OCR, chatbots), enabling automation and improving client efficiency by up to 70%.
- Designed and fine-tuned large language models (LLMs), including prompt engineering and integration with enterprise knowledge bases, leading to smarter decision-making and reduced manual effort.
- Built real-time computer vision applications (detection, segmentation, OCR) and integrated them into business systems, significantly enhancing accuracy and scalability.
- Consulted startups and enterprises on AI strategy, architecture, and deployment (cloud & on-premise), accelerating product development and reducing time-to-market.
- Managed complete AI project lifecycles (requirements gathering, solution design, deployment, support) in agile, international, and cross-functional environments, ensuring high-quality delivery.
Pawan S.
Last position:
CAPTCHA Recognition using CRNN
- Built a CRNN model with VGG16 and BiLSTM backbone for text-based CAPTCHA recognition
- Achieved 9.37% character error rate and 68.36% sequence accuracy on validation data
- Expanded data augmentation pipeline with distortions, noise injection, and clutter to improve robustness
- Conducted detailed error analysis on confusable characters (O, Q, D) and proposed error-specific augmentation
- Tech Stack: Python, TensorFlow/Keras, OpenCV, NumPy, Matplotlib
Puranjan B.
Last position:
Internship - Generative AI at Continental
- Gathered tire images and their feature descriptions.
- Cleaned dataset of image metadata using pandas.
- Stored image feature embeddings in Chroma vector db.
- Used image augmentations to increase dataset size.
- Used sklearn to create shuffled datasets and imbalanced-learn to balance class sizes in dataset.
- Used PyTorch to train and test different neural networks.
- Validated model using custom accuracy metric based on similarity search in ChromaDB.
- Visualized accuracy predictions using matplotlib.
- Plugged trained model into DreamBooth to train stable diffusion model and generate new images of tires.
- Created custom Docker image in Amazon Elastic Container Registry for machine learning script.
Uddipan B.
Last position:
Research Team Member at Munich Music Labs, TUM
- Focused on exploring the intersection of Music and AI.
Musaib P.
Last position:
Research Intern – Exploring Reasoning with Diffusion Models at Machine Learning and Perception group, FAU Erlangen-Nürnberg
- Investigating the equivalence between the Tiny Reasoning Model (TRM) and diffusion models for structured reasoning tasks such as Sudoku and maze solving.
- Exploring the reasoning and generative capabilities of diffusion models in symbolic problem-solving environments.
Ashmi J.
Last position:
Software Developer at Myrix Labs
- Engineered high-performance APIs with FastAPI + MongoDB, integrating live weather data (NOAA, NWS).
- Developed an AI chatbot with OpenAI APIs — context-aware by location, profession & interests.
- Created admin dashboard APIs for real-time monitoring and zero-downtime configuration.
- Integrated Stripe Embedded Payments with secure transactions & subscription management via webhooks.
Ekaansh K.
Last position:
Master thesis - LLM powered RAG System at Friedrich-Alexander-Universität Erlangen-Nürnberg
- Developed a RAG system to automate student queries with 96% accuracy, built using FastAPI and LangChain and deployed on the university server with Docker.
- Evaluated performance using RAGAS, comparing LLMs (Llama3.3, Llama3.1, GPT-4o-mini), vector embeddings, and various retrieval techniques within the RAG pipeline.
- Technical Skills: Python, FastAPI, Docker, AWS, LangChain, LangSmith, NLP, HTML, CSS
Discover over 15,000 top freelancers
Statistics of experts using Deep Learning
Aggregated from the professional profiles of matched freelancers.
Experience
7 years (Germany: 13 years)
Position duration
1.3 years (Germany: 2.1 years)
Positions per freelancer
5 (Germany: 8)
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Education, Manufacturing
Certification focus areas
Business Intelligence, Information Technology, Research and Development
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
100% (Germany: 86%)
Certifications per freelancer
2
Most common languages
English, German, Bangla
Speak two or more languages
100% (Germany: 99%)
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Nuremberg are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts in Nuremberg using Deep Learning
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Deep Learning experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (100%)
- Education (75%)
- Manufacturing (50%)
- Automotive (38%)
- Healthcare (38%)
- Retail (25%)
- Aerospace and Defense (13%)
- Arts and Crafts (13%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Deep Learning does
Deep Learning is a branch of machine learning that uses layered neural networks to identify patterns in large, complex datasets. It powers image recognition, speech processing, recommendation systems, forecasting and generative AI. Models learn useful representations from examples instead of relying only on manually defined rules.
Typical applications
Deep Learning appears in products and systems that need to interpret unstructured data or improve through training.
- Detect objects, defects and faces in images or video
- Classify documents, messages, audio and customer requests
- Generate or summarize text, images, speech and code
- Forecast demand, failures, risks and other business signals
Ecosystem and tooling
Professionals work with Python and frameworks such as PyTorch, TensorFlow and Keras. Projects may use Jupyter, Hugging Face, scikit-learn, CUDA and GPU cloud services for experimentation and training. Production work also involves APIs, Docker, Kubernetes, model registries, monitoring and data pipelines.
When companies need specialists
Freelance expertise is useful when a team has a promising use case but lacks the capacity to prepare data, test architectures or operate models reliably. It can also accelerate a proof of concept, modernize an existing machine learning workflow or move a trained model into production.
- Training data is inconsistent, limited or difficult to label
- Prototype results do not translate into a reliable product
- Inference costs, latency or hardware use need improvement
- A team needs practical guidance on open-source or custom models
What strong professionals deliver
Strong specialists connect model quality with business requirements. They define suitable evaluation methods, establish reproducible experiments and explain trade-offs between accuracy, speed, cost and interpretability. They also consider privacy, bias, security and monitoring before deployment rather than treating them as afterthoughts.
Collaboration in Nuremberg
Deep Learning work can be done remotely when data access, documentation and review processes are well organized. On-site collaboration in Nuremberg can help teams in manufacturing, logistics, healthcare, research and other data-intensive fields align models with operational needs. Clear communication in English or German may matter when specialists work closely with local stakeholders.
Frequently asked questions
Everything clients usually want to know about Deep Learning, in one place.
Deep Learning is used for tasks such as visual inspection, speech recognition, document understanding, demand forecasting and recommendation. It is especially useful when systems must learn from images, audio, text or other high-volume data.
Deep Learning can learn representations directly from complex data, while many traditional machine learning methods depend more heavily on manually selected features. Traditional approaches may be easier to train and explain on smaller structured datasets, so the right choice depends on the data, objective and operational constraints.
A strong Deep Learning specialist often combines neural network expertise with Python, data engineering, statistics and software development. Experience with PyTorch or TensorFlow, cloud GPUs, Docker, APIs, model monitoring and responsible AI can be important for production work.
The required background depends on the work. A proof of concept may need experience with data preparation, model selection and evaluation, while a production system requires evidence of deployment, monitoring, retraining and performance management. Ask for examples comparable to your data and delivery environment.
Yes. Deep Learning projects can be delivered remotely when specialists receive secure data access, clear documentation and reliable channels for technical reviews. On-site sessions in Nuremberg can still be valuable for workshops, factory visits or close collaboration with domain teams.
Deep Learning training often benefits from GPUs, particularly for large vision, language or generative models. Smaller experiments and inference workloads may run on CPUs, edge devices or existing infrastructure, depending on model size, latency and cost requirements.
Look for clear evaluation criteria, reproducible experiments and testing on data that reflects real operating conditions. A capable Deep Learning professional should explain failure cases, data limitations, bias risks, deployment trade-offs and how model performance will be monitored after release.
Before starting, a freelancer should clarify the business objective, data ownership, labeling process, success measures and target environment. For Deep Learning, it is also important to understand available compute, privacy constraints, expected latency, maintenance responsibilities and whether open-source or proprietary models are permitted.
The average hourly rate of freelancers in Nuremberg, Germany who have used Deep Learning in their recent projects is 45 €, which corresponds to a daily rate of about 362 € based on an 8-hour working day.
Of the freelancers in Nuremberg, Germany who have used Deep Learning in their recent projects, 100% hold at least a Bachelor's degree and 100% hold at least a Master's degree.
On average, freelancers in Nuremberg, Germany who have used Deep Learning in their recent projects have 7 years of professional experience, with a single engagement typically lasting around 1.3 years.
The most common languages among freelancers in Nuremberg, Germany who have used Deep Learning in their recent projects are English (100%), German (88%), and Bangla (25%).
The most common industries among freelancers in Nuremberg, Germany who have used Deep Learning in their recent projects are Information Technology (100%), Education (75%), and Manufacturing (50%).
The most common business areas among freelancers in Nuremberg, Germany who have used Deep Learning in their recent projects are Information Technology (88%), Product Development (88%), and Research and Development (88%).
Main locations of FRATCH Experts, who have recently used Deep Learning
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
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Hamburg 
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