Hire the Best Convolutional Neural Network Specialists

Top 1% Expert-Vetted Freelancer. I have a PhD in Computer Science (AI & Machine Learning) and I am a physician. I build custom AI systems with proprietary and public data, including creation of novel datasets and state-of-the-art models. * After a 30-minute to 1-hour initial meeting, I'll craft a clearly written report outlining a unique AI strategy for your project. * For long-term engagements, I implement AI solutions end-to-end, including data collection, data cleaning, novel model design, model implementation and training in Python, and iteration on the modeling and data strategy to achieve excellent performance. * One of my specialties is creating custom models for proprietary datasets, including images, videos, audio recordings, and domain-specific data. Book a 30-minute consultation with me to get immediate answers about your AI project: - How can you leverage AI in your organization? - What kind of AI do you need to solve a particular problem? - Should you use a model like ChatGPT/Claude or do you need a custom model? - What modeling strategy should you use? - How should you collect and annotate data? - How much data will you need? - How should you measure performance? After a consultation I’ll craft a clearly-written report outlining an AI strategy for your project. Specialties: Healthcare AI: I am an expert in healthcare AI, with a unique background that blends clinical understanding from my MD, deep technical expertise from my AI PhD, and business experience. I have completed 100+ engagements across medical specialties including radiology, dermatology, cardiology, mental health, family medicine, surgery, dentistry, physical therapy, women’s health, pediatrics, nutrition, and neurology. I have created datasets and custom models for diverse forms of biomedical data including x-rays, CTs, MRIs, clinical photographs, patient videos, medical audio, medical notes, EHR data, insurance claims, omics data, and more. Computer Vision: I create custom AI models for 2D images, 3D images, and videos on proprietary datasets. I am excited about projects from multiple industries including healthcare, manufacturing, automotive, and agriculture. I have extensive experience with classification, object detection, segmentation, and keypoint detection, to identify and localize abnormalities or features of interest. I developed the first machine learning model in the world to predict multiple abnormalities simultaneously from a CT scan. For a dermatology practice, I built the first CV model to predict Fitzpatrick skin type, pigmentation, redness, and wrinkle severity (mean accuracy 85%)—now a core model at Kesty AI. Artificial Intelligence R&D: I have led multiple AI research projects across industry and academia. I have developed novel AI methods, including HiResCAM, a convolutional neural network explanation method with mathematical guarantees. I have published original research across multiple areas of AI, including computer vision, natural language processing, explainable AI, expert systems, and applied AI. My research papers have been cited over 1,000 times. My healthcare AI blog Glass Box has over 700,000 readers from 185 countries. Natural Language Processing (NLP): I have a deep understanding of large language models (LLMs) like Claude, Gemini, ChatGPT, and Llama. I have leveraged Transformers and other NLP techniques for numerous applications, including customized chatbots, medical note generation, and structured information extraction. Advising Entrepreneurs: Before focusing on AI consulting, I spent seven years as the founder of a health AI startup. I led my previous company from concept to deployed B2B SaaS product serving medical practices. Our AI history-taking assistant and AI scribe saved clinicians 2+ hours daily. I managed engineering teams of 5-10 (60+ contributors), secured two U.S. patents, and raised competitive grant funding. I enjoy working with entrepreneurs and discussing pitch decks, fundraising, customer discovery, designing an MVP, and evaluating the ROI of an AI product. If you’d like to talk with me about your AI project, please feel free to send me a message or book a consultation using the link on my profile.

Greetings, I am a Senior AI Engineer and Data Architect specializing in Machine Learning, Deep Learning, Federated Learning, LLMs, AI Automation, and Data Architecture. I focus on building scalable, secure, and production-ready AI and data solutions that solve real business problems. My core strength lies in designing end-to-end AI and data systems, from data ingestion, data modeling, and data processing to model development, deployment, automation, and business intelligence. I have strong experience in LLM-based RAG systems, computer vision, privacy-preserving AI using Federated Learning, and modern data platforms. I also work on enhancing AI security through Blockchain integration where applicable. - Key Expertise: * Data Architecture & Engineering * Data Lake, Data Warehouse, and Lakehouse Architecture * Data Modeling: Star Schema, Snowflake Schema * ETL/ELT Pipelines and Data Integration * Data Governance, Data Quality, and Metadata Management * Batch and Real-Time Data Processing * Big Data Platforms: Spark, Hadoop, Cloudera CDP * DataOps, CI/CD, and MLOps - Generative AI and Automation * LLMs, RAG systems, chatbots * AI workflow automation and integrations - Advanced AI * Federated Learning * Secure AI systems with Blockchain integration - Machine Learning * Regression models, Decision Trees, SVM * Ensemble methods: Random Forest, Gradient Boosting, XGBoost * Probabilistic and distance-based models: Naive Bayes, KNN - Deep Learning * ANN, CNN, RNN, LSTM, GAN * Model optimization and deployment - Computer Vision * Image classification, object detection, segmentation I am passionate about collaborating with clients to deliver robust, efficient, and future-ready AI and data solutions. If you are looking for a Senior AI Engineer and Data Architect who combines research-level expertise with real-world implementation, I would be happy to discuss your project.

I am an AI Researcher with numerous publications including IEEE TNNLS, MICCAI, CBMS, etc. I also have a YouTube channel named "Idiot Developer", where I upload videos related to deep learning. I also write blog posts on my website idiotdeveloper.com. ✅ Projects: 1. Human Face Landmark Detection - using pre-trained MobileNetv2 2. Multiclass Face Segmentation - using UNET. 3. Dog Breed Classification - using pre-trained ResNet50 4. Human Parsing using Crowd Instance-level Human Parsing (CHIP) dataset 5. Brain Tumor Segmentation 6. GAN to generate Anime 7. Retina Blood Vessels 8. Background Removal from Human Images and Videos 9. Implemented various segmentation architectures: U-Net, ResU-Net, DeepLabV3+, DoubleUNet, U2-Net and more. 10. Lung Segmentation 11 Autoencoders

Do you have an AI vision that needs to become a real, working product? I don't just build models; I engineer complete, scalable solutions that turn data into actionable insights and automation. For over five years, I've specialized in bridging the gap between cutting-edge Artificial Intelligence (AI) research and robust software that delivers real-world value. My core expertise lies in computer vision and machine learning, but my skill set is full-stack. This means I can own your project from the initial data pipeline, through model training and optimization, all the way to deploying a polished desktop application or a secure enterprise API. I thrive on building tools that work seamlessly for end-users, whether it's a retail manager, a traffic controller, or a sports coach. My strongest suit is developing intelligent systems that "see" and understand the world. I've built a retail analytics platform (CrowdIQ) that transforms standard CCTV into a source of business intelligence, tracking customer demographics and behavior. In the sports domain, I created PadelIQ, an analytics engine that uses computer vision to track player movement, posture, and court coverage from match footage, providing real-time coaching feedback. For public safety, I developed a traffic management system (OmniRoad AI) using advanced object detection for real-time accident and congestion monitoring. Beyond computer vision, I architect full-scale data science pipelines. A prime example is my telecom churn prediction project, where I built a machine learning model to identify at-risk customers and paired it with an interactive Power BI dashboard. This end-to-end approach—from data analysis to a clear visualization of insights—ensures the model's findings directly inform business strategy and retention actions. I also develop the tools and infrastructure that power AI applications. I've built secure, enterprise-grade systems like DevelmoGPT, a RAG-based LLM that allows for secure, semantic search over private company documents. From creating simple utilities like PDF-to-audio converters to designing complex role-based access systems, I ensure the foundation of any AI solution is reliable, secure, and maintainable. My process is collaborative and results-driven. I start by deeply understanding your business problem, not just the technical requirement. We'll then iterate through prototyping, development, and testing to ensure the final product not only meets specs but also delivers tangible ROI. I communicate clearly at every stage, providing demos and documentation so you're never in the dark. Let's connect. Share your project idea or challenge, and I'll provide a clear outline of how we can leverage AI, machine learning, or computer vision to build your intelligent solution. Click the invite button to start the conversation. /// The following is just for SEO. You can ignore it /// #computer vision #computer vision engineer #computer vision OpenCV #machine learning computer vision #deep learning computer vision #computer vision machine learning #machine learning python #nlp machine learning

I help researchers, startups, and businesses turn AI ideas into working MVPs and scalable products through machine learning development, AI model integration, AI/Backend Engineer, Workflow Automations and research-backed implementation & writing support. 🏆 Top Rated | 100% Job Success | about $10K+ Earned | 40+ Completed Upwork Jobs Whether you need to validate an AI idea, integrate an existing model, reproduce research code, or improve an ML pipeline, I can help move your project from concept to a reliable implementation. 🏆 MVPs to Scalable Products | AI Research & Development | Research Support-Asistance 🧠 Deep Learning | Machine Learning | ML Model Training & Fine-Tuning | APIs | LLMs 🌟 2D/3D Vision | Sensors and Medical Data | GitHub & Hugging Face Code Reproduction 🧠 Classification, Regression, Time Series, Forecasting, Detection & Recognition WHAT I CAN HELP YOU BUILD: ✅ AI & Machine Learning MVPs, SaaS, Startup Predictive models, prototypes, backend APIs, AI model integration, and deployment-ready workflows. ✅ Deep Learning & Computer Vision Solutions Image classification, object detection, segmentation, anomaly detection, medical imaging, and 2D/3D vision pipelines. ✅ LLM, RAG & AI Integration OpenAI API integration, LangChain workflows, ChromaDB knowledge bases, retrieval pipelines, and AI-powered applications. ✅ Model Training & Fine-Tuning Data preparation, training pipelines, experimentation, evaluation, optimization, and reproducible implementation. ✅ Research Support & Code Reproduction Research-paper implementation, GitHub and Hugging Face code reproduction, benchmarking, ablation studies, experiment support, and technical reporting. ✅ Medical & Scientific Data Solutions Research-focused machine learning workflows for structured, image, and multimodal datasets. ✅ Time-Series & Sensor Data Solutions Forecasting, anomaly detection, signal processing, predictive modelling, and machine learning workflows for sensor, IoT, and sequential datasets. CORE TOOLS: Python | PyTorch | TensorFlow/Keras | Scikit-learn | OpenCV | Vercel AI | Github | Hugging Face | Flask | FastAPI | OpenAI API | Claude Code | LangChain | ChromaDB | Next.js | SQL | Docker | R/Rstudio | Matlab PyTorch | TensorFlow | Deep Learning | Neural Networks | Machine Learning | Machine Learning Model | Large Language Model | RAG | AI Model Development | AI Model Integration | Data Engineering | biostatistics | Statistical Analysis | Data Engineering | Image processing | Signal Processing WHAT YOU CAN EXPECT ✓ Clear communication and realistic scoping ✓ Clean, reproducible code and documentation ✓ Research-backed implementation decisions ✓ Flexible collaboration across USA, Europe, UK, and Australia time zones I also support Python/R Data Science Data Analysis, NLP, LLMs, OpenAI API, RAG chatbots, Clinical Data, Data Engineering, scraping tasks projects and build ETL pipelines as well as setup databases. Share your idea, dataset, existing codebase, or research paper, and I will help define the most practical path from prototype to implementation.

I am a Machine Learning Engineer with four years of experience working across deep learning research, large scale AI systems, and production model deployment. Over the years, I have worked extensively in medical imaging, computer vision, NLP, signal processing, and large language models, building systems that range from experimental research pipelines to deployed real world AI applications. My day to day work primarily involves Python, PyTorch, TensorFlow, Keras, HuggingFace Transformers, sentence transformers, scikit learn, OpenCV, Pandas, and NumPy. I enjoy working deeply on both the research and engineering sides of machine learning, especially problems that require understanding model behavior rather than simply applying existing architectures blindly. A large part of my background is research driven. I have authored multiple peer reviewed publications in indexed journals and IEEE conferences, including publications in Neurocomputing, Healthcare Analytics, Engineering Applications of Artificial Intelligence, Telematics and Informatics Reports, and other Elsevier and IEEE venues. My research has focused heavily on explainable AI, healthcare AI, and advanced deep learning systems. Some of my published work includes CARDxnosis, an explainable knowledge driven framework for ECG diagnosis and clinical report generation, an explainable AI system for trustworthy arrhythmia detection, a CNN RNN Attention hybrid architecture for automatic modulation classification, ensemble deep learning approaches for lung cancer detection from CT scans, and SRGAN based white blood cell image generation and classification pipelines. Alongside published work, I am currently involved in research on brain tumor segmentation, ADHD and ASD classification from brain connectome graphs, epileptic seizure prediction from EEG signals, and interpretable tabular learning using graph neural networks combined with Kolmogorov Arnold Networks. Beyond research, I have substantial hands on experience building and deploying production grade AI systems. One of my major recent projects was LaborBERT v4, a domain adaptive transformer fine tuning system processing hundreds of thousands of records through a large scale training pipeline. The project involved multiple experimental setups including contrastive learning, masked language model pretraining, temporal contrastive learning, cross attention based fusion, multi task training, and Matryoshka Representation Learning. I have also built hybrid embeddings plus LLM systems for taxonomy mapping using OpenAI embeddings alongside locally hosted LLaMA and Mistral models through Ollama. In addition, I have worked on deployed clinical AI systems and a portable on device diagnostic AI solution with embedded deep learning models for point of care inference, which gave me valuable experience in optimization, deployment constraints, inference design, and production reliability. My broader project portfolio includes vehicle detection using Mask R CNN, human activity recognition on the Kinetics 700 dataset, facial keypoint detection with MultiRes UNet, semantic segmentation pipeline redesign, Stable Diffusion based image editing workflows, toxic comment classification, RASA based conversational AI systems, and large scale scraping and automation pipelines using Playwright and Selenium. I have also worked with Flask and Django based deployment pipelines and cloud hosted ML systems. From an engineering perspective, I care strongly about clean and maintainable systems. I follow disciplined workflows involving modular code design, Git based version control, reproducible experimentation, structured evaluation, bootstrap validated metrics, and detailed documentation. I am also comfortable preparing scientific reports, research papers, and journal submissions using both LaTeX and Word. What ties all of this together is that I genuinely enjoy solving difficult technical problems, especially the kind that require balancing research depth with practical engineering constraints. I am most motivated by projects where thoughtful experimentation, careful system design, and real world usability matter equally.