Keras Jobs

I have a curated dataset of abdominal X-ray images that needs a robust deep-learning model capable of classifying key clinical findings. The end goal is a production-ready Python solution that can consistently score above 90 % accuracy on an unseen validation set. You’ll start with any mainstream framework you prefer—TensorFlow, Keras, or PyTorch—and handle the full pipeline: data preparation and augmentation, model architecture selection, training, hyper-parameter tuning, and evaluation. Please keep the code modular and well-commented so I can retrain or fine-tune later as new data comes in. A concise report that explains your decisions, metrics, and suggestions for future improvements will also be appreciated. To help me choose quickly, focus your proposal on your exp...

I need a complete machine-learning pipeline that can look at medical images—specifically plain-film X-rays—and tell me whether each study is of the chest, abdomen, or an extremity. All input files will be standard hospital exports (mostly DICOM, occasionally PNG/JPEG), so the model must handle typical variations in resolution and contrast. What I’m after is a reproducible, well-documented solution: data preparation, augmentation, model architecture (a CNN in TensorFlow, Keras, or PyTorch is fine), training, and evaluation. Please include class-balanced splits, explain any preprocessing you apply, and show the metrics you achieve on an unseen validation set. Deliverables • Python code with clear comments for preprocessing, training, and inference • Trained ...

I have a collection of X-ray studies and I need a robust deep-learning model that can look at each image and instantly tell me which predefined category it belongs to (e.g., chest PA vs. chest lateral, cervical spine, hand, etc.). The job is strictly about classifying the type of X-ray, not diagnosing any pathology. Here is what I already have and what I expect from you: • A curated folder structure with several thousand labelled PNG and DICOM files that you can download from my secure server. • A preference for Python with either PyTorch or TensorFlow/Keras—use whichever framework you feel will achieve the best accuracy and fastest inference on a modern GPU. • Clean, reproducible code (Jupyter notebook or script) plus a short README that explains environment se...

Project Title: AI-Based "Digital Arrest" Scam Detection System (MVP) Project Overview: I am looking for an AI/ML developer to build a functional prototype of a security system designed to detect "Digital Arrest" scams. The system needs to analyze video and audio inputs in real-time (or near real-time) to identify deepfakes, threatening language, and fake law enforcement visuals. Key Features Required (The Scope): I need a desktop-based prototype (Python/Streamlit or similar) that can process a sample video feed or live webcam input and perform the following: * Audio Threat Detection (NLP): * Transcribe audio in real-time (using OpenAI Whisper or Google Speech-to-Text). * Detect specific scam keywords/intents (e.g., "money laundering," "CBI," &...