Alexnet keras jobs
...learning model that reliably classifies each photo into the correct category. Your job is to design, train, and evaluate the full image-classification pipeline. You may build from scratch or fine-tune a proven architecture such as ResNet, EfficientNet, MobileNet, or a vision transformer—as long as the final model meets the accuracy targets we set together. Feel free to work in PyTorch or TensorFlow/Keras; I’m comfortable deploying either. What I’ll provide • A structured folder of training, validation, and test images • Category labels and a brief data dictionary • Access to a GPU instance if you need it What I need back 1. Clean, well-commented code (Jupyter notebook or Python scripts) that handles preprocessing, augmentation, training, an...
... The missing piece is the generative model that actually predicts depth, completes occluded geometry, and exports the result in a standard 3-D file. You will be working on an NVIDIA A6000 cloud instance that I have ready to go, so you can assume plenty of VRAM for large-scale diffusion, NeRF, or implicit surface models. I am framework-agnostic: if your best solution is PyTorch, TensorFlow, or Keras that’s fine, as long as the code is clean and reproducible. Key goals • Accept a single RGB image as input • Infer depth and surface normals, hallucinate hidden geometry, and rebuild a watertight mesh • Texture the mesh using the original image plus any learned in-painting • Output a common interchange format (OBJ, FBX, or glTF—pick whichever i...
...inconsistencies in the data. Preprocessing steps such as image resizing, normalization, and cleaning will be applied. Additionally, data augmentation techniques (e.g., rotations, flips, and brightness adjustments) will be used to increase variability and improve the model’s ability to generalize. The core of the system will involve developing a Convolutional Neural Network (CNN) using either TensorFlow/Keras or PyTorch. The model will be trained and optimized through experiments with different parameters and configurations. Its performance will be evaluated using commonly used metrics including accuracy, precision, recall, and F1-score, along with a confusion matrix to better understand the model’s performance across individual disease classes. Once a satisfactory m...
I need a full-length research paper prepared and shepherded through publication in a Scopus-indexed Q2 journal. The topic is neural-network-based detection of visual defects in woven or knitted textiles, implemented in Python. I am flexible about the framework—TensorFlow, PyTorch, Keras or a comparable library is fine—so long as the final code is reproducible and well-commented. The paper must include a solid literature review, a clearly explained network architecture, an experimental section using a representative dataset of fabric images, and a results discussion that meets the methodological rigour typical of Q2 outlets. I will supply any proprietary images I have; if additional public datasets are needed, please curate them. Deliverables • Draft manuscript f...
...identify and download a suitable, well-labelled dataset from Kaggle. Feel free to compare a few candidates, but the final choice should give good class balance and enough samples per disease category. Once the data is in place, walk through exploratory data analysis, preprocessing, and augmentation inside a Jupyter notebook. From there, build and tune a convolutional neural network (TensorFlow / Keras or PyTorch are both fine) and report the usual metrics plus a confusion matrix so I can judge class-wise performance. When the model is satisfactory, save it and wrap inference in a clean Streamlit app where a user uploads a single image and instantly sees the predicted disease name along with a confidence score. Additionally, integrate AI-based recommendations (e.g., treatment or...
...reliably detect vehicle licence plates and traffic signs in real-time video or still images. Everything must run locally without any cloud calls, built on TensorFlow and Keras with only open-source components. My goal is to receive a solution that I can drop onto an edge device or standard PC, start a camera feed, and immediately see bounding boxes around plates and recognised traffic signs. Model accuracy must be on par with commonly cited public weights; speed should be good enough for at least 15 fps on a mid-range GPU (CPU-only fallback appreciated). Deliverables • Fully documented source code in Python using TensorFlow + Keras • Pre-trained weights (or clear training script plus instructions to reproduce) for licence-plate and traffic-sign classes &b...
I’m putting together an academic-grade project that demonstrates how supervised learning can spot common w...data 3. A concise PDF explaining logic, workflow, and how to rerun everything (screenshots welcome) 4. Optional extras: a short report (~10 pages) and a slide deck to support a final-year viva—include these if you’re comfortable producing them, otherwise let me know so we can adjust milestones accordingly. Keep the solution native to widely used libraries (pandas, scikit-learn, TensorFlow/Keras or PyTorch) so reviewers can reproduce results without exotic dependencies. Accuracy matters, but clarity and reproducibility are paramount—I want to be able to hand this over, have someone install requirements, press “Run,” and immediately un...
...seasoned statistician who can move comfortably between classical regression techniques and modern Convolutional Neural Networks. The project centres on predictive analytics: you will build, compare and explain regression-based models, explore where a CNN adds value, and present the insights through clear, publication-ready visualisations created in Python (think pandas, scikit-learn, TensorFlow/Keras, matplotlib, seaborn or Plotly—use what fits best). We will begin with a brief video call so I can walk you through the dataset, the business question and the success metrics. After that, you will take full ownership of data preparation, model selection, training, validation and visual storytelling. Expect to hand back clean, well-commented notebooks and graphics that a non-te...
...face against a gallery to decide whether it is an identical match. • Serve three environments without extra rewrites: security-grade CCTV feeds, social-media style mobile uploads, and large photo-management archives. • Deliver low latency on a single modern GPU while still running acceptably on CPU-only hardware for lightweight deployments. I’m comfortable with either PyTorch or TensorFlow/Keras; use the framework you know best. A pre-trained backbone such as ResNet, MobileNet, or Vision Transformer is fine as long as you include the full training pipeline so I can continue to improve the model with fresh data. Deliverables 1. Source code with clear, commented modules for detection, embedding generation, and similarity matching. 2. Pre-trained weights ...
...researcher who can build a production-ready model that listens to a baby’s cry, watches the paired video, and decides—reliably—whether the cause is hunger, discomfort, or simple attention seeking. Audio and video must be fused inside one architecture; running them in parallel but independently will not satisfy our accuracy goals. You may use the deep-learning stack you trust most (PyTorch, TensorFlow, Keras, OpenCV, torchaudio, etc.) provided the final network can run in real time on an edge device and be exported to ONNX or TFLite. I will share product constraints and a small proprietary data set; you will expand it through public sources or augmentation, perform rigorous cross-validation, and refine the model until we consistently exceed 90 % precision and re...
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 y...
...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 model weights ready for deployment • A short report ...
...(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 setup, training commands, and how to run inference on a single file or a batch. • A trained model file and a simple inference function/CLI that returns ...
I need an expert to improve the accuracy of a histopathologic cancer detection model. The current model needs enhancement, and I prefer using algorithm enhancement for this task. Key Requirements: - Improve the model's accur...for this task. Key Requirements: - Improve the model's accuracy in detecting cancerous tissues. - Use advanced techniques and methodologies for algorithm enhancement. Ideal Skills and Experience: - Expertise in machine learning and deep learning - Strong background in medical image analysis - Experience with histopathological images - Proficiency in Python and relevant libraries (TensorFlow, Keras, PyTorch) - Familiarity with model evaluation and performance metrics Please provide examples of similar work and a detailed approach to how you would ...
...lip-sync errors, or artifacts). * Uniform/Badge Recognition: Detect if the person is wearing a police uniform or showing a badge (using object detection like YOLO). * Real-Time Risk Dashboard: * A simple UI that displays a "Trust Score." If the score drops below a threshold, it shows a "SCAM ALERT" warning. Preferred Tech Stack: * Language: Python * ML Frameworks: TensorFlow / PyTorch / Keras * Computer Vision: OpenCV, MediaPipe * NLP: Hugging Face Transformers (BERT/RoBERTa for intent classification) * Interface: Streamlit or Flask (for the demo dashboard) Deliverables: * Source Code (well-commented). * A file for easy installation. * A short demo video showing the system detecting a scam attempt from a sample video file. * Documentation on t...
I need a machine learning algorithm for a regression task using text data. Ideal Skills and Experience: - Proficiency in machine learning techniques, especially regression - Experience with text data processing and natural language processing (NLP) - Strong programming skills in Python or R - Familiarity with ML libraries like TensorFlow, Keras, or scikit-learn - Ability to preprocess, analyze, and model text data
...data-prep workflow, tune the architecture, and ship a repeatable training routine that reaches production-ready performance. What needs to happen • Curate and augment the image dataset, ensuring balanced classes and clear train/validation/test splits. • Redesign or refine the network—think transfer learning with EfficientNet or a custom ResNet variation implemented in PyTorch or TensorFlow/Keras. • Integrate early-stopping, learning-rate scheduling, and experiment tracking (e.g., TensorBoard or Weights & Biases). • Export a lightweight, versioned model file plus a clean inference script that takes a folder of images and returns class labels and confidence scores. • Document the environment setup, dependencies, and training commands ...
...data-prep workflow, tune the architecture, and ship a repeatable training routine that reaches production-ready performance. What needs to happen • Curate and augment the image dataset, ensuring balanced classes and clear train/validation/test splits. • Redesign or refine the network—think transfer learning with EfficientNet or a custom ResNet variation implemented in PyTorch or TensorFlow/Keras. • Integrate early-stopping, learning-rate scheduling, and experiment tracking (e.g., TensorBoard or Weights & Biases). • Export a lightweight, versioned model file plus a clean inference script that takes a folder of images and returns class labels and confidence scores. • Document the environment setup, dependencies, and training commands ...
...“Penghargaan paling tinggi bagi seorang pekerja keras bukanlah apa yang dia peroleh, tapi seberapa berkembang ia dengan kerja kerasnya itu.” What I need is a well-structured, engaging piece—think 800-1,000 words—written in Bahasa Indonesia, vibrant in tone yet grounded with practical take-aways. By the final paragraph, readers should feel eager to open their textbooks, commit to consistent effort, and see learning as personal growth rather than mere grades. Please weave in study-boosting tips (time-blocking, active recall, peer learning, etc.) and sprinkle relatable campus scenarios so the message lands. Key deliverable • One plagiarism-free article (Google Docs or Word) optimised around “motivasi mahasiswa,” “semangat belajar,...
...require a purpose-built deep learning model capable of reliably distinguishing authentic images from manipulated (deepfake) images. The scope is tightly focused on model design, training, and evaluation: developing an effective CNN-based architecture, training it on established deepfake datasets, and tuning it to perform robustly under real-world conditions. The implementation may use TensorFlow / Keras, PyTorch, or an equivalent framework, provided the entire training and inference pipeline is fully reproducible on a single modern GPU. I can supply standard datasets (e.g., FaceForensics++ image frames, DFDC samples) along with additional proprietary images if required. Please indicate if alternative datasets would materially improve performance. Deliverables • Well-docum...
...datasets (for instance, CMU Panoptic, PKU-MMD, Kinetics-Skeleton, or similar) plus any augmentations you create yourself. No private data collection will be possible on my side. • Training & evaluation: deliver clear metrics—accuracy, precision/recall per emotion class and real-time FPS tested on a 1080p classroom-style video. • Inference pipeline: provide Python code (PyTorch or TensorFlow/Keras are both fine), a lightweight REST or gRPC endpoint, and a demo script that ingests an RTSP stream from a classroom CCTV camera and overlays bounding boxes with emotion labels in real time. • Documentation: include setup instructions, environment file or Dockerfile, and a concise report explaining architecture choices, hyper-parameters, final metrics and ho...
I am ready to bekerja keras to turn my training as a dokter, specifically as a Spesialis anak, into reliable income. What I need now is a clear, actionable plan that shows exactly how to translate my pediatric knowledge into profitable services or products—whether that involves launching a private or tele-health clinic, creating parent-focused digital resources, or packaging expert content for online courses and eBooks. Here’s what I’d like from you: • A step-by-step roadmap outlining realistic revenue streams for a pediatric specialist. • Market validation of each idea, including approximate audience size and earning potential. • A marketing blueprint (online and offline) to attract parents and caregivers quickly. • Practical guidance on...
...from real-world projects (e.g., real-time voice assistants, self-healing agents, chatbots for fitness/health/finance, sentiment analysis, and integrations with tools like Azure OpenAI, Mistral/LLAMA, LangGraph, and Chroma). Sessions should cover: • AI & ML Basics: Core concepts, machine learning models, deep learning, NLP, computer vision, and tools like Python (pandas, scikit-learn, TensorFlow/Keras, NLTK, spaCy). • Generative AI: Building chatbots, RAG architecture, document intelligence, natural language interactions, and integrations with Azure OpenAI, Apache NiFi, or similar. • Agentic AI: Autonomous agents for tasks like voice coaching, error handling, API automation, using frameworks like LangGraph, CrewAI, AutoGen, ReAct, and persistent memory (e.g....
...end-to-end in the browser, crop the user’s face in real time, pass that crop to a freshly trained CNN, and immediately forward the model’s decision back to my PAI endpoint. The backbone of the job is the CNN itself. I’ll supply mixed image and video datasets; your task is to design, train, and fine-tune a model that can perform confidently under live-camera conditions. Whether you prefer TensorFlow, Keras, PyTorch, or another deep-learning framework is up to you, as long as the final weights and inference code are portable. On the front end, I need a lightweight web component—WebRTC or similar—that guides users to place their face inside an on-screen box. When the alignment is correct, the component should grab the frame, crop precisely around th...
I need a robust image-recognition system focused purely on object detection. You will take my raw images, prepare and label the dataset, choose the right deep-learning framework—TensorFlow, Keras, or PyTorch is fine—and train a model that reliably spots the target objects in new pictures. Once trained, package the solution so I can run inference locally or on a small cloud instance. That means delivering the cleaned dataset, training scripts, the final weights, and a short README that walks me through setup, retraining, and evaluation commands (preferably in a single or ). Accuracy, speed, and a clear hand-off matter more to me than fancy dashboards, so concentrate on a well-documented, reproducible pipeline and a model that meets the mAP benchmarks we’ll agree
I need to add a reliable mask generator to my ANPR pipeline that pinpoints the license-plate region in still JPEG photographs. The task is limited to detection—no character recognition for now—so the model simply has to return an accurate bounding box or, better, a pixel-level mask for...training steps. Reproducibility is key; I want to be able to retrain the model from scratch on my side. Deliverables (to be accepted): • Clean, well-commented Python code and • Trained weights (.pth, .ckpt, or .h5) • CLI script plus a minimal REST endpoint for batch inference • README explaining data prep, training, and inference commands OpenCV, PyTorch, TensorFlow, or Keras are all acceptable; pick whichever you are most comfortable with, and keep the dep...
I’m working on a personal research project that relies on convolutional neural networks to analyse image data, and I’ve reached the point where the baseline architecture no longer meets my goals. I need the model to be both more accurate and noticeably faster at inference without sacrificing one for the other. You’ll start with my existing checkpoints and training scripts (Python, TensorFlow/Keras—PyTorch equivalents are fine if you would rather port). The task is to redesign or fine-tune the network, apply the right data-augmentation or regularisation tricks, and then compress, prune, quantise, or otherwise accelerate it so that I see measurable gains in both metrics. I’m happy to experiment with advanced techniques such as knowledge distillation or N...
...will receive a folder of cropped and noise-added fingerprint fragments plus their full counterparts for validation. I am open to CNN-based inpainting, hybrid approach, AI and image processing—so long as the final output convincingly restores minutiae and passes a side-by-side comparison against the ground-truth scans. Please work in mainstream, well-documented libraries (PyTorch, TensorFlow or Keras, OpenCV, scikit-image) and keep the environment reproducible through a conda .yml file. Deliverables: • Fully commented Python scripts or notebooks • Trained model weights and instructions for inference on new scans • A concise README outlining setup, training, and evaluation steps • Quantitative report showing reconstruction quality (PSNR/SSIM or sim...
... script that can: Take a live video feed from a webcam, Recognize the gestures in real-time, and Output recognized text (and speak it out using gTTS or Web Speech API). Documentation: A basic README file explaining: How to run the code, How to use the trained model, Simple instructions for setting up and testing. Skills Required: Basic Machine Learning experience (TensorFlow / Keras / PyTorch). Gesture Recognition or Hand Pose Estimation using models like MediaPipe or HandPose. Real-Time Processing (video or webcam input). Familiarity with (optional, if the model needs to be used in a browser). Budget: This is a small project with a limited budget. Please provide an estimate based on basic functionality and a quick turnaround (within 1–2 days). Timeline:
...trained model for gesture recognition. Code should include token detection, sentence building, and displaying the recognized text. Documentation: A basic README explaining the model architecture, how to run the inference code, and usage instructions. Required Skills: Machine Learning (ML) with experience in computer vision and gesture recognition. Deep Learning frameworks such as TensorFlow, Keras, or PyTorch. Familiarity with real-time gesture recognition models (e.g., MediaPipe, HandPose, OpenCV). Experience with for web-based deployment is a plus. Ability to work under tight deadlines and deliver working code quickly. Preferred Experience: Previous work on gesture recognition, sign language, or pose estimation models. Knowledge of real-time video processing and mo...
We are urgently looking for an experienced Machine Learning / Computer Vision developer to deliver a robust offline handwritten OCR system that supports both Hindi and English handwriting with high accuracy. Project Requirements: Must accurately read handwritten text (not printed). Mandatory language s...OCR model you can provide. Deliverables must include: Full source code access Model architecture + weights Complete setup and usage documentation Should work on standard image formats (JPG/PNG) and scanned pages. Can assist with fine-tuning on our own dataset. Required Expertise: Strong experience with handwritten OCR Deep learning (CNN/RNN/Transformers) Tools/frameworks: PyTorch, TensorFlow/Keras, OpenCV, Tesseract (if customized) Experience with Indic languages OCR (highly ...
I need a specialized image classification model to analyze medical images, specifically for ear-related purposes. The primary task is classifying images to assist in medical evaluations. Key Requirements: - Develop an im...evaluations. Key Requirements: - Develop an image classification model - Focus on medical images of ears - Ensure high accuracy and reliability - Deliver a user-friendly interface for classification results Ideal Skills and Experience: - Expertise in machine learning and image classification - Experience with medical image analysis - Proficiency in Python and relevant libraries (e.g., TensorFlow, Keras) - Strong background in developing and deploying ML models Please provide a portfolio with relevant projects and ensure you have a solid understanding of medica...
...support LightGBM, CNN, and GNN experiments exactly as implemented in my project files. The freelancer will be responsible for installing and configuring: 1. Required Environment Python 3.10+ Jupyter Notebook Pip & virtual environment GPU support (CUDA & cuDNN) if my laptop supports it Required Python packages including: pandas, numpy scikit-learn lightgbm seaborn, matplotlib tensorflow / keras (for CNN) torch + torch_geometric (for GNN) scipy pillow tqdm networkx utilities 2. Dataset Setup Please prepare the following datasets on my laptop: • EMBER2018 (CSV features) Used for LightGBM. • Malimg Dataset Folder structure with malware family images for CNN classification. • LAMDA Dataset CSV with features and labels for GNN and drift...
...with free-text fields. The work will live in a single, clearly commented Jupyter Notebook and should make full use of the Python stack I already rely on: pandas and NumPy for wrangling, matplotlib and seaborn for plots, scikit-learn for vectorisation and any basic modelling, plus NLTK or spaCy for text cleaning and tokenisation. Should deeper experimentation help, feel free to reach for TensorFlow/Keras or XGBoost, but only if they add insight. Here is what I’d like to see when you hand the notebook back: • Cleaned and well-documented preprocessing steps for both the numeric and text portions • Informative visuals that highlight key distributions, correlations, and any interesting patterns you uncover • A concise narrative of findings embedded in Markdo...
I need a well-structured Jupyter Notebook that walks through a complete data-analysis workflow from raw import to polished insights. The stack is already decided—pandas and NumPy for wrangling, scikit-learn for any light modeling or preprocessing, matplotlib and seaborn for clear visualisations. If NLP techniques, TensorFlow/Keras, XGBoost, NLTK or spaCy happen to add value, feel free to weave them in; the notebook should stay fully reproducible with tidy, commented code cells. Here is what matters to me: • Clean exploratory analysis showing how you profiled the dataset, handled missing values, engineered useful features and justified each choice. • Visualisations that genuinely reveal patterns rather than decorate the page. • A concise narrative—M...
...and optionally augment this data. From there, the model—whether a UNet, CNN–LSTM, or another Keras-friendly architecture that you justify—should be trained to highlight pixel-level change. Please keep the code clear, reproducible, and GPU-ready. Visual output is essential: I need crisp change-detection masks overlaid on the original imagery plus colour-coded maps that make before/after differences intuitive to a non-technical audience. For evaluation, deliver the usual remote-sensing metrics (precision, recall, F1, IoU, and a confusion matrix). Include any additional indicators that reveal model bias or class imbalance. Deliverables • Clean, annotated Python notebook (or .py script) using Keras • Trained model weights and instructions to ...
...instantly return the right label with high confidence. Here’s the flow I have in mind: you design and train a convolutional neural network (or a transformer-based vision model if it suits the task better), fine-tune it on the dataset I’ll provide, and wrap the result in a lightweight REST or GraphQL API so it can slot straight into our existing backend. You’re free to work in PyTorch or TensorFlow/Keras; accuracy, speed and clean, documented code matter more to me than the choice of framework. Deliverables • A trained, version-controlled model ready for production • Inference script or dockerised API endpoint with installation instructions • Brief report outlining architecture, training process and achieved metrics If you’ve shipped i...
...instantly return the right label with high confidence. Here’s the flow I have in mind: you design and train a convolutional neural network (or a transformer-based vision model if it suits the task better), fine-tune it on the dataset I’ll provide, and wrap the result in a lightweight REST or GraphQL API so it can slot straight into our existing backend. You’re free to work in PyTorch or TensorFlow/Keras; accuracy, speed and clean, documented code matter more to me than the choice of framework. Deliverables • A trained, version-controlled model ready for production • Inference script or dockerised API endpoint with installation instructions • Brief report outlining architecture, training process and achieved metrics If you’ve shipped i...
My CNN project in TensorFlow / Keras trains and runs end-to-end but plateaus at disappointingly low accuracy. I’m looking for a fresh set of eyes to pinpoint why and tune it back on track—without tearing the codebase apart. What you’ll actually do • Walk through the current notebook / .py files and training logs to spot signs of overfitting, underfitting or plain hyper-parameter mis-matches. • Experiment inside the same files with learning rate schedules, batch size, epoch count and, where sensible, light touches of regularisation (dropout, weight decay) or data augmentation. • Keep architecture and data loading intact; all fixes should be incremental, easy for me to diff and rerun. • Run a clean training cycle that demonstrates a clear...
I have a small, curated dataset of European songs and need a clear, reproducible demonstration model built in Python using TensorFlow/Keras. The goal is simply to show how we could predict or classify which tracks are most likely to resonate with European listeners—nothing production-grade, just a clean proof of concept that I can study and rerun. Here’s what I’m after: • A short, well-commented notebook or script that loads the data, performs any essential preprocessing, trains a straightforward model, and prints basic evaluation metrics. • Clear instructions (README or inline notes) so I can execute everything on my machine with a fresh virtual environment. • A brief write-up—one pager or a few slides—summarising feature choices, m...
...environment (Azure, AWS, Google Cloud Platform) Demonstrated proficiency with AI/ML fundamental concepts and technologies including ML, Deep learning, NLP, and computer vision. Demonstrated expertise in attacking GenAI products and platforms. Demonstrated recent experience with large language models. Demonstrated experience with using AI testing frameworks and tools such as TensorFlow or PyTorch, or Keras Demonstrated ability to write test scripts, automate test cases, and analyze test results using programming languages and testing frameworks listed above. Demonstrated ability to Identify and document defects, irregularities or inconsistencies in AI systems and working closely with developers to rectify and resolve them. Ability to work independently to learn new technologies, m...
...Comparative Analysis: Benchmarking the proposed approach against existing state-of-the-art methods. 6. Conclusion and Discussion: Summarizing findings, discussing implications, and suggesting future directions. ________________________________________ Requirements: • Strong background in AI, Machine Learning, or Deep Learning. • Proficiency in Python with frameworks such as PyTorch, TensorFlow, or Keras. • Ability to design and execute experiments independently. • Excellent skills in academic writing and research documentation. • Familiarity with IEEE/Elsevier/Springer paper structures and standards. • Prior publication or experience writing research papers is highly preferred. ________________________________________ Deliverables: • Complete re...
...Machine Learning project developer to complete my academic project related to image classification for breast cancer detection. The dataset (images of cancerous and normal breast tissue) will be provided. The goal is to train a machine learning/deep learning model to classify whether a patient has breast cancer or not. Project Requirements: Develop and train the model using Python (TensorFlow/Keras or PyTorch) Implement image preprocessing, data augmentation, and model evaluation Achieve good accuracy, precision, recall, and F1-score Provide a detailed project report (Word or PDF) explaining: Problem statement & objectives Dataset description Model architecture & methodology Results and analysis (with graphs, confusion matrix, etc.) Conclusion and future scope ...
I have a Quantum-IoT pipeline that predicts urban-heat islands with a hybrid LSTM / Quantum-LSTM network. The raw sensor feed is undermining performance: inconsistent readings are slipping ...data-validation layer that flags or corrects those inconsistencies before the preprocessing step. • Retrain the classical LSTM and the Q-LSTM branches after the data fix and show the improvement through comparative metrics (MAE, RMSE, R²). • Deliver clean, well-commented scripts and a short README outlining what was changed, why, and the results. Familiarity with IoT time-series cleaning, TensorFlow/Keras, and at least one quantum framework (PennyLane or Qiskit) is essential. If you can get the pipeline back on track and demonstrate better predictive accuracy, that’s e...
Freelancer should be of Pakistan or India. I need new freelancer at low budget. I need a complete, end-to-end Python pipeline that...be of Pakistan or India. I need new freelancer at low budget. I need a complete, end-to-end Python pipeline that accurately segments brain-tumor regions on the BraTS 2023 MRI dataset. The job covers everything from data loading and preprocessing through model training, validation, and inference. You’re free to choose the deep-learning framework you’re most productive with—PyTorch, TensorFlow, or Keras—as long as the final code is clean, reproducible, and clearly commented. a deep learning-based segmentation model using TransUNet, a hybrid CNN + Transformer-based U-Net model, to automatically segment brain tumors from MRI scans ...
I need new freelancer at low budget. I need a complete, end-to-end Python pipeline that accurately segments brain-tumor regions on the BraTS 2023 MRI dataset. The job covers everything from data loading and preprocessing through model training, validation, and inference. You’re free to choose the deep-learning framework you’re most productive with—PyTorch, TensorFlow, or Keras—as long as the final code is clean, reproducible, and clearly commented. a deep learning-based segmentation model using TransUNet, a hybrid CNN + Transformer-based U-Net model, to automatically segment brain tumors from MRI scans in the BraTS 2023 dataset.
...Deliverables 1. Cleaned dataset and feature-engineering code. 2. Two or more candidate models with evaluation results and comparison plots. 3. Final chosen model saved (pickle/joblib) plus inference script. 4. README summarizing methodology, assumptions, and how to run or extend the model. Tools Python 3.x with common libraries such as pandas, scikit-learn, statsmodels, TensorFlow/Keras or PyTorch—use what best fits the winning approach. I’m aiming for a functional prototype that is ready for internal testing and easy to enhance in future iterations....
...overlapping windows of 128 samples across six channels, and applies the same z-score normalization computed on the training split to ensure training–inference parity. A compact CNN→LSTM model learns short motion motifs with 1D convolutions and aggregates them temporally with an LSTM before a softmax layer outputs activity probabilities. To satisfy embedded constraints, the floating-point model trained in Keras/TensorFlow is exported to TensorFlow Lite and quantized to INT8 using post-training quantization with a class-balanced calibration set of at least 500 windows. Quantization typically reduces size by about 4× and speeds inference while keeping accuracy within roughly 0–2 percentage points of the FP32 baseline. The Kotlin Android app integrates the Tens...
Computer Vision Data Scientist (AI / Deep Learning) Company Overview: W...Responsibilities: Develop models for object detection, classification, and image segmentation. Work with TensorFlow, PyTorch, and OpenCV. Process and augment image datasets. Deploy models using ONNX, TensorRT, or Docker. Collaborate with product and AI engineering teams. Requirements: 3+ years of experience in computer vision or deep learning. Proficient in Python and frameworks such as PyTorch or Keras. Experience with CNN architectures (ResNet, YOLO, EfficientNet). Familiar with GPU training and optimization. Preferred Skills: Experience with real-time image processing or edge AI. Knowledge of AWS Rekognition or Azure Cognitive Services. Compensation: $65–$120 per hour or $130,000–...
