Dilip is available for hire
Hire DilipDilip Mathew Thomas
Verified Expert in Engineering
机器学习开发人员
Location
Kochi, Kerala, India
Toptal Member Since
April 11, 2019
同时获得计算机科学与工程博士学位, Dilip在该行业拥有超过十年的经验. 自2015年以来，他一直专注于与机器学习和深度学习相关的项目. Dilip has an eye for detail which helps in working closely with domain scientists and improving the accuracy and reliability of models for fine-grained image classification, 目标检测与分割, 自然语言处理, 时间序列预测, and generative AI.
Portfolio
独立咨询公司
Scikit-learn, PyTorch, Keras，人工智能...
Vyby Inc
人工智能(AI)，稳定扩散，谷歌云平台(GCP)...
Photograde
Scikit-learn，深度学习，计算机视觉，Redis, FastAPI, Flask, REST api...
Experience
Availability
Part-time
Preferred Environment
Git, Scikit-learn, PyTorch, Keras, Ubuntu
The most amazing...
...project I've worked on was the automation of a factory using an array of cameras and computer vision techniques.
Work Experience
AI/ML Consultant
2015 - PRESENT
独立咨询公司
- Performed the role of CTO for several early-stage startups by translating high-level product requirements into technical requirements. 设计实验，指导初级工程师构建和评估AI模型.
- 执行学术出版物，并根据客户的要求进行定制. 提供深度学习和机器库(如PyTorch)方面的实践经验, TensorFlow, Keras, Hugging Face, and scikit-learn.
- 架构的自然语言处理和稳定的扩散模型与拥抱脸库.
- Worked with domain experts to understand the nuances and biases in the data and used their feedback to create better features and data to train AI models, 提高准确性和可靠性.
- Built fine-grained visual classification models using a combination of classification and metric learning techniques for improved accuracy and robustness.
- Constructed generative image models for image generation from a sketch by considering the user requirements for the style of the image.
- 使用卷积循环神经网络从图像中执行文本识别.
- 开发了一种用于服装检测的物体检测模型.
- Created prototypes for anomaly detection in a surveillance camera video feed using unsupervised techniques.
- 为人群设计的原型，依靠街头摄像头的视频.
技术:Scikit-learn, PyTorch, Keras，人工智能, 自然语言处理(NLP), Hugging Face, Fine-tuning, 图形处理器(GPU), Data Science, Computer Vision, Deep Learning, Machine Learning, Algorithms, Data Scientist, Linux, Python 2, Python 3, Git, Python, XGBoost, Pandas, Programming, Integration, User Interface (UI), Models, Cloud, OpenCV, AI Programming, Research, 卷积神经网络(CNN), Image Processing, Image Analysis, 大型语言模型(llm), Data Analysis
AI/ML顾问|生成式AI
2023 - 2023
Vyby Inc
- 将业务用例转换为构建MVP的技术问题陈述.
- 使用基于ChatGPT创建的文本提示的稳定扩散生成图像.
- Converted the generated image into a video rendering with 3D photography using context-aware layered depth in-painting.
- 使用Stable Diffusion AUTOMATIC1111 web UI的API功能构建后端API.
Technologies: 人工智能(AI)，稳定扩散，谷歌云平台(GCP), 自然语言处理(NLP), Hugging Face, Deep Learning, Machine Learning, Computer Vision, Linux, Python 2, Python 3, Git, Python, 图形处理器(GPU), Data Scientist, Pandas, Programming, Integration, Models, Cloud, OpenCV, AI Programming, 卷积神经网络(CNN), Image Processing, 大型语言模型(llm), Data Analysis
AI/ML顾问|模型精度提升 & Deployment
2022 - 2023
Photograde
- Improved ML models' accuracy to be at par or better than competitors by interacting with domain experts and designing and implementing experiments to select the right model training features.
- Designed and implemented asynchronous APIs using FastAPI and a job management system using Redis and job queues to scale the model deployment for concurrent use by multiple users.
- Deployed the model in production and developed back-end Flask REST API interfaces to interact with the model.
- Developed back-end Flask REST API interfaces to train a model to learn a user's photo editing style.
技术:Scikit-learn，深度学习，计算机视觉，Redis, FastAPI, Flask, REST api, 谷歌云平台(GCP), Python-rq, Machine Learning, 人工智能(AI), MySQL, Data Scientist, Linux, Python 2, Python 3, DevOps, Git, Python, Fine-tuning, 图形处理器(GPU), XGBoost, Pandas, Programming, Integration, User Interface (UI), Models, Cloud, OpenCV, AI Programming, Data Visualization, 卷积神经网络(CNN), Image Processing, Image Analysis, Data Analysis
AI/ML Consultant | R&D数量金融
2020 - 2022
AlphaBeta
- Implemented several academic research papers from scratch and trained and back-tested machine learning models like transformers, time series models, CNN models, random forest, 以及金融数据上的梯度增强树.
- 开发了计算各种技术和基本金融变量的代码.
- Wrote code for extracting and post-processing market and earnings data from finance databases.
技术:人工智能(AI), Machine Learning, Scikit-learn, TensorFlow, PyTorch, XGBoost, Keras, MySQL, Data Scientist, Linux, Python 2, Python 3, Git, Python, Fine-tuning, 图形处理器(GPU), Pandas, Programming, Models, AI Programming, Research, Data Visualization, Data Analysis
AI/ML Consultant | R&D Computer Vision
2017 - 2021
Streamoid技术
- Developed highly accurate object detection models for fashion categories by correcting for data biases. Built object detection models for small objects and worked on optimizations to improve inference speed.
- Improved the accuracy of fashion attribute classification models through careful analysis of model weaknesses, 试验更好的技术, 消除数据偏差.
- Generated pixel-level annotated data using traditional computer vision algorithms and trained semantic segmentation models. Improved the accuracy of semantic segmentation models by analyzing annotation errors and correcting them.
- Designed and developed a custom color classification CNN network using a pixel-voting scheme to detect the dominant color in fashion apparel in highly noisy images.
技术:PyTorch, TensorFlow, Scikit-learn, Keras, Deep Learning, Computer Vision, Linux, Python 2, Python 3, Git, Python, Fine-tuning, 图形处理器(GPU), XGBoost, Data Scientist, Pandas, Programming, Integration, User Interface (UI), Models, Cloud, OpenCV, AI Programming, Research, Bitbucket, Data Visualization, 卷积神经网络(CNN), Image Processing, Image Analysis, Data Analysis
AI/ML Consultant | R&D Computer Vision
2015 - 2018
Uncanny Vision
- Created and implemented several experiments for developing an anomaly detection model for video surveillance use cases using autoencoder neural networks and one-class classification methods.
- Improved the human pose detection model's accuracy by debugging the convergence issues during model training.
- Developed an automatic number-plate recognition model for very challenging vehicle number-plate recognition scenarios. Used computer vision and computer graphics techniques to synthetically generate vehicle number-plate training data.
- 设计并开发了一个使用目标检测读取模拟仪表值的系统, segmentation, 以及数字识别模型.
- 训练使用CNN和LSTM模型识别图像中的文本的模型.
技术:深度学习, 人工智能(AI), Machine Learning, Python 3, Python, Caffe, TensorFlow, Keras, PyTorch, Scikit-learn, Computer Vision, Linux, Git, Fine-tuning, 图形处理器(GPU), Data Scientist, Programming, Models, Cloud, OpenCV, AI Programming, Research, Bitbucket, Data Visualization, 卷积神经网络(CNN), Image Processing, Image Analysis, Data Analysis
Computer Scientist
2015 - 2015
Adobe
- 研究了使用拓扑方法进行数据分析.
- 探索了Adobe数字营销组合的研究用例.
- Designed a topic modeling system to understand user behavior and engagement from their mobile phone usage.
Technologies: Python, Data Science, 人工智能(AI), Computer Vision, Deep Learning, Machine Learning, Algorithms, Git, Scikit-learn, Data Scientist, Programming, Data Visualization
技术人员
2006 - 2007
NetApp
- 设计并实现虚拟磁带库的重复数据删除模块.
- 开发了一个概念验证，以显示重复数据删除的有效性.
- 维护内容管理模块的后端代码.
技术:c++、C、数据科学、人工智能、算法、编程
高级软件工程师
2002 - 2006
Philips
- Designed and implemented enhancements for the workflow management of cardiovascular intervention software.
- 创建并开发了一个内存管理模块，用于高效的图像存储和检索.
- 构建患者数据库的导入和导出模块.
- Performed the onsite system integration and testing at Philips Medical Systems, Netherlands.
- 在软件发布之前，生成测试用例并测试不同的模块.
技术:c++， C，算法，编程
Experience
监控视频馈送中的异常检测
This project reviewed different techniques for detecting anomalies in videos using unsupervised machine learning techniques. 我们探索了重建的使用, predictive, 以及这个项目中基于深度学习的生成模型.
These reconstruction-based models build representations that minimize the reconstruction error of training samples from the normal distribution. Spatio-temporal predictive models consider the correlation by viewing videos as a spatiotemporal time series and learning representations that minimize the prediction error on spatiotemporal sequences. The generative models learn to generate samples from the training distribution while minimizing the reconstruction error and the distance between generated and trained distribution. Each of these methods focuses on learning prior information useful for constructing the representation for the video anomaly detection task.
These reconstruction-based models build representations that minimize the reconstruction error of training samples from the normal distribution. Spatio-temporal predictive models consider the correlation by viewing videos as a spatiotemporal time series and learning representations that minimize the prediction error on spatiotemporal sequences. The generative models learn to generate samples from the training distribution while minimizing the reconstruction error and the distance between generated and trained distribution. Each of these methods focuses on learning prior information useful for constructing the representation for the video anomaly detection task.
Education
2009 - 2015
Ph.D. 计算机科学与工程硕士
印度科学研究所-班加罗尔，印度
2007 - 2009
计算机科学与工程硕士学位
印度科学研究所-班加罗尔，印度
1998 - 2002
计算机科学与工程学士学位
印度卡利卡特国立理工学院
Skills
Libraries/APIs
Keras, Scikit-learn, Pandas, OpenCV, PyTorch, TensorFlow, VTK, REST APIs, XGBoost, Python-rq
Tools
Bitbucket, Git
Languages
Python, Python 3, Python 2, c++， C
Paradigms
Data Science, DevOps
Storage
Redis, MySQL
Platforms
Linux, Ubuntu, 谷歌云平台(GCP)
Frameworks
Caffe, Flask
Other
Machine Learning, Deep Learning, Computer Vision, 人工智能(AI), Algorithms, 自然语言处理(NLP), Fine-tuning, 图形处理器(GPU), Programming, Models, AI Programming, Research, 卷积神经网络(CNN), Image Processing, Data Analysis, 计算拓扑, 科学数据分析, Hugging Face, Integration, Cloud, Data Visualization, Image Analysis, 大型语言模型(llm), 计算几何, Stable Diffusion, FastAPI, Data Scientist, Machine Language, User Interface (UI)
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