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Frendy R.

@frendyrachman

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AI Engineer | Data Scientist | Prompt Engineer

$10 USD / orë
・
Flamuri i
Indonesia (12:39 p.d.)
・
U bashkua në tetor 17, 2024
$10 USD / orë
・
⭐️ Creator of the "Mobile Legends AI Highlight Auto Clipper" – Automating Gaming Highlights with YOLO ? Accomplished Data Scientist & AI Specialist | Expert in Machine Learning, Computer Vision, and Automation ? Passionate About Real-World AI Applications | From Gaming to Healthcare ? Contributor on Medium & LinkedIn – Sharing AI Innovations and Insights Greetings! I'm Frendy Rachman, a driven data scientist and AI enthusiast passionate about transforming complex problems into impactful solutions. With a proven track record in delivering cutting-edge machine learning models, I bring precision, creativity, and efficiency to every project. ?️ What I Do : I specialize in creating end-to-end AI solutions tailored to diverse needs. From object detection in medical imaging to video automation in gaming, I thrive on designing systems that drive innovation. My expertise spans: - Computer Vision : YOLO, object detection, segmentation, and automation. - Machine Learning : Predictive analytics, customer segmentation, and statistical modeling. - Data Science : Advanced analytics, visualization, and insights generation. - Automation : Streamlining workflows for efficiency using AI. ? Experience : I have successfully executed impactful projects like: - Mobile Legends AI Highlight Auto Clipper : Leveraging YOLO to detect key gaming UI elements and automate video clipping. - Brain MRI Tumor Detection : Enhancing diagnostic capabilities with machine learning. - RFM Segmentation for Transjakarta : Optimizing marketing strategies with data-driven customer insights. - Viscosity Measurement with Computer Vision : Revolutionizing industrial processes with YOLO-based solutions. ? Value Proposition : What sets me apart is my ability to seamlessly bridge technical expertise with business needs. I don’t just build models—I craft solutions that integrate smoothly into your ecosystem, driving measurable results. ? Why Work with Me : ? Innovation : Delivering AI solutions that go beyond expectations. ? Timely Delivery : Meeting deadlines without compromising quality. ? Results-Oriented : Focused on actionable insights and impact. ? Integration Expertise : Embedding AI seamlessly into workflows. Whether you’re looking to automate processes, gain deeper insights from data, or explore the potential of AI for your business, I’m here to help you achieve your goals. Let’s collaborate and turn your vision into reality. ? Looking forward to shaping the future together!
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Freelancer Python Developers Indonesia
Shikoni të gjitha Python Developers në Indonesia

Portofol

Portofol

12353741
This project involved fine-tuning the Llama 3.1 large language model (LLM) developed by Meta using a custom dataset consisting of 10,000 hadiths. The dataset was formatted as prompt-completion pairs, which allowed the model to be trained specifically on these Islamic teachings. By feeding the model these tailored prompts and their corresponding completions, the fine-tuning process aimed to enhance the model's ability to understand and generate responses bases on hadiths. This customization was intended to refine the model's performance in generating accurate and contextually relevant responses related to Islamic teachings.
Fine-Tuning Llama 3.1 with 10.000 Hadiths Dataset
This project involved fine-tuning the Llama 3.1 large language model (LLM) developed by Meta using a custom dataset consisting of 10,000 hadiths. The dataset was formatted as prompt-completion pairs, which allowed the model to be trained specifically on these Islamic teachings. By feeding the model these tailored prompts and their corresponding completions, the fine-tuning process aimed to enhance the model's ability to understand and generate responses bases on hadiths. This customization was intended to refine the model's performance in generating accurate and contextually relevant responses related to Islamic teachings.
Fine-Tuning Llama 3.1 with 10.000 Hadiths Dataset
This project involved fine-tuning the Llama 3.1 large language model (LLM) developed by Meta using a custom dataset consisting of 10,000 hadiths. The dataset was formatted as prompt-completion pairs, which allowed the model to be trained specifically on these Islamic teachings. By feeding the model these tailored prompts and their corresponding completions, the fine-tuning process aimed to enhance the model's ability to understand and generate responses bases on hadiths. This customization was intended to refine the model's performance in generating accurate and contextually relevant responses related to Islamic teachings.
Fine-Tuning Llama 3.1 with 10.000 Hadiths Dataset
12353722
This study found a new way to measure fluid viscosity using video processing and deep learning. Instead of using traditional method, it used a simple camera and a special algorithm called YOLO model. The model trained to detect the movement of a falling metal ball on a fluid taken with a smartphone camera. The results showed that this method is faster than traditional methods. This could be useful in industries and labs, and there's a wide potential to make it even better.
Measurement of Liquid Viscosity Based on Video Processing us
This study found a new way to measure fluid viscosity using video processing and deep learning. Instead of using traditional method, it used a simple camera and a special algorithm called YOLO model. The model trained to detect the movement of a falling metal ball on a fluid taken with a smartphone camera. The results showed that this method is faster than traditional methods. This could be useful in industries and labs, and there's a wide potential to make it even better.
Measurement of Liquid Viscosity Based on Video Processing us
This study found a new way to measure fluid viscosity using video processing and deep learning. Instead of using traditional method, it used a simple camera and a special algorithm called YOLO model. The model trained to detect the movement of a falling metal ball on a fluid taken with a smartphone camera. The results showed that this method is faster than traditional methods. This could be useful in industries and labs, and there's a wide potential to make it even better.
Measurement of Liquid Viscosity Based on Video Processing us
12353627
The "Brain MRI Tumor Detection" project harnesses the power of deep learning techniques to improve the early and accurate identification of brain tumors from MRI scans. By utilizing advanced neural network architectures, such as convolutional neural networks (CNNs), the project processes and analyzes high-resolution MRI images to detect abnormal growths indicative of tumors. This approach significantly enhances the diagnostic capabilities of medical professionals, offering a reliable tool for distinguishing between benign and malignant tumors with greater precision. The integration of deep learning not only accelerates the diagnostic process but also provides a valuable support system for radiologists, contributing to better patient outcomes through early intervention and more targeted treatment strategies.
Brain MRI Tumor Detection with Convolutional Neural Network
The "Brain MRI Tumor Detection" project harnesses the power of deep learning techniques to improve the early and accurate identification of brain tumors from MRI scans. By utilizing advanced neural network architectures, such as convolutional neural networks (CNNs), the project processes and analyzes high-resolution MRI images to detect abnormal growths indicative of tumors. This approach significantly enhances the diagnostic capabilities of medical professionals, offering a reliable tool for distinguishing between benign and malignant tumors with greater precision. The integration of deep learning not only accelerates the diagnostic process but also provides a valuable support system for radiologists, contributing to better patient outcomes through early intervention and more targeted treatment strategies.
Brain MRI Tumor Detection with Convolutional Neural Network
The "Brain MRI Tumor Detection" project harnesses the power of deep learning techniques to improve the early and accurate identification of brain tumors from MRI scans. By utilizing advanced neural network architectures, such as convolutional neural networks (CNNs), the project processes and analyzes high-resolution MRI images to detect abnormal growths indicative of tumors. This approach significantly enhances the diagnostic capabilities of medical professionals, offering a reliable tool for distinguishing between benign and malignant tumors with greater precision. The integration of deep learning not only accelerates the diagnostic process but also provides a valuable support system for radiologists, contributing to better patient outcomes through early intervention and more targeted treatment strategies.
Brain MRI Tumor Detection with Convolutional Neural Network
The "Brain MRI Tumor Detection" project harnesses the power of deep learning techniques to improve the early and accurate identification of brain tumors from MRI scans. By utilizing advanced neural network architectures, such as convolutional neural networks (CNNs), the project processes and analyzes high-resolution MRI images to detect abnormal growths indicative of tumors. This approach significantly enhances the diagnostic capabilities of medical professionals, offering a reliable tool for distinguishing between benign and malignant tumors with greater precision. The integration of deep learning not only accelerates the diagnostic process but also provides a valuable support system for radiologists, contributing to better patient outcomes through early intervention and more targeted treatment strategies.
Brain MRI Tumor Detection with Convolutional Neural Network
12353741
This project involved fine-tuning the Llama 3.1 large language model (LLM) developed by Meta using a custom dataset consisting of 10,000 hadiths. The dataset was formatted as prompt-completion pairs, which allowed the model to be trained specifically on these Islamic teachings. By feeding the model these tailored prompts and their corresponding completions, the fine-tuning process aimed to enhance the model's ability to understand and generate responses bases on hadiths. This customization was intended to refine the model's performance in generating accurate and contextually relevant responses related to Islamic teachings.
Fine-Tuning Llama 3.1 with 10.000 Hadiths Dataset
This project involved fine-tuning the Llama 3.1 large language model (LLM) developed by Meta using a custom dataset consisting of 10,000 hadiths. The dataset was formatted as prompt-completion pairs, which allowed the model to be trained specifically on these Islamic teachings. By feeding the model these tailored prompts and their corresponding completions, the fine-tuning process aimed to enhance the model's ability to understand and generate responses bases on hadiths. This customization was intended to refine the model's performance in generating accurate and contextually relevant responses related to Islamic teachings.
Fine-Tuning Llama 3.1 with 10.000 Hadiths Dataset
This project involved fine-tuning the Llama 3.1 large language model (LLM) developed by Meta using a custom dataset consisting of 10,000 hadiths. The dataset was formatted as prompt-completion pairs, which allowed the model to be trained specifically on these Islamic teachings. By feeding the model these tailored prompts and their corresponding completions, the fine-tuning process aimed to enhance the model's ability to understand and generate responses bases on hadiths. This customization was intended to refine the model's performance in generating accurate and contextually relevant responses related to Islamic teachings.
Fine-Tuning Llama 3.1 with 10.000 Hadiths Dataset
12353722
This study found a new way to measure fluid viscosity using video processing and deep learning. Instead of using traditional method, it used a simple camera and a special algorithm called YOLO model. The model trained to detect the movement of a falling metal ball on a fluid taken with a smartphone camera. The results showed that this method is faster than traditional methods. This could be useful in industries and labs, and there's a wide potential to make it even better.
Measurement of Liquid Viscosity Based on Video Processing us
This study found a new way to measure fluid viscosity using video processing and deep learning. Instead of using traditional method, it used a simple camera and a special algorithm called YOLO model. The model trained to detect the movement of a falling metal ball on a fluid taken with a smartphone camera. The results showed that this method is faster than traditional methods. This could be useful in industries and labs, and there's a wide potential to make it even better.
Measurement of Liquid Viscosity Based on Video Processing us
This study found a new way to measure fluid viscosity using video processing and deep learning. Instead of using traditional method, it used a simple camera and a special algorithm called YOLO model. The model trained to detect the movement of a falling metal ball on a fluid taken with a smartphone camera. The results showed that this method is faster than traditional methods. This could be useful in industries and labs, and there's a wide potential to make it even better.
Measurement of Liquid Viscosity Based on Video Processing us
12353627
The "Brain MRI Tumor Detection" project harnesses the power of deep learning techniques to improve the early and accurate identification of brain tumors from MRI scans. By utilizing advanced neural network architectures, such as convolutional neural networks (CNNs), the project processes and analyzes high-resolution MRI images to detect abnormal growths indicative of tumors. This approach significantly enhances the diagnostic capabilities of medical professionals, offering a reliable tool for distinguishing between benign and malignant tumors with greater precision. The integration of deep learning not only accelerates the diagnostic process but also provides a valuable support system for radiologists, contributing to better patient outcomes through early intervention and more targeted treatment strategies.
Brain MRI Tumor Detection with Convolutional Neural Network
The "Brain MRI Tumor Detection" project harnesses the power of deep learning techniques to improve the early and accurate identification of brain tumors from MRI scans. By utilizing advanced neural network architectures, such as convolutional neural networks (CNNs), the project processes and analyzes high-resolution MRI images to detect abnormal growths indicative of tumors. This approach significantly enhances the diagnostic capabilities of medical professionals, offering a reliable tool for distinguishing between benign and malignant tumors with greater precision. The integration of deep learning not only accelerates the diagnostic process but also provides a valuable support system for radiologists, contributing to better patient outcomes through early intervention and more targeted treatment strategies.
Brain MRI Tumor Detection with Convolutional Neural Network
The "Brain MRI Tumor Detection" project harnesses the power of deep learning techniques to improve the early and accurate identification of brain tumors from MRI scans. By utilizing advanced neural network architectures, such as convolutional neural networks (CNNs), the project processes and analyzes high-resolution MRI images to detect abnormal growths indicative of tumors. This approach significantly enhances the diagnostic capabilities of medical professionals, offering a reliable tool for distinguishing between benign and malignant tumors with greater precision. The integration of deep learning not only accelerates the diagnostic process but also provides a valuable support system for radiologists, contributing to better patient outcomes through early intervention and more targeted treatment strategies.
Brain MRI Tumor Detection with Convolutional Neural Network
The "Brain MRI Tumor Detection" project harnesses the power of deep learning techniques to improve the early and accurate identification of brain tumors from MRI scans. By utilizing advanced neural network architectures, such as convolutional neural networks (CNNs), the project processes and analyzes high-resolution MRI images to detect abnormal growths indicative of tumors. This approach significantly enhances the diagnostic capabilities of medical professionals, offering a reliable tool for distinguishing between benign and malignant tumors with greater precision. The integration of deep learning not only accelerates the diagnostic process but also provides a valuable support system for radiologists, contributing to better patient outcomes through early intervention and more targeted treatment strategies.
Brain MRI Tumor Detection with Convolutional Neural Network
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