Soham Nandi

Computer Science Engineering, specializing in Artificial Intelligence and Machine learning

Soham Nandi is a technical writer based in Memari, India. His academic background is in Computer Science Engineering, specializing in Artificial Intelligence and Machine learning. He has extensive experience in Data Analytics, Machine Learning, and Python. He has worked on group projects that required the implementation of Computer Vision, Image Classification, and App Development.

Soham's passion for AI and Machine Learning propels him toward his goal of becoming a distinguished Machine Learning Engineer and leveraging his knowledge to help the less fortunate. His projects include the Rice grain classifier and Handwriting-based Personality Detector, and he is certified in Data Analytics and Data Science.

Drawing inspiration from writers ranging from Satyajit Ray to Mark Manson, he has his own science fiction stories and blog. He also creates content on personal development and mental health through his Instagram handle. Beyond the screen and code, Soham's world is filled with music. With the ability to play the guitar, ukulele, and drums, he rocks to the tunes of the metal and rock genres, using music as a creative outlet, planning to start a band of his own.

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Articles from Soham

How Is Generative AI Redefining Trust?

Researchers propose revisions to trust models, highlighting the complexities introduced by generative AI chatbots and the critical role of developers and training data.

9 Sep 2024

AI Training Data Faces Growing Restrictions As Web Consent Tightens

Research presents a comprehensive audit of web consent protocols, revealing a rapid increase in restrictions on AI training data, significantly impacting data availability and diversity.

8 Sep 2024

Meta Researchers Animate Children's Drawings With Innovative Twisted Perspective Technique

Researchers developed an automated system that animates children's drawings by addressing unique artistic styles, supported by a large, annotated dataset of over 178,000 images.

5 Sep 2024

Aleph Alpha's Pharia-1-LLM-7B Models Revolutionize Multilingual AI for Domain-Specific Tasks

Aleph Alpha has introduced the Pharia-1-LLM-7B models, optimized for concise, multilingual responses with domain-specific applications in automotive and engineering. The models include safety features and are available for non-commercial research.

4 Sep 2024

Google Hints at AI-Driven Future as Gemini May Soon Transform Android Auto Experience

Google's next-generation AI assistant, Gemini, is poised to transform the driving experience by integrating advanced AI capabilities directly into Android Auto, potentially replacing Google Assistant with a more interactive and intuitive in-car system.

4 Sep 2024

Reinforcing Gaming AI with Learning Models

Reinforcement learning is transforming gaming by enabling AI to adapt and learn, creating dynamic experiences that enhance player engagement and satisfaction.

3 Sep 2024

Using Machine Learning to Identify Suicide Risks

Researchers developed a machine learning model to detect high-risk suicide users in online forums by analyzing social interaction patterns. The study showed that specific network features could predict suicidal behavior, underscoring the value of network-based analysis in suicide prevention.

2 Sep 2024

Machine Learning Optimizes Polymer Analysis

Researchers integrated a convolutional neural network with broadband dielectric spectroscopy to predict the electrical equivalent circuit (EEC) topology of polymer membranes. This method reduces user bias, enhancing the accuracy and efficiency of polymer analysis in renewable energy applications.

30 Aug 2024

Hybrid Deep Learning Optimizes Renewable Power Flow

This study introduces HDRL-QIGA, a hybrid deep learning model combining reinforcement learning and quantum-inspired algorithms to optimize power flow in renewable energy systems. The model outperforms traditional methods, reducing fuel costs and power losses while ensuring voltage stability.

28 Aug 2024

Symmetry-Breaking Detection Enhances Object Recognition

The novel SBDet model introduces a relaxed rotation-equivariant network (R2Net) that improves object detection in scenarios with symmetry-breaking or non-rigid transformations. This innovation offers greater accuracy and robustness in real-world visual tasks like autonomous driving and geosciences.

28 Aug 2024