SHUBHAM SHANKAR's Projects
List of amazing AWS Services that can be utilized.π
Amazon Comprehend is a natural-language processing (NLP) service that uses machine learning to uncover information in unstructured data. The service can identify critical elements in data, including references to language, people, and places, and the text files can be categorized by relevant topics.
A simple Calculator built using Kotlin
A curated list of my Artificial Intelligence project.
Data augmentation is a technique of artificially increasing the training set by creating modified copies of a dataset using existing data. Here is a notebook of different augmentation techniques.
AWS SDK for Python (Boto3) to create, configure, and manage AWS services
Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) via a single API, along with a broad set of capabilities we need to build generative AI applications with security, privacy, and responsible AI.
This repository serves as a template for Python projects, providing a structure and examples to kickstart my development process
Blockchain is a decentralized, transparent, and secure digital ledger that records transactions across multiple computers. It uses cryptographic techniques to ensure immutability and integrity.
Built this react project based on one of my favorite shows of all time Breaking Bad.
CLIP is a multi-modal, zero-shot open-source paradigm. Without optimizing for a specific purpose, given a picture and text descriptions, the model can predict the best suitable text description for that image.
Cloud Computing Project - Web application created and hosted on different cloud platforms.
Wireshark Projects: Wireshark is a basic tool for observing the messages exchanged between executing protocol entities and is called a packet sniffer. My reading from the lab are available here.
Developed a covid tracker that tracks overall cases, recovered cases, and deaths across the globe.
From basics to building complex projects
In this project, I have designed and implemented a database for keeping track of information about a DBMS subsystem for managing discretionary access control.
Data Mining project : Built a classifier, trained a classifier, created clusters, performed 5-fold-cross-validation.
Computer science is the study of problems, problem-solving, and the solutions that come out of the problem-solving process. This repo contains solutions to algorithmic problems from various platforms like AlgoExpert, LeetCode, HackerRank, CodeSignal, etc.
Deep learning is a class of machine learning algorithms thatβ uses multiple layers to progressively extract higher-level features from the raw input.
Detectron2 is an open-source computer vision library by Facebook AI Research. It provides a flexible framework for training and deploying object detection models. Built on PyTorch, it offers modular components, efficient GPU utilization, and supports tasks like instance segmentation and keypoint detection.
Diffusion models are deep generative models that work by adding noise (Gaussian noise) to the available training data (also known as the forward diffusion process) and then reversing the process (known as denoising or the reverse diffusion process) to recover the data.
Upon sleep detection, the code will trigger notifications that include a combination of gentle vibrations on the watch and an escalating audio alert on the phone. This approach provides a multi-sensory wake-up cue to increase the user's chance of being roused from sleep.
Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object.
Welcome to the world of FastAPI, a sleek and high-performance web framework for constructing Python APIs.
This repository contains a variety of full stack projects that I've worked on. I've experimented with several technologies such as reactJs, nextJs etc.
Prompt engineering is a concept in AI[NLP]. Prompt engineering typically works by converting one or more tasks to a prompt-based dataset and training a language model with what has been called "prompt-based learning" or just "prompt learning".
This repository is my platform to learn, experiment, and innovate with LLMs. Here I try to dive in and discover diverse applications, research experiments, and projects fueled by the power of language models.
This repository is my platform to learn, experiment, and innovate with LLMs. Here I try to dive in and discover diverse applications, research experiments, and projects fueled by the power of language models.