Ankitkumargautam/AdaBoost

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Created over 5 years ago
Updated over 5 years ago

Ankitkumargautam/AdaBoost

Ankitkumargautam

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Boosting (originally called hypothesis boosting) refers to any Ensemble method that can combine several weak learners into a strong learner. The general idea of most boosting methods is to train predictors sequentially, each trying to correct its predecessor

classify_cat_dog

This is a project about creating a flutter application that uses a pre-trained AI data model to do classification between cat and dog with the help of TensorFlow.

video-server

Image-Augmentation-master

Master all types of augmentation in image pre-procesing Useful in CNN research

Story

My little piece of work

emp

Object-Detection-using-Yolo

Used tensorflow, keras.  Used Darknet-53 to classify 80 classes  Used pre-trained weight for predictions in  Compared with RCNN, Fast RCNN  Pre-work done on SDD for object detection

story2

Facial-Recognition-via-CNN

This is a facial recognition method by using Convolutional Neural Network. In this project we can distinguish between images by extracting features of images

BREAST-CANCER-RISK-PREDICTION-

Breast cancer risks can be reduced via early detection of the disease; according to the American Cancer Society (2007) early detection of breast cancer risks can help reduce the possibility of mitigating the full growth of tumors

Jupyter Notebook

react-navbar

react-website

GradientBoosting

Just like AdaBoost, Gradient Boosting works by sequentially adding predictors to an ensemble, each one correcting its predecessor. However, instead of tweaking the instance weights at every iteration like AdaBoost does, this method tries to fit the new predictor to the residual errors made by the previous predictor.

Jupyter Notebook

Text-Analysis

Here in Text Analysis I have used Word Cloud generator. Word clouds or tag clouds are graphical representations of word frequency that give greater prominence to words that appear more frequently in a source text. The larger the word in the visual the more common the word was in the document(s). This type of visualization can assist evaluators with exploratory textual analysis by identifying words that frequently appear in a set of interviews, documents, or other text.

Jupyter Notebook

XGBoost

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.

Jupyter Notebook

Movies-Recommender-Systems

In this project we will focus on providing a basic recommendation system by suggesting items that are most similar to a particular item, in this case, movies.

Jupyter Notebook

prismats

This is full stack web application involving prisma, tailwind, typescript, postgreSQL and nextjs framework.

beststackweb

Fullstack App With TypeScript, PostgreSQL, Next.js, Prisma & GraphQL: Data Modeling