"I've participated in several competitions in Kaggle concerning medical images. My most recent competition deals with images of skin lesions and classifying them as either melanoma or not. I focused on fine-tuning pretrained models and ensembling them.
I also like to keep track of the latest trends of computer vision research, with a focus on making models memory-efficient through model compression and interpretability."
Xuelong A. - "I've participated in several competitions in Kaggle concerning medical images. My most recent competition deals with images of skin lesions and classifying them as either melanoma or not. I focused on fine-tuning pretrained models and ensembling them.
I also like to keep track of the latest trends of computer vision research, with a focus on making models memory-efficient through model compression and interpretability."See full answer
"TF-IDF CONCEPT EXPLANATION AND INTUITION BUILDING:
TF-IDF is a measure that reflects the importance of a word in the document relative to a collection of documents. Its full form is Term Frequency - Inverse Document Frequency.
The term TF indicates how often a term occurs in a particular document. It is the ratio of count of a particular term in a document to the number of terms in that particular document. So, the intuition is that if a term occurs frequently in a single documen"
Satyam C. - "TF-IDF CONCEPT EXPLANATION AND INTUITION BUILDING:
TF-IDF is a measure that reflects the importance of a word in the document relative to a collection of documents. Its full form is Term Frequency - Inverse Document Frequency.
The term TF indicates how often a term occurs in a particular document. It is the ratio of count of a particular term in a document to the number of terms in that particular document. So, the intuition is that if a term occurs frequently in a single documen"See full answer