Review — CB Loss: Class-Balanced Loss Based on Effective Number of Samples (Image Classification)

Original Source Here Review — CB Loss: Class-Balanced Loss Based on Effective Number of Samples (Image Classification) Using the Effective Number of Samples for Each Class to Re-Balance the Loss, Outperforms Focal Loss in RetinaNet In this paper, Class-Balanced Loss Based on Effective Number of Samples, (CB Loss), by Cornell University, Cornell Tech, Google Brain,Continue reading “Review — CB Loss: Class-Balanced Loss Based on Effective Number of Samples (Image Classification)”

3 Common Problems with Neural Network Initialisation

https://cdn-images-1.medium.com/max/2600/0*lspTcI9IOJOurp5w Original Source Here Too-small Initialisation — Vanishing Gradient Why does it happen: Initialised weights of a neural network are too small Result: Premature convergence Symptoms: Model performance improves very slowly during training. The training process is also likely to stop very early. If the initial weights of a neuron are too small relative toContinue reading “3 Common Problems with Neural Network Initialisation”

Image Classification with Transfer Learning

https://cdn-images-1.medium.com/max/725/0*lRg7a-f_bIh5A81j Original Source Here Image Classification with Transfer Learning Transfer learning is repurposing a pre-trained model for another but similar usage. This method is seen in various machine learning applications especially in situations where the dataset is relatively small. In this project, I built an image classification model from scratch using transfer learning. When IContinue reading “Image Classification with Transfer Learning”

3 Common Problems with Neural Network Initialisation

https://cdn-images-1.medium.com/max/2600/0*lspTcI9IOJOurp5w Original Source Here Too-small Initialisation — Vanishing Gradient Why does it happen: Initialised weights of a neural network are too small Result: Premature convergence Symptoms: Model performance improves very slowly during training. The training process is also likely to stop very early. If the initial weights of a neuron are too small relative toContinue reading “3 Common Problems with Neural Network Initialisation”

Linear Regression in MachineLearning

Original Source Here Linear Regression in MachineLearning Machine Learning problems always deals with the datasets itself . Real world Data are need some feature Engineering techniques which may contain null values and categorical variables as well. This can arises due to human error or it can be from sensor itself. But we can also obtainedContinue reading “Linear Regression in MachineLearning”

A Straightforward Guide to Cleaning and Preparing Data in Python

https://cdn-images-1.medium.com/max/2600/0*x7WkyWCBkQcsQYkY Original Source Here A Straightforward Guide to Cleaning and Preparing Data in Python How to Identify and deal with dirty data. Photo by jesse orrico on Unsplash Real-world data is dirty. In fact, around 80% of a data scientist’s time is spent collecting, cleaning and preparing data. These tedious (but necessary) steps make theContinue reading “A Straightforward Guide to Cleaning and Preparing Data in Python”

4 Improvements For Your Data Science Resume

Original Source Here Table of Contents Introduction Highlight Projects Style of Writing Style and Format of Resume Mission Statement Summary References Introduction A resume is something that always seems like it can be improved upon, however, the most important thing for you to do is understand advice and apply it in your special way. WhatContinue reading “4 Improvements For Your Data Science Resume”

Master Thesis

Original Source Here The Serious Business of Humour Detection in Political News using Sentence Embeddings and Deep Learning There are some tacit assumptions about the importance of humour in communication science. Existing research is now beginning to provide automated determination of whether a text is funny or not, but with minimal differentiation according to degreeContinue reading “Master Thesis”

A Review of Artificial Intelligence Applications in Cardiac CT

Original Source Here A Review of Artificial Intelligence Applications in Cardiac CT Artificial Intelligence and Cardiology Artificial intelligence (AI) applications in cardiology have the potential to be groundbreaking, but which applications will actually help save lives and help people live longer? One major area that is being transformed is cardiac computed tomography (CT). Specifically, theContinue reading “A Review of Artificial Intelligence Applications in Cardiac CT”