Machine Learning

Before discussing Mel Spectrograms, we first need to understand what the Mel Scale is and why it is useful. The Mel Scale is a logarithmic transformation of a signal’s frequency. The core idea of this transformation is that sounds of equal distance on the Mel Scale are perceived to be of equal distance to humans. What does this mean?For example, most human beings can easily … [Read more...] about Learning from Audio: The Mel Scale, Mel Spectrograms, and Mel Frequency Cepstral Coefficients

The Machine Learning Bite Size Series is aimed at explaining the terms in a simple 1 minute read for a quick reference.Suppose you’re training a machine learning model and generating predictions, you compare the predicated value with the actual targets and generate a loss value, depending on the comparison of the output.The formula for computing the loss value for Hinge Loss … [Read more...] about Hinge Loss : [Machine Learning Bite Size Series]

The No Free Lunch Theorem is often thrown around in the field of optimization and machine learning, often with little understanding of what it means or implies. The theorem states that all optimization algorithms perform equally well when their performance is averaged across all possible problems. It implies that there is no single best optimization algorithm. Because of the … [Read more...] about No Free Lunch Theorem for Machine Learning

Algorithm is a set of rules to be followed when solving problems.Algorithm need to be programmed classify and process informationThe effeciency & Accuracy of the Algorithm are dependent on how well the algorithm was programmed.How ML different from Treditional programing ?Traditional Programming :Data program→ Computer→ OutputML:Data Output→ Computer→ ProgramHow does ML … [Read more...] about Introduction to Machine learning

본 논문이 발표되기까지의 몇 년 간 instance segmentation은 object detection으로부터 큰 발전이 있었습니다. Instance segmentation의 sota를 달성한 Mask R-CNN과 FCIS 경우는 object detection인 Faster R-CNN과 R-FCN으로부터 발전이 되었습니다. 하지만 이러한 방식들은 성능에만 치중을 한 나머지 SSD나 YOLO와 같은 real-time한 작업은 전혀 고려하지 않았습니다. 본 논문은 one-stage의 instance segmentation 모델을 통해 SSD나 YOLO와 같은 real-time한 작업이 가능한 방법을 제시합니다.YOLACT의 핵심 method는 Faster R-CNN에 … [Read more...] about YOLACT

I am from Jammu and Kashmir. I did my schooling from KC Public School and completed my BTech from Yogananda College of Engineering and Technology. My father is a businessman and my mother is a housewife and we have a joint family.One of my great personality traits is that I have acquired a leadership quality. In the future, I am confident that I will be able to handle and … [Read more...] about Small town, big dreams — my success story into the field of data science!

Once your machine learning model is ready, you have to deploy it to a device. One of the ways that can be done is by shipping the model with the application. A challenge with this method is that whenever your model changes, you will need to ship a new APK to the app stores. Obviously, this takes a long time because every app update needs to be verified by the app store. Now, … [Read more...] about Custom TensorFlow Lite model on Android using Firebase ML

A Quantum Machine Learning AlgorithmThe Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm. It aims to find an upper bound of the lowest eigenvalue of a given Hamiltonian.If you’re not a physicist, your most appropriate reply is: “what?!”Fortunately, you don’t need a physicist to understand quantum machine learning. So, let me rephrase.The VQE is an … [Read more...] about The Variational Quantum Eigensolver — Explained

It has more than two centuries but has become the most used Machine Learning algorithmsBayes Theorem allows anyone, in a deceptively simple manner, to calculate a conditional probability where intuition often fails. You’ve might bump into this theorem in Machine Learning when dealing with Maximum a Posteriori (MAP) — a probability framework for fitting a model to a training … [Read more...] about Bayes Theorem

The Deep Learning term is becoming more popular as an appealing phrase to get public attention to deep tech and VC investment as we can see some investment firms if not many actively utilize it to intrigue their investors and readers.Not surprisingly, recent breakthroughs in AI happened due to further advancement of deep learning research and the introduction of massive neural … [Read more...] about Deep Learning Could Create More Economic Value Than The Internet Did