Deep Learning models are great at solving extremely complex tasks efficiently but this superpower comes at a cost. Due to a large number of parameters, these models are typically big in size(memory footprint) and also slow in the inference (during predictions). Slow and heavy models are not much appreciated when it comes to the deployment part. As we… Read More »
Data is like a fuel to a Data Scientist. Any study or research work requires a good amount of quality data. The term ‘good amount of quality data’ changes with the kind of study one wants to do. Various sampling techniques are there to get you just that. As a researcher, you may want to study-different animals, changing… Read More »
If you have some experience in solving the classification problems then you must have encountered imbalanced data several times. An imbalanced dataset is a dataset that has an imbalanced distribution of the examples of different classes. Consider a binary classification problem where you have two classes 1 and 0 and suppose more than 90% of your training examples… Read More »
Ensemble learning techniques are quite popular in machine learning. These techniques work by training multiple models and combining their results to get the best possible outcome. In this article, we will learn about three popular ensemble learning methods-bagging, boosting, and stacking. Each one of these methods has its own benefits and limitations, but in practice ensemble methods often… Read More »
As data scientists, we come across numerous classification problems every once in a while. Ensemble learning techniques like bagging and boosting typically give us quite high classification performances. But all such models are much complex and hard to interpret. To make sure that everything is working fine and also to understand the prediction results/logic better, it becomes necessary… Read More »
OpenCV-AI-toolkit was first introduced nearly 20 years ago(in 1999) by Intel Research and it is getting richer and better every year since then. OpenCV was primarily written in C++ language but has bindings for Python, Java, and MATLAB that makes it easy to integrate into different ML/AI projects. You will find almost every Computer Vision(Computer Graphics based) project… Read More »
Deep Learning is part of the Machine Learning family that deals with creating the Artificial Neural Network (ANN) based models. ANNs are used for both supervised as well as unsupervised learning tasks. Deep Learning is extensively used in tasks like-object detection, language translations, speech recognition, face detection, and recognition..etc. Let’s create our First Neural Network with PyTorch- In… Read More »
Overview PyTorch is a deep learning framework developed by Facebook’s AI Research lab(FAIR) about four years ago (in 2016). This PyTorch framework was designed to make our machine learning and deep learning project journey super fast and smooth. Pytorch is written in Python, C++, and CUDA and is supported across Linux, macOS, and Windows platforms. It also has… Read More »
An intuitive introduction to Reinforcement Learning Reinforcement Learning(RL) is a very interesting sub-field of Machine Learning(ML). While other ML techniques rely on static input-output pairs to learn the hidden rules and then apply those rules on the unseen data to get the possible outcomes. A Reinforcement Learning algorithm tends to learn the best decisions automatically over time. RL… Read More »
An introduction to audio data analysis (sound analysis) and Speech Recognition using python Overview A huge amount of audio data is being generated every day in almost every organization. Audio data yields substantial strategic insights when it is easily accessible to the data scientists for fuelling AI engines and analytics. Organizations that have already realized the power and… Read More »