Even though it has an ornate name, the underlying concept is actually quite simple and intuitive. The concept of Empirical Risk Minimization becomes relevant in the world of supervised learning. The actual goal of supervised learning is to find a model that solves a problem as opposed to finding a model that best fits the … Continue reading What is Empirical Risk Minimization
Category: Machine Learning
Measuring the Stability of Machine Learning Algorithms
When you think of a machine learning algorithm, the first metric that comes to mind is its accuracy. A lot of research is centered on developing algorithms that are accurate and can predict the outcome with a high degree of confidence. During the training process, an important issue to think about is the stability of … Continue reading Measuring the Stability of Machine Learning Algorithms
What Is Monte Carlo Simulation
There are many phenomena in everyday life where it's very difficult to model the problem. There are so many variables and so many dependencies that any approximation or assumption would lead to a huge errors in outputs. This is usually a combination of uncertainty and variability. Even though we have access to all the historical information, … Continue reading What Is Monte Carlo Simulation
Deep Learning For Smart Cities
In recent years, technological advancements in hardware, software, and embedded systems are enabling billions of smart devices to be connected to the internet. This ecosystem is collectively referred to as Internet of Things. A lot of people are actively migrating to cities, which means the essential resources are going to get scarcer. Cities will have to manage … Continue reading Deep Learning For Smart Cities
Understanding The Industrial IoT Technology Stack
Internet of Things (IoT) has emerged as one of the hottest trends in the technology world. It has the potential to radically change the way we experience life. It will particularly have a huge impact on the industrial world where we have to deal with massive machines, buildings, and open fields. Industrial technologies have direct impact … Continue reading Understanding The Industrial IoT Technology Stack
Deep Learning For Sequential Data – Part V: Handling Long Term Temporal Dependencies
In the previous blog post, we learnt why we cannot use regular backpropagation to train a Recurrent Neural Network (RNN). We discussed how we can use backpropagation through time to train an RNN. The next step is to understand how exactly the RNN can be trained. Does the unrolling strategy work in practice? If we … Continue reading Deep Learning For Sequential Data – Part V: Handling Long Term Temporal Dependencies
Deep Learning For Sequential Data – Part IV: Training Recurrent Neural Networks
In the previous blog post, we learnt how Recurrent Neural Networks (RNNs) can be used to build deep learning models for sequential data. Building a deep learning model involves many steps, and the training process is an important step. We should be able to train a model in a robust way in order to use it for … Continue reading Deep Learning For Sequential Data – Part IV: Training Recurrent Neural Networks
Deep Learning For Sequential Data – Part III: What Are Recurrent Neural Networks
In the previous two blog posts, we discussed why Hidden Markov Models and Feedforward Neural Networks are restrictive. If we want to build a good sequential data model, we should give more freedom to our learning model to understand the underlying patterns. This is where Recurrent Neural Networks (RNNs) come into picture. One of the … Continue reading Deep Learning For Sequential Data – Part III: What Are Recurrent Neural Networks
Deep Learning For Sequential Data – Part II: Constraints Of Traditional Approaches
In the previous blog post, we discussed the nature of sequential data and why we need a robust separate modeling technique to analyze that data. Traditionally, people have been using Hidden Markov Models (HMMs) to analyze sequential data, so we will center the discussion around HMMs in this blog post. HMMs have been implemented for … Continue reading Deep Learning For Sequential Data – Part II: Constraints Of Traditional Approaches
Deep Learning For Sequential Data – Part I: Why Do We Need It
Most of the current research on deep learning is focused on images. Deep learning is being actively applied to many areas, but image recognition is definitely generating a lot of buzz. Deep neural networks are being used for image classification tasks and they are able to outperform all the other approaches by a big margin. … Continue reading Deep Learning For Sequential Data – Part I: Why Do We Need It
Perpetual Enigma 