What Is Monte Carlo Simulation

1 mainThere 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, we can’t accurately predict a future outcome because of inaccurate modeling. This becomes especially relevant when we are dealing with systems where the degrees of freedom are dependent on each other. An example would be movement of fluids or kinetic modeling of gases. How do we compute the possible outcomes? How can we assess the impact of all the free variables to make sure we predict the outcome under uncertainty?   Continue reading

Deep Learning For Smart Cities

1 mainIn 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 infrastructure like water, power, transport, and so on very effectively if they want to support everybody. But how do we do that? The data that is being collected varies so much quality and format that it becomes very difficult to use it effectively. How can we effectively use the data being collected by connected sensors?   Continue reading

Understanding The Industrial IoT Technology Stack

1 mainInternet 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 on some of the most pressing problems facing humanity like water shortage, energy consumption, infrastructure management, and so on. When we apply IoT methodologies to the industrial world, it is called Industrial IoT. There has been a lot of discussion as to what exactly is it. Is it a technology? Is it a collection of things? More importantly, there has been a lot of misinformation around it. Let’s go ahead and dissect it, shall we?   Continue reading

Deep Learning For Sequential Data – Part V: Handling Long Term Temporal Dependencies

1 mainIn 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 can just unroll an RNN and make it into a feedforward neural network, then what’s so special about the RNN in the first place? Let’s see how we tackle these issues.   Continue reading

Deep Learning For Sequential Data – Part IV: Training Recurrent Neural Networks

1 mainIn 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 inferencing. The training process needs to be trackable and it should converge in a reasonable amount of time. So how do we train RNNs? Can we just use the regular techniques that are used for feedforward neural networks?   Continue reading

Deep Learning For Sequential Data – Part III: What Are Recurrent Neural Networks

1 mainIn 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 biggest restrictions of Convolutional Neural Networks is that they force us to operate on fixed size input data. RNNs know how to operate on sequences of data, which is exciting because it opens up a lot of possibilities! What are RNNs? How do we construct them?   Continue reading

Deep Learning For Sequential Data – Part II: Constraints Of Traditional Approaches

1 mainIn 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 many tasks such as speech recognition, gesture recognition, part-of-speech tagging, and so on. But HMMs place a lot of restrictions as to how we can model our data. HMMs are definitely better than using classical machine learning techniques, but they don’t fully cover the needs of all the modern data analysis. This is because of the constraints that are used to build HMMs. What are those constraints?   Continue reading