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
Author: Prateek Joshi
VC at Moxxie Ventures. Founder/CEO of Plutoshift. Author of 13 books on ML. Featured on Forbes, NBC, Bloomberg, CNBC, TechCrunch, Silicon Valley Business Journal, and more. You can visit prateekj.com to learn more.
Undestanding IoT Gateways
The Internet of Things (IoT) ecosystem is rapidly expanding. Some analysts predict that there will be around 50 billion connected devices by 2020. If you are new to IoT, it refers to the collective ecosystem of devices that are connected to the internet. These devices can be sensors, actuators, health monitors, meters, and so on. … Continue reading Undestanding IoT Gateways
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
Estimating The Predictability Of Time Series Data – Part II
In the previous blog post, we discussed various types of time series data. We understood the concepts of stationarity and shocks. In this blog post, we will continue to discuss how we can estimate the predictability of time series data. People say that future is unpredictable. But that's grossly reductive! What they actually mean to … Continue reading Estimating The Predictability Of Time Series Data – Part II
Estimating The Predictability Of Time Series Data – Part I
Time series data refers to a sequence of measurements made over time. The frequency of these measurements are usually fixed, say once every second or once every hour. We encounter time series data in a variety of scenarios in the real world. Some examples include stock market data, sensor data, speech data, and so on. … Continue reading Estimating The Predictability Of Time Series Data – Part I
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
Perpetual Enigma 