Sequences occur everywhere in our daily life. Some of the examples include sensor data, stock market quotes, speech signals, and many more. A sequence is a collection of elements where each element is indexed. Repetitions are allowed in this case, which means any element can reappear in a given sequence. If we look closely, we can … Continue reading Cauchy Sequences In The Real World
Tag: Sequential Data
Measuring The Memory Of Time Series Data
Time series data has memory. It remembers what happened in the past and avenge any wrongdoings! Can you believe it? Okay the avenging part may not be true, but it definitely remembers the past. The "memory" refers to how strongly the past can influence the future in a given time series variable. If it has a … Continue reading Measuring The Memory Of Time Series Data
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 