Most organizations, enabled by massive increase in the amount and variety of data, are already using data science to understand their business performance and make operational decisions. Some organizations are just getting started with data science, while others have made significant investments and have data science teams spread across global…

In 2019, the war for ML frameworks has two main contenders: PyTorch and TensorFlow. There is a growing adoption of PyTorch by researchers and students due to ease of use, while in industry, Tensorflow is currently still the platform of choice. Some of the key advantages of PyTorch are: Simplicity…

Implementing an RNN from scratch in Python. — The main objective of this post is to implement an RNN from scratch and provide an easy explanation as well to make it useful for the readers. Implementing any neural network from scratch at least once is a valuable exercise. …

In order to understand how a machine learning algorithm learns from data to predict an outcome, it is essential to understand the underlying concepts involved in training an algorithm. I assume you have basic machine learning understanding and also basic knowledge of probability and statistics. If not please go through…

In a multi-label classification problem, the training set is composed of instances each can be assigned with multiple categories represented as a set of target labels and the task is to predict the label set of test data e.g., A text might be about any of religion, politics, finance or…

The process of setting the hyper-parameters requires expertise and extensive trial and error. There are no simple and easy ways to set hyper-parameters — specifically, learning rate, batch size, momentum, and weight decay. Deep learning models are full of hyper-parameters and finding the best configuration for these parameters in such…

Applying latest deep learning techniques for text processing — Transfer learning is a technique where instead of training a model from scratch, we reuse a pre-trained model and then fine-tune it for another related task. It has been very successful in computer vision applications. In natural language processing (NLP) transfer learning was mostly limited to the use of pre-trained…

Essential Probability & Statistics for Machine Learning — Machine Learning is an interdisciplinary field that uses statistics, probability, algorithms to learn from data and provide insights which can be used to build intelligent applications. In this article, we will discuss some of the key concepts widely used in machine learning. Probability and statistics are related areas of mathematics…

Challenges in classification and techniques to improve performance — Classification problems having multiple classes with imbalanced dataset present a different challenge than a binary classification problem. The skewed distribution makes many conventional machine learning algorithms less effective, especially in predicting minority class examples. …