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Topics: AI | Data Science | Data Visualization | Deep Learning | Machine Learning | NLP | Python | R | Statistics

About Nagesh Singh Chauhan

Nagesh holds a Bachelor's degree in Computer Science. He has over 5 years of IT industry experience having specialization in various domains related to data science including machine learning, time series analysis, big data, cloud platforms, NoSQL DB, Text Analysis and Natural language processing, Time series analysis, probability and statistics, and computer vision. When he is not playing around with data he spends his time in cooking, playing guitar, and sports. He loves to write technical articles on various aspects of Data Science, you can check out some of his articles on Medium. Find him on LinkedIn.

Nagesh Singh Chauhan Posts (38)

  • Zero-Shot Learning: Can you classify an object without seeing it before? - 12 Apr 2021
    Developing machine learning models that can perform predictive functions on data it has never seen before has become an important research area called zero-shot learning. We tend to be pretty great at recognizing things in the world we never saw before, and zero-shot learning offers a possible path toward mimicking this powerful human capability.
  • The Inferential Statistics Data Scientists Should Know - 11 Mar 2021
    The foundations of Data Science and machine learning algorithms are in mathematics and statistics. To be the best Data Scientists you can be, your skills in statistical understanding should be well-established. The more you appreciate statistics, the better you will understand how machine learning performs its apparent magic.
  • Hugging Face Transformers Package – What Is It and How To Use It - 16 Feb 2021
    The rapid development of Transformers have brought a new wave of powerful tools to natural language processing. These models are large and very expensive to train, so pre-trained versions are shared and leveraged by researchers and practitioners. Hugging Face offers a wide variety of pre-trained transformers as open-source libraries, and you can incorporate these with only one line of code.
  • Attention mechanism in Deep Learning, Explained - 11 Jan 2021
    Attention is a powerful mechanism developed to enhance the performance of the Encoder-Decoder architecture on neural network-based machine translation tasks. Learn more about how this process works and how to implement the approach into your work.
  • Silver BlogOptimization Algorithms in Neural Networks - 18 Dec 2020
    This article presents an overview of some of the most used optimizers while training a neural network.
  • Silver BlogNaïve Bayes Algorithm: Everything you need to know - 08 Jun 2020
    Naïve Bayes is a probabilistic machine learning algorithm based on the Bayes Theorem, used in a wide variety of classification tasks. In this article, we will understand the Naïve Bayes algorithm and all essential concepts so that there is no room for doubts in understanding.
  • Introduction to Convolutional Neural Networks - 03 Jun 2020
    The article focuses on explaining key components in CNN and its implementation using Keras python library.
  • Silver BlogModel Evaluation Metrics in Machine Learning - 28 May 2020
    A detailed explanation of model evaluation metrics to evaluate a classification machine learning model.
  • Dimensionality Reduction with Principal Component Analysis (PCA) - 21 May 2020
    This article focuses on design principles of the PCA algorithm for dimensionality reduction and its implementation in Python from scratch.
  • Hyperparameter Optimization for Machine Learning Models - 07 May 2020
    Check out this comprehensive guide to model optimization techniques.

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