Tag: ipynb

• My First Working Plotly Post (20 Jun 2021)
  This notebook addresses the `Liquid::SyntaxError` that occurs with Plotly.js in Markdown files by eliminating in-notebook rendering of Plotly charts (using `pio.write_html`) and removing Plotly.js DOM (no `init_notebook_mode()`).
• My First ipynb Post (20 Jun 2021)
  
• Understanding Search Functionality (01 Jun 2021)
  Part 2 in a three-part study exploring the world of Yammer– a workplace communication platform. In this study, we investigate Yammer's search feature with the goal of making recommendations to the development team.
• Investigating A Drop In User Engagement (26 May 2021)
  Part 1 in a three-part study exploring the world of Yammer– a workplace communication platform. In this study, we investigate a drop in user engagement on the platform by using product analytics and data science tools.
• N-Gram Language Models in Python (19 Dec 2019)
  In this article, we will be writing a program to find n-gram statistics, compute the probability of a sentence, and perform online prediction of words.
• Unsupervised Learning of Gaussian Mixture Models With the EM Algorithm (13 Dec 2019)
  The Expectation-Maximization algorithm is a powerful iterative method for calculating maximum likelihood estimates from unlabeled data. In this article, we will be exploring its use in Gaussian mixture models to perform the task of clustering with the Scikit-learn digits dataset. The performance of the EM-algorithm is then compared to a vanilla k-Means implementation provided in Scikit-learn.
• Comparing the Performance of Various Classifiers on the Scikit-learn Digits and MNIST Datasets (06 Dec 2019)
  In this article we will be implementing several classifiers to predict the class labels of the Scikit-learn digits and MNIST_Light datasets.
• Recognising Named Entities (NER) (29 Nov 2019)
  Named entity recognition (abbreviated NER) is a subset of NLP that seeks to identify and categorise key information in text (called entities). In this article we will be performing NER by training a neural net on the CoNLL2003 tags and GloVe embeddings datasets.
• Convolutional Neural Networks - The Basics (22 Nov 2019)
  In this post, we'll introduce you to the Convolutional Neural Network and its application to image classification. We'll walk you through building a simple CNN for multi-class classification, then run it on the Kaggle Flowers Recognition dataset. You will also get to improve your model's results using several common techniques. Once you have that down, you'll get to try out transfer learning using your CNN and a pre-trained state-of-the-art InceptionV3 base. Finally, we'll show you how to achieve a great classification accuracy with feature extraction and fine-tuning.
• Decision Tree Learning with CART and ID3 Algorithms (15 Nov 2019)
  Often times, classification can be framed as a sort of questioning-answering system. Questions are asked about the input data which aid the model in determining a prediction. One example of a 'question' that a model might ask is 'does this input image contain this attribute value?' Decision trees naturally help to structure this kind of if-then hierarchical decision-making by defining a series of questions that lead to a class label or value. In this article, we will explore several algorithms for constructing the two types of decision trees; the ID3 algorithm for Classification Trees and the CART for Regression Trees. While reading along, you will be able to implement your own ID3 algorithm from scratch using the code provided in this notebook.
• Extracting Noun Groups Using Machine Learning Techniques (14 Oct 2019)
  We will use a supervised machine learning technique applied to language processing to ultimately detect partial syntactic structures and extract named entities.
• Classifying Emotion Using Convolutional Neural Networks (Publication) (06 Apr 2019)
  In this published paper, we will be exploring the concepts of object recognition and deep learning to ultimately train a classification model to recognise human emotion from the FER-2013 facial expression dataset (Kaggle, 2013).