A review and tips for the OMSCS CS7641 Machine Learning course, covering assignments, exams, and workload.
A tutorial on implementing a neural network in JavaScript using Google's deeplearn.js library to improve web accessibility by choosing font colors.
A guide to implementing logistic regression with gradient descent in JavaScript to solve classification problems.
Explains the Normal Equation as an alternative to Gradient Descent for linear regression in JavaScript, including implementation.
A guide to implementing multivariate linear regression with gradient descent in JavaScript, including feature scaling.
A guide to implementing vectorized gradient descent in JavaScript for machine learning, improving efficiency over unvectorized approaches.
Explores methods to optimize the gradient descent algorithm in JavaScript, focusing on selecting the right learning rate for convergence.
A recap of PyData Warsaw 2017, covering key talks, new package announcements, and analytics on the conference's international attendees.
An overview of Machine Learning applications in Remote Sensing, covering key algorithms and the typical workflow for data analysis.
Explains polynomial regression as a solution to under-fitting in machine learning when data has a nonlinear correlation.
A guide to implementing linear algebra concepts and matrix operations in JavaScript, using the math.js library for machine learning.
Part 4 of a series on the Microsoft Bot Framework, focusing on adding natural language processing using LUIS (intents, entities, utterances).
A guide to implementing linear regression with gradient descent in JavaScript, using a housing price prediction example.
Explains word embeddings, comparing count-based and context-based methods like skip-gram for converting words into dense numeric vectors.
Explains the math behind GANs, their training challenges, and introduces WGAN as a solution for improved stability.
Explores the importance of interpreting ML model predictions, especially in regulated fields, and reviews methods like linear regression and interpretable models.
A data scientist shares his career journey from psychology to Lazada, debunks common myths about the field, and offers practical advice for aspiring practitioners.
A tutorial on building a Recurrent Neural Network (RNN) with LSTM cells in TensorFlow to predict S&P 500 stock prices.
A guest lecture summary on starting a data science career, based on a talk for SMU's Masters in IT students.
A practical guide outlining essential tools, skills, and practice methods for beginners to start a career in data science.
