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 how machine learning concepts like neural network training and optimization mirror daily life challenges and decision-making processes.
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 developer explores using deep learning and sequence-to-sequence models to train a chatbot on personal social media data to mimic their conversational style.
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.
An introduction to deep learning, explaining its rise, key concepts like CNNs, and why it's powerful now due to data and computing advances.
Explores a neural network model, sketch-rnn, that generates vector drawings by learning from human sketch sequences, mimicking abstract visual concepts.
A guide for beginners on how to start learning deep learning using the Keras library, including recommended resources and prerequisites.
Explores using machine learning algorithms to predict outcomes in the NCAA March Madness basketball tournament, analyzing data and modeling techniques.
Final part of a series on building a product classification API, covering the creation of a custom Python class and web app for categorizing product titles.
