-
Object Detection and Image Classification with YOLO
We explain object detection, how YOLO algorithm can help with image classification, and introduce the open source neural network framework Darknet.
-
  Journey to Machine Learning – 100 Days of ML Code
A personal account from Machine Learning enthusiast Avik Jain on his experiences of #100DaysOfMLCode, a challenge that encourages beginners to code and study machine learning for at least an hour, every day for 100 days.
-
Ultimate Guide to Getting Started with TensorFlow
Including video and written tutorials, beginner code examples, useful tricks, helpful communities, books, jobs and more - this is the ultimate guide to getting started with TensorFlow.
-
Don’t Use Dropout in Convolutional Networks
If you are wondering how to implement dropout, here is your answer - including an explanation on when to use dropout, an implementation example with Keras, batch normalization, and more.
-
OLAP queries in SQL: A Refresher
Based on the recent book - Principles of Database Management - The Practical Guide to Storing, Managing and Analyzing Big and Small Data - this post examines how OLAP queries can be implemented in SQL.
-
Word Vectors in Natural Language Processing: Global Vectors (GloVe)
A well-known model that learns vectors or words from their co-occurrence information is GlobalVectors (GloVe). While word2vec is a predictive model — a feed-forward neural network that learns vectors to improve the predictive ability, GloVe is a count-based model.
-
Linear Regression In Real Life
A helpful guide to Linear Regression, using an example of a friends road trip to Las Vegas to highlight how it can be used in a real life situation.
-
Data Visualization Cheat Sheet
Core principles for successful data visualization, including tips on how to reduce clutter, preattentive processing and how to integrate text within the graph.
-
DynamoDB vs. Cassandra: from “no idea” to “it’s a no-brainer”
DynamoDB vs. Cassandra: have they got anything in common? If yes, what? If no, what are the differences? We answer these questions and examine performance of both databases.
-
Interpreting a data set, beginning to end
Detailed knowledge of your data is key to understanding it! We review several important methods that to understand the data, including summary statistics with visualization, embedding methods like PCA and t-SNE, and Topological Data Analysis.
|
Journey to Machine Learning – 100 Days of ML Code
Ultimate Guide to Getting Started with TensorFlow
Linear Regression In Real Life
Data Visualization Cheat Sheet