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SFU Professional Computer Science
An unofficial publication by students of the master’s program in professional computer science
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XGBoost: A Deep Dive into Boosting
XGBoost: A Deep Dive into Boosting
Every day we hear about the breakthroughs in Artificial Intelligence. However, have you wondered what challenges it faces?
Rohan Harode
Feb 3, 2020
Recommendation Systems: Collaborative Filtering using Matrix Factorization — Simplified
Recommendation Systems: Collaborative Filtering using Matrix Factorization — Simplified
Matrix Factorization explained using understandable examples
SACHIN PRABHU THANDAPANI
Mar 1, 2019
An Introduction to Convolutional Neural Network (CNN)
An Introduction to Convolutional Neural Network (CNN)
cmpt733_dataproject
Feb 10
Detecting Malicious URLs
Detecting Malicious URLs
A comprehensive guide using ML and NLP Techniques
Data Dive
Feb 11
Hierarchical Time Series Forecast for Apparel Industry — Doppler Effect
Hierarchical Time Series Forecast for Apparel Industry — Doppler Effect
Developing a solution for the retail industry to get the sales forecast across space, time, and product dimension.
Ria Gupta
Apr 19, 2020
How to Build a Distributed Big Data Pipeline Using Kafka and Docker
How to Build a Distributed Big Data Pipeline Using Kafka and Docker
A Real-time Stream Processing App Example Exploring Twitter Sentiment and Weather Condition Correlation
Vignesh Perumal
Feb 12, 2021
Latest
Transforming Talent Management Experience with Job Market Data — GoodPeople
Transforming Talent Management Experience with Job Market Data — GoodPeople
Authors: Rovenna Chu, Ha Do, Andrew Han, Rakhi Lakhwani
dathusiats733
Apr 15
Dish Out - A Restaurant Business Analysis Tool
Dish Out - A Restaurant Business Analysis Tool
Authors: Aneeqha Tahseen Syeda, Bhawna Arya, Mohamed Anan Anaikar, Neha Borulkar
Data_Banana
Apr 14
TinyML: Micro Footprint, Macro Impact
TinyML: Micro Footprint, Macro Impact
Authors: Aditya Panchal, Divye Maheshwari, Hemang Bhanushali, Priyanka Manam, Rohit Irvisetty
Priyankamanam
Feb 11
Cracking The YouTube Algorithm
Cracking The YouTube Algorithm
Check out this article to know more about how the YouTube recommendation system works!
Swaathi Sundaramurugan
Feb 11
Terrific Data Scientist Found Unconscious After Deploying the Model Without MLOps
Terrific Data Scientist Found Unconscious After Deploying the Model Without MLOps
GALV
Feb 11
Catch Me If You Can — Getting the True Meaning out of Big Cloud of Texts
Catch Me If You Can — Getting the True Meaning out of Big Cloud of Texts
Authors: Rovenna Chu, Ha Do, Andrew Han, Rakhi Lakhwani
dathusiats733
Feb 11
PCA: The right way
PCA: The right way
This blog is written and maintained by students in the Professional Master’s Program in the School of Computing Science at Simon Fraser…
dataLabb
Feb 11
Deep Dive into Animation Plot with Real-time Data
Deep Dive into Animation Plot with Real-time Data
Authors: Jiahe Wang, Zhi Zheng, Huiyi Zou, Shilin Wang, Hayden Yan
Hayden Yan
Feb 11
Are you ready for Machine-led Machine Learning? MAML: A Modern Approach to Meta-Learning
Are you ready for Machine-led Machine Learning? MAML: A Modern Approach to Meta-Learning
By: Gabriel Henderson, Shrey Grover, Kaumil Trivedi, Gurashish Singh & Arshdeep Singh
SFUMLites
Feb 11
Picasso of the Future!
Picasso of the Future!
Authors: Akshita Sharma, Himani Naveen Joshi, Gokul Mohanarangan, Pranav Balaji, Shivek Chhabra
Avocado Chair
Feb 11
spaCy supremaCy is here, and here is why you shouldn’t sleep on it.
spaCy supremaCy is here, and here is why you shouldn’t sleep on it.
Data_Banana
Feb 10
Adios Pandas! Process Big Data in a Flash using Terality, Dask, or PySpark
Adios Pandas! Process Big Data in a Flash using Terality, Dask, or PySpark
Check out this blog to operate large datasets with ease by changing just the import line!
Data Miners
Feb 10
Redis — the optimal solution for high-performance data retrieval
Redis — the optimal solution for high-performance data retrieval
Haoming Pan, Yukang Liu, Yewin Shu, Zheng Wu, Jie Min
Yewinshu
Feb 10
In 20 minutes learn to conduct an A/B test
In 20 minutes learn to conduct an A/B test
Authors: Cheng Guo, Wenxiao Li, Jie Wang, and Yuke Wang.
Yuke Wang
Feb 10
Sailing through Kafka Streams
Sailing through Kafka Streams
Building distributed real time data streaming applications with Kafka Streams API.
Data Affair
Feb 10
What You Never Knew About Attention Mechanisms
What You Never Knew About Attention Mechanisms
Greg Mehdiyev, Ray Hong, Jinghan Yu, Brendan Artley
Brendan Artley
Feb 10
CNN: an eye for AI
CNN: an eye for AI
CNN Tutorial — Zero to Hero !
Cortana
Feb 10
In-depth understanding of Apache Kafka in 10 minutes
In-depth understanding of Apache Kafka in 10 minutes
Authors: Danlei Qiang, Jingshan Feng, Sijia Cai, Tiange Wang, Yizhe Lyu
Kunkun
Feb 10
Learn K-Means and Hierarchical Clustering Algorithms in 15 minutes
Learn K-Means and Hierarchical Clustering Algorithms in 15 minutes
Authors: Haonan Wu, Jingyan Sun, Lingxiao Song, Ziyue Cheng
c733 data scientists
Feb 10
A Practical Guide to Support Vector Machines (SVM)
A Practical Guide to Support Vector Machines (SVM)
Authors: Wei Zhang, Wanyi Su, Zeyu Hu, Jinyao Lu, Yin Yu Kevani Chow
Happy Data
Feb 10
Docker: What You Need to Know as a Data Scientist
Docker: What You Need to Know as a Data Scientist
Authors: Bofei Jing, Shuo Qiu, Yifei Chen, and Heng Zhou
Shuo Qiu
Feb 9
RAPIDS: The Future of GPU Data Science?
RAPIDS: The Future of GPU Data Science?
Authors: Aidan Vickars, Anant S Awasthy, Karthik Srinatha, Rishabh Kaushal
Rishabh Kaushal
Feb 9
A dashin’ plot: Beginner’s guide to Plotly & Dash
A dashin’ plot: Beginner’s guide to Plotly & Dash
Authors: Tanmay Jain, Ayush Raina, Siddhartha Haldar, Anirban Banerjee
Data Knyts
Feb 7
Surviving In a Random Forest with Imbalanced Datasets
Surviving In a Random Forest with Imbalanced Datasets
CONTRIBUTORS: Bilal Hussain, Kyoun Huh, Hon Wing Eric Chan, Sakina Patanwala
Kyoun Huh
Feb 13, 2021
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