Sep2018 Oct Top Stories, Tweets
All (57) | Courses, Education (8) | Meetings (5) | News, Features (8) | Opinions, Interviews (15) | Top Stories, Tweets (6) | Tutorials, Overviews (14) | Webcasts & Webinars (1)
- Top Stories, Oct 8-14: 10 Best Mobile Apps for Data Scientist / Data Analysts; BIG, small or Right Data: Which is the proper focus?
- Oct 15, 2018.
Also: How To Learn Data Science If You’re Broke; Top 8 Python Machine Learning Libraries; 9 Must-have skills you need to become a Data Scientist, updated; SQL, Python, & R: All in One Platform; Using Confusion Matrices to Quantify the Cost of Being Wrong
- Top KDnuggets tweets, Oct 3–9: 5 Reasons Logistic Regression should be the first thing you learn when becoming a Data Scientist
- Oct 10, 2018.
Also: Cheat sheet: Deep learning losses & optimizers; Journey to Machine Learning – 100 Days of ML Code; Math for Machine Learning; Essential Math for Data Science: ‘Why’ and ‘How’
- Top September Stories: Essential Math for Data Science: Why and How; Machine Learning Cheat Sheets
- Oct 10, 2018.
Also: Journey to Machine Learning - 100 Days of ML Code; How many data scientists are there and is there a shortage?
- Top Stories, Oct 1-7: Machine Learning Cheat Sheets; How to Create a Simple Neural Network in Python
- Oct 8, 2018.
Also: Recent Advances for a Better Understanding of Deep Learning; Basic Image Data Analysis Using Python – Part 4; A Concise Explanation of Learning Algorithms with the Mitchell Paradigm; Essential Math for Data Science: Why and How
- Top KDnuggets tweets, Sep 26 – Oct 2: Why building your own Deep Learning Computer is 10x cheaper than AWS; 6 Steps To Write Any Machine Learning Algorithm
- Oct 3, 2018.
Also: New Book: Math for #MachineLearning; How to visualize decision tree; Causation in a Nutshell; Machine Learning Cheat Sheets; Brief History of Machine Learning Models Explainability
- Top Stories, Sep 24-30: Machine Learning Cheat Sheets; Learning the Mathematics of Machine Learning
- Oct 1, 2018.
Also: Math for Machine Learning; Introducing Path Analysis Using R; Introduction to Deep Learning; Essential Math for Data Science: Why and How; 6 Steps To Write Any Machine Learning Algorithm From Scratch: Perceptron Case Study