Interview
summary
Demystifies deep learning with concrete entry steps: learn basic statistics and linear algebra, use existing frameworks and online courses like Coursera rather than reimplementing algorithms, and work from public datasets. Gives examples that actually shipped: fraud and risk detection for trading and payments, health-data classifiers that suggest exercise, image and IoT anomaly detection and page-click recommendation systems. Emphasizes practical thresholds to cross and that these projects do work in production.
AI-written summary · about
chapters (6)
- 0:00Intro
- 0:55What is your talk about
- 1:26How did you get into machine learning
- 2:18How would you start out
- 3:14Machine learning projects
- 4:20Machine learning explosion
related talks
03mInterview
51mGetting Started with Deep Learning for Software Developers
22mReal-world applications of machine learning: Beyond the hype
30mMachine Learning: Beyond the Tutorial
27mA Look Inside Applied Machine Learning
19mDoing Machine Learning Without a Data Scientist
55mData Science Fundamentals
59mA developers guide to Machine Learning