Gradient Descent Explained: Batch, Mini-Batch, and Stochastic (Simple)

Ace your machine learning interviews with Exponent’s ML engineer interview course: https://bit.ly/4bAaFK5 This video provides an in-depth explanation of various optimization algorithms in machine learning, specifically batch, mini-batch, and stochastic gradient descent, detailing their differences and applications. It also addresses the challenges of reaching a global minimum in non-convex loss functions and the effectiveness of different algorithms in finding local minima, emphasizing the importance of parameter initialization and performance evaluation on validation and test sets. Want more machine learning content? - Fake News Detection System - Machine Learning Mock Interview - https://youtu.be/qrNqUwpypT8 - Amazon Machine Learning Engineer Interview: K-Means Clustering - https://youtu.be/xKZHH-UOsUM - How to Become a Machine Learning Engineer - https://youtu.be/VP8eC3I1IHQ 👉 Subscribe to our channel: http://bit.ly/exponentyt 🕊️ Follow us on Twitter: http://bit.ly/exptweet 💙 Like us on Facebook for special discounts: http://bit.ly/exponentfb 📷 Check us out on Instagram: http://bit.ly/exponentig 📹 Watch us on TikTok: https://bit.ly/exponenttiktok ABOUT US: Did you enjoy this interview question and answer? Want to land your dream career? Exponent is an online community, course, and coaching platform to help you ace your upcoming interview. Exponent has helped people land their dream careers at companies like Google, Microsoft, Amazon, and high-growth startups. Exponent is currently licensed by Stanford, Yale, UW, and others. Our courses include interview lessons, questions, and complete answers with video walkthroughs. Access hours of real interview videos, where we analyze what went right or wrong, and our 1000+ community of expert coaches and industry professionals, to help you get your dream job and more!