# Tag: Gradient Descent

**Optimization in Machine Learning: Robust or global minimum?**- Jun 30, 2017.

Here we discuss how convex problems are solved and optimised in machine learning/deep learning.**Machine Learning Crash Course: Part 1**- May 24, 2017.

This post, the first in a series of ML tutorials, aims to make machine learning accessible to anyone willing to learn. We’ve designed it to give you a solid understanding of how ML algorithms work as well as provide you the knowledge to harness it in your projects.**KDnuggets™ News 17:n19, May 17: Guerrilla Guide to Machine Learning with R; 5 Machine Learning Projects You Can’t Overlook**- May 17, 2017.

The Guerrilla Guide to Machine Learning with R; 5 Machine Learning Projects You Can No Longer Overlook, May; The Two Phases of Gradient Descent in Deep Learning; HDFS vs. HBase: All you need to know; Must-Know: What are common data quality issues for Big Data and how to handle them?**Top /r/MachineLearning Posts, April: Why Momentum Really Works; Machine Learning with Scikit-Learn & TensorFlow**- May 5, 2017.

Why Momentum Really Works; O'Reilly's Hands-On Machine Learning with Scikit-Learn and TensorFlow; Implemented BEGAN and saw a cute face at iteration 168k; Self-driving car course; Exploring the mysteries of Go; DeepMind Solves AGI**KDnuggets™ News 17:n17, May 3: Learn Machine Learning… in 10 Days?!? Gradient Descent, Simplified**- May 3, 2017.

How to Learn Machine Learning in 10 Days; Keep it simple! How to understand Gradient Descent algorithm; The Guerrilla Guide to Machine Learning with Python; What Data You Analyzed - KDnuggets Poll Results and Trends; Cartoon: Machine Learning - What They Think I Do**Keep it simple! How to understand Gradient Descent algorithm**- Apr 28, 2017.

In Data Science, Gradient Descent is one of the important and difficult concepts. Here we explain this concept with an example, in a very simple way. Check this out.**Learning to Learn by Gradient Descent by Gradient Descent**- Feb 2, 2017.

What if instead of hand designing an optimising algorithm (function) we*learn*it instead? That way, by training on the class of problems we’re interested in solving, we can learn an optimum optimiser for the class!**The Gentlest Introduction to Tensorflow – Part 2**- Aug 19, 2016.

Check out the second and final part of this introductory tutorial to TensorFlow.**The Gentlest Introduction to Tensorflow – Part 1**- Aug 17, 2016.

In this series of articles, we present the gentlest introduction to Tensorflow that starts off by showing how to do linear regression for a single feature problem, and expand from there.**A Concise Overview of Standard Model-fitting Methods**- May 27, 2016.

A very concise overview of 4 standard model-fitting methods, focusing on their differences: closed-form equations, gradient descent, stochastic gradient descent, and mini-batch learning.**All Machine Learning Models Have Flaws**- Mar 3, 2015.

This classic post examines what is right and wrong with different models of machine learning, including Bayesian learning, Graphical Models, Convex Loss Optimization, Statistical Learning, and more.