Ever wondered how the machine learning algorithms give us the optimal result, whether it is prediction, classification or any other? How…
Ever wondered how the machine learning algorithms give us the optimal result, whether it is prediction, classification or any other? How those algorithms work? What is the maths behind those algorithms which leads to the result, which can be used as a tool to assess real world problems? Gradient Descent algorithm is the one which makes the magic possible. Gradient Descent is the backbone of every machine learning algorithm and it also acts as a base for many deep learning optimizers. It works on a simple mechanism, and it is to find the optimal weights for the loss function by iterating over the error curve of values shown above, by which the error in the model predictions and the actual values is minimum.
In this article, we explore gradient descent - the grandfather of all optimization techniques and it’s variations. We implement them from scratch with Python.
Gradient Descent for Data Science and Machine Learning. Solve Optimization Problems using Gradient Descent. You might not find it super exciting in and of itself, but it will enable us to do exciting things throughout the article, so bear with me.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
For the calculation of Loss, various optimization techniques are used in the field of Machine learning and Deep learning. This article will cover commonly used loss function in Machine learning and Deep learning, its use and mathematics behind it.
PyTorch for Deep Learning | Data Science | Machine Learning | Python. PyTorch is a library in Python which provides tools to build deep learning models. What python does for programming PyTorch does for deep learning. Python is a very flexible language for programming and just like python, the PyTorch library provides flexible tools for deep learning.