PyTorch Graphs, Automatic Differentiation, and Autograd

14 mins read Table of content: Understanding Graphs, Automatic Differentiation, and AutogradBuilding Your First Neural NetworkGoing Deep with PyTorchMemory Management and Using Multiple […]

PyTorch Quick Tutorial

23 mins read Overview What is PyTorch? How can you get started with it from scratch? We’ll cover all of that in this […]

A tutorial on Histogram of Oriented Gradients (HOG) Feature Descriptor in Computer Vision with Python code

17 mins read Introduction Feature engineering is a game-changer in the world of machine learning algorithms. It’s actually one of my favorite aspects […]

Hough Transform implementation in Python

6 mins read The Hough transform (Duda and Hart, 1972), which started out as a technique to detect lines in an image, has been […]

A tutorial on Hough Transform

16 mins read Basics The Hough transform is an incredible tool that lets you identify lines. Not just lines, but other shapes as […]

A tutorial on Motion Estimation with Optical Flow with Python Implementation

26 mins read Recent breakthroughs in computer vision research have allowed machines to perceive their surrounding world through techniques such as object detection for detecting […]

Upper Confidence Bound (UCB) Algorithm Explained with Python code

7 mins read In this tutorial, I will explain to you the application of the Upper Confidence Bound(UCB) algorithm to solve the Multi […]

Difference between model-based and model-free reinforcement learning

3 mins read To answer this question, let’s revisit the components of an MDP, the most typical decision-making framework for RL. An MDP […]

Reinforcement Q-Learning from Scratch in Python with OpenAI Gym

24 mins read Most of you have probably heard of AI learning to play computer games on their own, a very popular example […]

What is q-learning?

5 mins read Introduction One of my favorite algorithms that I learned while taking a reinforcement learning course was q-learning. Probably because it […]

How to split data in decision tree nodes?

17 mins read The problem: We need to recommend apps to users according to what they’re likely to download Recommendation systems are one […]

Machine Learning From Scratch Series: Gradient Descent

9 mins read Gradient Descent is an iterative algorithm that is used to minimize a function by finding the optimal parameters. Gradient Descent can […]

Logistic Regression Implementation From Scratch in Python

4 mins read The objective of this tutorial is to implement our own Logistic Regression from scratch. This is going to be different […]

Machine Learning From Scratch Series: K-means Clustering: K-Nearest Neighbors (KNN) Algorithm

8 mins read Introduction A famous quote states: “You are the average of the five people you spend the most time with.” Although […]

Implementing LSTM Networks in Python with Keras

27 mins read A powerful and popular recurrent neural network is the long short-term model network or LSTM. It is widely used because […]

How to reshape Input Data for Long Short-Term Memory (LSTM) Networks in Keras

9 mins read It can be difficult to understand how to prepare your sequence data for input to an LSTM model. Often there […]

A complete guide to understanding Long Short Term Memory (LSTM) Networks

37 mins read In this post, I provide three useful resources for understanding LTSMs. Introduction Sequence prediction problems have been around for a […]

Understanding L1 and L2 as Loss Function and Regularization

6 mins read While practicing machine learning, you may have come upon a choice of the mysterious L1 vs L2. Usually, the two […]

Different missing data mechanisms

3 mins read Missing data mechanisms concern the relationship between missing data and the values of variables in the data matrix. Given this focus, […]

Kernel Density Estimation (KDE) in Python

10 mins read Nonparametric Density Estimation In some cases, a data sample may not resemble a common probability distribution or cannot be easily […]