A Comprehensive Guide to Face Detection and Recognition
You can access the full courses here: Build Lorenzo – A Face Swapping AI and Build Jamie – A Facial Recognition AI Part 1 In this lesson, we’re going to
Mohit Deshpande
Recognizing Images with Contour Detection using OpenCV
You can access the full course here: Advanced Image Processing – Build a Blackjack Counter Transcript 1 Hello everybody. My name is Mohit Deshpande. And in this video, I want
An Overview of Structural Similarity
You can access the full course here: Create a Raspberry Pi Smart Security Camera In this lesson we will discuss a different approach to image-similarity called structural similarity(SSIM). A Mean
How to Process Video Frames using OpenCV and Python
You can access the full course here: Create a Raspberry Pi Smart Security Camera Transcript Hello everybody, my name is Mohit Deshpande and in this video, we’re going to start
A Comprehensive Guide to Neural Networks
You can access the latest Machine Learning courses here: Machine Learning Mini-Degree Transcript 1 Hello everybody. My name is Mohit Deshpande. And before we get into our main topic of
An Introduction to AI
You can access the full course here: The Complete Artificial Neural Networks Developer Course Why do we even have artificial intelligence? Computers are really dumb machines! When we write code
An Overview of Reinforcement Learning: Teaching Machines to Play Games
Think back to the time you first learned a skill: driving a car, playing an instrument, cooking a recipe. Let’s consider the example of playing chess. Initially, it might have
Supervised Learning – Using Decision Trees to Classify Data
One challenge of neural or deep architectures is that it is difficult to determine what exactly is going on in the machine learning algorithm that makes a classifier decide how
Advanced Recurrent Neural Networks
Recurrent Neural Networks (RNNs) are used in all of the state-of-the-art language modeling tasks such as machine translation, document detection, sentiment analysis, and information extraction. Previously, we’ve only discussed the
Understanding Advanced Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are used in all of the state-of-the-art vision tasks such as image classification, object detection and localization, and segmentation. Previously, we’ve only discussed the LeNet-5 architecture,
Dimensionality Reduction
Dimensionality Reduction is a powerful technique that is widely used in data analytics and data science to help visualize data, select good features, and to train models efficiently. We use
Recurrent Neural Networks for Language Modeling
Many neural network models, such as plain artificial neural networks or convolutional neural networks, perform really well on a wide range of data sets. They’re being used in mathematics, physics,
All About Autoencoders
Data compression is a big topic that’s used in computer vision, computer networks, computer architecture, and many other fields. The point of data compression is to convert our input into
Data Clustering with K-Means
Determining data clusters is an essential task to any data analysis and can be a very tedious task to do manually! This task is nearly impossible to do by hand
A Guide to Improving Deep Learning’s Performance
Although deep learning has great potential to produce fantastic results, we can’t simply leave everything to the learning algorithm! In other words, we can’t treat the model as some black-box,
Introduction to Convolutional Neural Networks for Vision Tasks
Neural networks have been used for a wide variety of tasks across different fields. But what about image-based tasks? We’d like to do everything we could with a regular neural
Complete Guide to Deep Neural Networks – Part 2
Read Part 1 here. Last time, we formulated our multilayer perceptron and discussed gradient descent, which told us to update our parameters in the opposite direction of the gradient. Now
Complete Guide to Deep Neural Networks – Part 1
Neural networks have been around for decades, but recent success stems from our ability to successfully train them with many hidden layers. We’ll be opening up the black-box that is