In this project, the goal was to train a Convolutional Neural Network (CNN) for regression prediction with Keras and then train a CNN to predict house prices from a set of images.

The objective of the project was to create a deep learning model to classify images of clothing from the Fashion MNIST dataset. The Fashion MNIST dataset is a collection of grayscale images of 10 different categories of clothing and accessories, like T-shirts, trousers, pullovers, dresses, coats, sandals, shirts, sneakers, bags, and ankle boots.

This project used Haar cascade face detector, extract the face region of interest (ROI) from the image and then pass the ROI through LeNet for smile detection.

This project demonstrated how to use deep learning techniques, specifically with frameworks like Keras and TensorFlow, to automatically solve CAPTCHA challenges.

This amied to use Early Stopping and Model Checkpointing in training Keras models encapsulates a sophisticated approach to deep learning.

The project was designed to classify an image by identifying the main subject in the image, leveraging pre-trained deep learning models available through TensorFlow's Keras library. It accepts an image file and a model name as input parameters. The script supports various state-of-the-art image classification models like VGG16, VGG19, ResNet50, InceptionV3, and Xception, which have been trained on the ImageNet dataset.

The goal was to visualize network architecture using Keras and TensorFlow.

The aim was to implement MiniVGGNet to work on CIFAR-10 data set.

The goal was for building, training, evaluating, and plotting the performance of a convolutional neural network (LeNet) for digit classification on the MNIST dataset.

The goal was to create the first deep learning neural network model in Python using Keras. Here, we started by loading and preparing our dataset, followed by defining and compiling a Keras neural network model. We trained the model on our data, evaluate its performance, and then use it to make predictions on new data. We used Pima Indians onset of diabetes dataset.

We explored hands-on code that illustrates how to implement and apply convolution operations and kernels to images. This insight aided in understanding the internal workings of Convolutional Neural Networks (CNNs) during their training phase.

Mastering Backpropagation: A Step-by-Step Guide to Implementing it with Python

The project demonstrated how a perceptron model could learn bitwise operations through a basic machine learning process involving training with input features and corresponding labels, followed by testing to evaluate the model's predictions.

This project explores pedestrian detection using four different computer vision techniques.
Method 1: Background Subtraction + Contour Extraction
Method 2: Haar Cascades (Viola-Jones Classifiers)
Method 3: Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM)
Method 4: Single Shot Detector (SSD) with MobileNet

This project showcased the implementation of Haar Cascade classifiers for object detection in video streams. Haar Cascades are a popular method for object detection due to their efficiency and effectiveness, particularly in detecting faces and other predefined objects. Using OpenCV, this project demonstrates how to apply Haar Cascades to real-time video data to identify and track objects.

This project focused on counting fingers in a real-time video using OpenCV.

The Smile Detection Project aimed at identifying smiles real-time video feeds using a facial landmark detector to accurately determine the presence of a smile.

The Face Detection project aimed to identify and locate human faces within a digital image utilizing Haar Cascades.

These project series provides an essential overview of computer vision techniques using OpenCV. It begins with fundamental image operations—loading, displaying, and pixel manipulation—then advances to drawing, translation, rotation, resizing, flipping, and cropping. Additionally, it explores arithmetic operations, bitwise manipulations, masking, and channel manipulation. Accompanied by downloadable source code for each tutorial, this series offers a practical and efficient way to grasp the key functionalities of OpenCV, making it perfect for beginners eager to learn quickly.