Pinned Repositories
FaceProject
complete
Handwritten-Image-Detection-MNIST-Data
In this exercise I will work with image data: specifically the famous MNIST data set. This data set contains 70,000 images of handwritten digits in grayscale (0=black, 255 = white). The images are 28 pixels by 28 pixels for a total of 784 pixels. This is quite small by image standards. Also, the images are well centered and isolated. This makes this problem solvable with standard fully connected neural nets without too much pre-work.
ImageFaceDectection
This will count how many faces there are inside the imported picture.
LiveFaceDectection
This application will box in each face that the camera can see and tell you how many faces it detects.
Deep_Learning_Spring_2024
Quantum-2024
Pre-Trained-Models
In this exercise I will show how to load pre-trained models such as VGG16 and ResNet. This is a fairly simple exercise designed to get familiar with models like VGG and Resnet and the output they give.
100DaysPython
100 Days
Backpropagation
In this exercise I will use backpropagation to train a multi-layer perceptron (with a single hidden layer). The experiment deals with different patterns and see how quickly or slowly the weights converge. Then it shows the impact and interplay of different parameters such as learning rate, number of iterations, and number of data points.
BMI
Udemy guided application
CoelhoNunes's Repositories
CoelhoNunes/Quantum-2024
CoelhoNunes/Deep_Learning_Spring_2024
CoelhoNunes/LinkedinAlgorithms
CoelhoNunes/FaceProject
complete
CoelhoNunes/100DaysPython
100 Days
CoelhoNunes/ML-AZ
Machine learning modules from Udemy
CoelhoNunes/portfolio-HTML-CSS
This project involves raw HTML and CSS code to make a copy of my portfolio.
CoelhoNunes/cryptocurrencyProjects
This is tapping inside Coin Market Cap to display the latest cryptocurrency values.
CoelhoNunes/coreML-iDoggys
My own package that is able to identify which dog is in the picture.
CoelhoNunes/LiveFaceDectection
This application will box in each face that the camera can see and tell you how many faces it detects.
CoelhoNunes/ImageFaceDectection
This will count how many faces there are inside the imported picture.
CoelhoNunes/PredictionRegression
Variations in showing data through visual computation.
CoelhoNunes/FakeData
Here I have used the Faker import to create fake data and manipulated it in different ways.
CoelhoNunes/Handwritten-Image-Detection-MNIST-Data
In this exercise I will work with image data: specifically the famous MNIST data set. This data set contains 70,000 images of handwritten digits in grayscale (0=black, 255 = white). The images are 28 pixels by 28 pixels for a total of 784 pixels. This is quite small by image standards. Also, the images are well centered and isolated. This makes this problem solvable with standard fully connected neural nets without too much pre-work.
CoelhoNunes/RNNS-IMDB-Data
For this exercise, I will train a "vanilla" RNN to predict the sentiment on IMDB reviews. The data consists of 25000 training sequences and 25000 test sequences. The outcome is binary (positive/negative) and both outcomes are equally represented in both the training and the test set.
CoelhoNunes/Pre-Trained-Models
In this exercise I will show how to load pre-trained models such as VGG16 and ResNet. This is a fairly simple exercise designed to get familiar with models like VGG and Resnet and the output they give.
CoelhoNunes/CNN-CIFAR-10-Dataset
In this exercise I will build and train convolutional neural networks. In the first part, I walk through the different layers and how they are configured. In the second part, I built my own model, train it, and compare the performance.
CoelhoNunes/Backpropagation
In this exercise I will use backpropagation to train a multi-layer perceptron (with a single hidden layer). The experiment deals with different patterns and see how quickly or slowly the weights converge. Then it shows the impact and interplay of different parameters such as learning rate, number of iterations, and number of data points.
CoelhoNunes/Deep-Neural-Network-Techniques
From the wide variety of Deep Neural Network techniques, I have orchestrated a project that deals with the following topics: Convolution Neural Network (CNN), Transfer Learning, Data Augmentation, and finally, Recurrent Neural Networks (RNN). Specifically, I have built a CNN to classify images on a dataset, then illustrated the power of using the pre-trained models such as VGG16 and ResNet. In addition, techniques in using that dataset with data augmentation and then using the RNNs to classify the dataset are present.
CoelhoNunes/BMI
Udemy guided application
CoelhoNunes/MustacheApp
A filter that places a mustache on the users face
CoelhoNunes/hotDog
Udemy guided application
CoelhoNunes/FlashChat
Udemy guided application
CoelhoNunes/readingFiles
This project reads text files and organizes the results into text files with serval parameters.
CoelhoNunes/javaDataStructurePart4
This includes the following data structure technique: Radix Sort
CoelhoNunes/javaDataStructurePart3
This includes the following data structure technique: Min Heap
CoelhoNunes/javaDataStructurePart1
This includes the following data structure techniques: exponentiation, recursive function, and sorted array.
CoelhoNunes/javaDataStructurePart2
This includes the following data structure techniques: binary search, binary search tree nodes, and one stack.
CoelhoNunes/Validate-an-Account
Checking to see if the account number entered is a valid code.
CoelhoNunes/myInstagram
This is an Android imitation of Instagram with live post, posting, and updates through APIs.