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Name: Coelho
Type: User
Bio: I am ready to start building!
Name: Coelho
Type: User
Bio: I am ready to start building!
100 Days
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.
Udemy guided application
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.
My own package that is able to identify which dog is in the picture.
This is tapping inside Coin Market Cap to display the latest cryptocurrency values.
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.
complete
Here I have used the Faker import to create fake data and manipulated it in different ways.
Udemy guided application
This app introduced Android Studio and linked data information through an Application Programming Interface (API)
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.
Udemy guided application
This will count how many faces there are inside the imported picture.
This includes the following data structure techniques: exponentiation, recursive function, and sorted array.
This includes the following data structure techniques: binary search, binary search tree nodes, and one stack.
This includes the following data structure technique: Min Heap
This includes the following data structure technique: Radix Sort
This application will box in each face that the camera can see and tell you how many faces it detects.
Machine learning modules from Udemy
A filter that places a mustache on the users face
This is an Android imitation of Instagram with live post, posting, and updates through APIs.
This project involves raw HTML and CSS code to make a copy of my portfolio.
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.
Variations in showing data through visual computation.
This project reads text files and organizes the results into text files with serval parameters.
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.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.