Garima Nishad's Projects
Part of speech tagging is the process of determining the category of a word from the words in its surrounding context. You can think of part of speech tagging as a way to go from words to their Mad Libs categories.
The day/night image dataset consists of 200 RGB color images in two categories: day and night. There are equal numbers of each example: 100 day images and 100 night images. We'd like to build a classifier that can accurately label these images as day or night, and that relies on finding distinguishing features between the two types of images! Note: All images come from the AMOS dataset (Archive of Many Outdoor Scenes).
Android TensorFlow MachineLearning Example (Building TensorFlow for Android)
In this notebook, we look at how attention is implemented. We will focus on implementing attention in isolation from a larger model. That's because when implementing attention in a real-world model, a lot of the focus goes into piping the data and juggling the various vectors rather than the concepts of attention themselves.
An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise.”
In this project, I have created a neural network architecture to automatically generate captions from images. After using the Microsoft Common Objects in COntext (MS COCO) dataset to train my network, I have tested my network on novel images!
The day/night image dataset consists of 200 RGB color images in two categories: day and night. There are equal numbers of each example: 100 day images and 100 night images. We'd like to build a classifier that can accurately label these images as day or night, and that relies on finding distinguishing features between the two types of images! Note: All images come from the AMOS dataset (Archive of Many Outdoor Scenes).
A curated list of awesome Deep Learning tutorials, projects and communities.
Create a beer label classifier using SIFT, SURF, ORB.
OpenCV reads in images in BGR format (instead of RGB) because when OpenCV was first being developed, BGR color format was popular among camera manufacturers and image software providers. The red channel was considered one of the least important color channels, so was listed last, and many bitmaps use BGR format for image storage. However, now the standard has changed and most image software and cameras use RGB format, which is why, in these examples, it's good practice to initially convert BGR images to RGB before analyzing or manipulating them.
🤓 Build your own (insert technology here)
Generates new celeb faces using deep DC-GAN.
My blog.
In this notebook, I'll construct a character-level LSTM with PyTorch. The network will train character by character on some text, then generate new text character by character. As an example, I will train on Anna Karenina. This model will be able to generate new text based on the text from the book!
The day/night image dataset consists of 200 RGB color images in two categories: day and night. There are equal numbers of each example: 100 day images and 100 night images. We'd like to build a classifier that can accurately label these images as day or night, and that relies on finding distinguishing features between the two types of images! Note: All images come from the AMOS dataset (Archive of Many Outdoor Scenes).
A complete computer science study plan to become a software engineer.
Here are a few exercises to get you started with coding matrices. The exercises start off with vectors and then get more challenging
Coding vectors in Python.
To select the most accurate color boundaries, it's often useful to use a color picker and choose the color boundaries that define the region you want to select!
This notebook will change and print colors.
This script converts h5 file to tflite
Official implementation of Character Region Awareness for Text Detection (CRAFT)
Exercise notebooks for CVND.
Convolutional Neural Networks
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent