Garima13a/Oct

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Python
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MIT License
Created over 5 years ago
Updated over 5 years ago

Garima13a/Oct

Garima13a

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YOLO-Object-Detection

YOLO is a state-of-the-art, real-time object detection algorithm. In this notebook, we will apply the YOLO algorithm to detect objects in images.

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Kalman-Filters

Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe. The filter is named after Rudolf E. Kálmán, one of the primary developers of its theory.

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Automatic-Image-Captioning

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!

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Landmark-Detection-Tracking-SLAM-

SLAM gives you a way to track the location of a robot in the world in real-time and identify the locations of landmarks such as buildings, trees, rocks, and other world features. This is an active area of research in the fields of robotics and autonomous systems.

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Facial-Keypoint-Detection

This project combines the knowledge of computer vision techniques and deep learning architectures to build a facial keypoint detection system that takes in any image with faces, and predicts the location of 68 distinguishing keypoints on each face!

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MNIST_GAN

In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits! GANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out: Pix2Pix CycleGAN & Pix2Pix in PyTorch, Jun-Yan Zhu A list of generative models The idea behind GANs is that you have two networks, a generator 𝐺 and a discriminator 𝐷 , competing against each other. The generator makes "fake" data to pass to the discriminator. The discriminator also sees real training data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks as close as possible to real, training data. The discriminator is a classifier that is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistinguishable from real data to the discriminator. The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector that the generator uses to construct its fake images. This is often called a latent vector and that vector space is called latent space. As the generator trains, it figures out how to map latent vectors to recognizable images that can fool the discriminator. If you're interested in generating only new images, you can throw out the discriminator after training. In this notebook, I'll show you how to define and train these adversarial networks in PyTorch and generate new images!

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ORB

Oriented FAST and Rotated BRIEF (ORB)

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Attention-Mechanism-Basics

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Flower_Classification_Tensorflow.js

For this project, I'll be using the 'Flower Classification' dataset which I downloaded from Kaggle. This project has been made using Tensorflow.js.

Semantic-Segmentation

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Convert-h5-to-tflite-final

Grad-CAM-with-fastai

Use Grad-cam with fastai

Coding-Vectors

Coding vectors in Python.

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Instantiate-Multiple-Cars

Now, the car.py file has been modified so that __init__ takes in an optional color parameter!

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Modify-Predict-State

Here is the current implementation of the predict_state function. It takes in a state (a Python list), and then separates those into position and velocity to calculate a new, predicted state. It uses a constant velocity motion model.

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Dog-Breed-Classifier

This project classifies breeds of dogs using CNN.

Color-playground

This notebook will change and print colors.

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Denoising-Autoencoder

Denoising auto-encoder forces the hidden layer to extract more robust features and restrict it from merely learning the identity. Autoencoder reconstructs the input from a corrupted version of it."

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Coding-Matrices

Here are a few exercises to get you started with coding matrices. The exercises start off with vectors and then get more challenging

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Android-Image-Classifier

Bounding-Box-Generator

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Leetcode_solutions

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Images-as-Numerical-Data

Read in and display the image¶

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Beer_Label_Classification

Create a beer label classifier using SIFT, SURF, ORB.

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Inexact-Move-Function

Calculates the inexact moves by a robot

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Visualizing-RGB-Channels

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Guide-to-Mathematical-Notation

Becoming "Wikipedia proficient"

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Normalized-Sense-Function

In this notebook, let's go over the steps a robot takes to help localize itself from an initial, uniform distribution to sensing and updating that distribution and finally normalizing that distribution.

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Medics

Gaussian-Calculations

For uncertain, continuous quantities, such as the estimated location of a self-driving car, we use Gaussians to represent uncertainty in that quantity. The smaller the variance, the more certain we are about a quantity.

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Text-Sentiment-Analysis

The Sentiment Analysis evaluates text input and returns a sentiment score for each document, ranging from 0 (negative) to 1 (positive). This capability is useful for detecting positive and negative sentiment in social media, customer reviews, and discussion forums.

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Gaze-tracker

Character-Level-LSTM

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!

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Sense-Function

n this notebook, let's go over the steps a robot takes to help localize itself from an initial, uniform distribution to sensing and updating that distribution

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Predict-Function

After performing a parameter update, which is done after some new measurement is collected, the next step is to incorporate motion into our Gaussian calculations. Recall that, as we estimate the location of a robot or self-driving car: * the measurement update *increases* our estimation certainty * the motion update/prediction *decreases* our certainty

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K-NEAREST-NEIGHBOURS

In this Lab you will load a customer dataset related to a telecommunication company, clean it, use KNN (K-Nearest Neighbours to predict the category of customers., and evaluate the accuracy of your model. Let's learn about KNN and see how we can apply it real world problems.

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Attention-Basics

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.

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Data-Leakage-Detection

Data leakage is an uncontrolled or unauthorized transmission of classified information to the outside. It poses a serious problem to companies as the cost of incidents continues to increase. Many software solutions were developed to provide data protection. However, data leakage detection systems cannot provide absolute protection. Thus, it is essential to discover data leakage as soon as possible. The purpose of this research is to design and implement a data leakage detection system based on special information retrieval models and methods.

New-Mean-and-Variance

Now let's take the formulas from the example below and use them to write a program that takes in two means and variances, and returns a new, updated mean and variance for a gaussian. This step is called the parameter or measurement update because it is the update that happens when an initial belief (represented by the blue Gaussian, below) is merged with a new piece of information, a measurement with some uncertainty (the orange Gaussian).

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Optical-Flow-and-Motion-Vectors

Optical flow tracks objects by looking at where the *same* points have moved from one image frame to the next. Let's load in a few example frames of a pacman-like face moving to the right and down and see how optical flow finds **motion vectors** that describe the motion of the face!

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Accuracy-and-Misclassification

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).

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Interacting-with-a-Car-Object

In this notebook, you've been given some of the starting code for creating and interacting with a car object.

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LSTM-Structure-and-Hidden-State

This hidden state is a function of the pieces of data that an LSTM has seen over time; it contains some weights and, represents both the short term and long term memory components for the data that the LSTM has already seen.

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Foveal-Hyperplasia-classification

this proof-of-concept study reveals the first use of AI in distinguishing between normal and pathological retinal development, with an application in paediatric ophthalmology to detect the presence and severity of foveal hypoplasia from OCT images.

Retinal-OCT-Scan-segmentation

We have aimed to create a retinal OCT scan segmentation system that predicts all ten layer segments present on retina.

-LSTM-for-Part-of-Speech-Tagging

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.

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