–[kamakshi Ramamurthy] (https://github.com/Kamakshi8104) –Sarvesh Badgujar
The ability of computers to do jobs with what we refer to as artificial intelligence has advanced significantly in the age in which we live. The ability to build software that allows a machine to learn for itself from massive data sets and enhance its usefulness over time is known as machine learning.
Scene Graph Generator is the project we’re about to embark on. It entails using a machine learning model to detect the objects in a given image and Generates graphs to establish relationship between various detected objects in the form of the graph data structure.
Computer vision is one of the fields of artificial intelligence that trains and enables computers to understand the visual world. Computers can use digital images and deep learning models to accurately identify and classify objects and react to them
Computer vision in AI is dedicated to the development of automated systems that can interpret visual data (such as photographs or motion pictures) in the same manner as people do. The idea behind computer vision is to instruct computers to interpret and comprehend images on a pixel-by-pixel basis. This is the foundation of the computer vision field. Regarding the technical side of things, computers will seek to extract visual data, manage it, and analyze the outcomes using sophisticated software programs. (edited)
Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.
Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.
Neural networks rely on training data to learn and improve their accuracy over time. However, once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts. One of the most well-known neural networks is Google’s search algorithm.
New things learned this week
Linear Algebra 3b1b
Learnt linear algebra from the 3b3b playlist. Gave a pretty cool viewpoint of imagining the vectors and vector operations which we used to perform before in just the terms of algebra before.
Neural Networks 3b1b
Learnt about machine learning and neural networks from 3b1b and how they work which set a good background for diving into logistic regression and the coding part of neural networks.
Neural Networks Coursera
week 1 and week 2
Learnt about numpy, logistic regression and using vectorization in code in numpy for logistic regression. Saw how numpy and vectorization can make the code much more faster and efficient. Hence making the neural network easy to train. Did some assignments on numpy and logistic regression using numpy to get a better understanding. #some_honourable_mentions Struggled with the week 2’s final assignment of identifying cats . It was a brain draining assignment totally filled with the complex implementation of the code on all the topics learned till now .
The Members of the project are :
Kamakshi Ramamurthy - A second year B.Tech computer science student at VJTI Interests- coding, software development. Sarvesh Badgujar - A second year B.Tech information technology student at VJTI Interests- padhai chodke sab kuch.