With so much noise out there with LLMs and GPTs, we often tend to forget the basic building blocks or the primitive concepts that led to the advancement of these technologies. Recurrent Neural Networks, or RNNs in short are one of the basic concepts that lead to a better understanding of these advanced concepts. In …
Simple. Neural Networks only understand numerical data. Their weights, their biases, and their training, all take place using numerical values, floats, and integers, yada yada. Now when we want to work with words and other textual data, we need a way to convert them to numerical representation, that’s where word embeddings come under the picture. …
Generative Adversarial Networks (GANs) are incredibly powerful in the world of machine learning. They excel at creating synthetic data that’s so close to real data, it’s almost uncanny. Today, we’re diving into how these networks work and even trying our hand at building a simple GAN framework using PyTorch. But why the term “Adversarial”? Well, …
Generative Adversarial Networks (GANs) From Scratch Read More »
With the concerns about data privacy rising and with the rise of AI models, people are increasingly concerned about their data being misused for purposes beyond their consent. As we know that data is the new oil of this century, it is a very sought after commodity. Seeing these concerns, Google introduced the concept of …
Today I will be discussing my understanding of the paper Universal Backdoor Attacks. The paper delves into another exciting exploit that can be leveraged against popular convolutional models such as Resnets. What is a Universal Backdoor A backdoor is an alternate entry to your house. In the field of computers and security in general, it …
So today I will be analyzing and providing a brief summary of my understanding of this paper titled Remember What You Want To Forget This paper presents one of the algorithms for machine unlearning. Machine Unlearning is an important practice when it comes to privacy and security in machine learning. Machine unlearning algorithms aim to …
Differential privacy is a technique devised for data analysis whilst maintaining the privacy of the individuals whose data is involved. It was first introduced in the year 2006, by Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith and has been one of the foundational pillars in the intersection of data science and privacy. At …
Previously we talked about Fast Sign Gradient Method( FGSM), we saw how this white box technique, cleverly exploits the gradients in a model, to perturb the input to give the wrong prediction from the model. Since, in this method, we perturb our input just once, a modified version of this attack does so repeatedly for …
In this post, we will be talking about the vulnerabilities that plague machine learning. Yes, in the realm of computer science, no field is void of vulnerabilities and loopholes and as we progress towards a very AI-based future, the security and robustness of machine learning models become an important aspect. What are Adversarial Attacks? The …
The special ingredient to machine learning. We learned in the last post about Linear Regression. We concluded with a cost function that we needed to minimize. Today we will see how we minimize this cost function. To recap, the cost function was : Here, h𝚯(x) is the linear regression equation that we discussed earlier( y= …