Machine-Learning

As machine learning systems are increasingly used in critical areas like finance, employment, and criminal justice, it’s essential to ensure these models are fair and do not discriminate against certain groups. In this post, I will explore the concept of fairness in machine learning. Related discussions on algorithmic accountability often draw on tools such as HuggingFace model cards, which document bias evaluations for publicly released models. Defining Fairness Fairness in machine learning can be understood in several ways: ...

Paper Authors: Akshat Gupta, Laxman Singh Tomar, Ridhima Garg Abstract Cyber attacks deceive machines into believing something that does not exist in the first place. However, there are some to which even humans fall prey. One such famous attack that attackers have used over the years to exploit the vulnerability of vision is known to be a Homoglyph attack. It employs a primary yet effective mechanism to create illegitimate domains that are hard to differentiate from legit ones. Moreover, as the difference is pretty indistinguishable for a user to notice, they cannot stop themselves from clicking on these homoglyph domain names. ...

Date of Event: September 6, 2019 Venue: Dr. Akhilesh Das Gupta Institute of Technology & Management Event Overview: In an exhilarating day filled with insights and hands-on activities, the Machine Learning workshop organized by Intel turned out to be a landmark event for aspiring ML enthusiasts. Our auditorium, buzzing with the energy of keen learners, became a crucible for innovation and deep understanding in the rapidly evolving field of Machine Learning. ...

View on Packt · GitHub Publisher: Packt Publishing Author: Akshat Gupta About the Book Hands-on Deep Learning with TensorFlow 2.0 is a practical guide to building, training, and deploying deep learning models using TensorFlow 2.0 and Keras. The book is designed for practitioners who want to move beyond theory and build real neural network systems from scratch. What It Covers Neural network fundamentals — perceptrons, activation functions, backpropagation CNNs — convolutional layers, pooling, image classification pipelines RNNs and LSTMs — sequence modelling, text classification, time series Transfer learning — fine-tuning pre-trained models for custom tasks Model deployment — TensorFlow Serving, SavedModel format, production considerations TensorFlow 2.0 specifics — eager execution, tf.function, Keras functional API Who It’s For Developers and data scientists who are comfortable with Python and want a hands-on introduction to deep learning using one of the most widely adopted frameworks in industry. ...

Date of Event: May 05, 2020 Venue: Online Event Overview: In May 2020, Poornima University in Rajasthan hosted a groundbreaking workshop focusing on the Role of Natural Language Processing (NLP) in Healthcare. This event provided a unique platform for healthcare professionals, technologists, and students to explore the intersection of advanced linguistic technology and healthcare applications. Highlights of the Workshop: Introduction to NLP in Healthcare: The workshop started with an overview of NLP and its evolving role in the healthcare sector, emphasizing how it transforms patient care and medical data analysis. ...

Date: April 2017 Location: Chennai, Tamil Nadu, India Organized by: Government of India Smart India Hackathon (SIH) is one of the world’s largest open innovation hackathons, organized by the Government of India to provide students a platform to solve pressing problems faced by government ministries and industries. Problem Statement Our team worked on a problem statement issued by the Ministry of Earth Sciences — one of the most technically demanding tracks in the 2017 edition, involving the processing and analysis of earth observation and environmental data. ...

Date of Event: December 17, 2019 Venue: Ujjain Event Overview: Ujjain witnessed an enriching educational event in December 2019, with a workshop dedicated to introducing the fundamentals of Artificial Intelligence (AI). Designed for beginners and enthusiasts alike, this event served as a primer to the world of AI, attracting a diverse audience from students to professionals keen on understanding this cutting-edge technology. Highlights of the Workshop: Fundamentals of AI: The workshop focused on the core principles of AI, providing a comprehensive introduction to its basic concepts and terminologies. ...

Training a state-of-the-art machine learning model is expensive. Large language models like GPT-3 required hundreds of petaflop-days of compute and millions of dollars. Yet once deployed behind an API, they are vulnerable to a surprisingly subtle attack: an adversary who never sees the weights, never reads the training data, and never touches the server — but can still steal the model by asking it questions. This is a model extraction attack, and it is one of the more underappreciated threats in production ML security. Related adversarial work — see Goodfellow et al. on FGSM — focuses on perturbing inputs to fool a model. Model extraction goes further: the attacker wants a copy of the model itself. ...