In recent years, self-supervised learning (SSL) has gained significant attention in the field of artificial intelligence (AI) and machine learning (ML). This approach to learning aims to extract useful information from unlabelled data by leveraging different techniques. In this article, we will delve deeper into self-supervised learning, its benefits, and how it has been applied… Continue reading Self-Supervised Learning
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Adversarial Attacks and Defenses in Deep Learning
Deep learning has revolutionized the field of artificial intelligence, achieving state-of-the-art performance in a wide range of applications, from image recognition to natural language processing. However, deep learning models are vulnerable to adversarial attacks, where malicious actors can craft inputs that fool the model into making incorrect predictions. In this article, we will explore the… Continue reading Adversarial Attacks and Defenses in Deep Learning
numpy.percentile() in python
numpy.percentile()function used to compute the nth percentile of the given data (array elements) along the specified axis. Syntax : numpy.percentile(arr, n, axis=None, out=None) Parameters : arr :input array. n : percentile value. axis : axis along which we want to calculate the percentile value. Otherwise, it will consider arr to be flattened(works on all the… Continue reading numpy.percentile() in python
numpy.subtract() in Python
numpy.subtract() function is used when we want to compute the difference of two array.It returns the difference of arr1 and arr2, element-wise. Syntax : numpy.subtract(arr1, arr2, /, out=None, *, where=True, casting=’same_kind’, order=’K’, dtype=None, subok=True[, signature, extobj], ufunc ‘subtract’) Parameters : arr1 : [array_like or scalar]1st Input array. arr2 : [array_like or scalar]2nd Input array. dtype… Continue reading numpy.subtract() in Python
numpy.argmax() in Python
numpy.argmax() returns the index (or indices) of the maximum element along the specified axis. Syntax – numpy.argmax(a, axis=None, out=None) a : array_like — input array. axis : int or None — axis along which to find the indices of the maximum. None flattens. out : ndarray, optional — alternative output array to store result (must… Continue reading numpy.argmax() in Python
Python Dictionary (with Examples)
What is a Python dictionary? A Python dictionary is a built-in mapping type that stores values under keys. It’s implemented with an internal hash table that maps each hashable key to a memory location for its value. Dictionaries are the go-to structure for lookups and mappings in Python. We can think of them as fast… Continue reading Python Dictionary (with Examples)
Python List (with Examples)
Introduction to Python Lists Python lists are versatile data structures that allow you to store and manipulate collections of items. A list is an ordered collection of elements enclosed in square brackets []. Each item in a list is separated by a comma. Lists are mutable, meaning their elements can be modified after creation. Python List… Continue reading Python List (with Examples)
Queue in Python
A Queue in Python is a linear data structure that follows the FIFO (First In, First Out) principle — meaning the first element added is the first one removed. Queues are widely used in task scheduling, data buffering, and asynchronous programming (like producer-consumer models). Front → [A, B, C, D] → Rear Enqueue(E) → [A,… Continue reading Queue in Python
Stack in Python
A stack is a linear data structure that follows the LIFO (Last In, First Out) principle — meaning the last element added is the first one removed. Stacks are one of the fundamental data structures in computer science, often used for: Undo/redo functionality in editors Browser back/forward navigation Function call management in recursion Expression evaluation… Continue reading Stack in Python
How to Limit Heap Size in Python?
In Python, memory management (including heap allocation) is handled automatically by the Python Memory Manager and Garbage Collector (GC). Unlike low-level languages such as C or C++, Python does not expose direct APIs to manually set or cap heap size. However, if you’re running applications in restricted environments (like containers, servers, or embedded systems), or… Continue reading How to Limit Heap Size in Python?