NumPy

The calculation of the norm of vectors is essential in both artificial intelligence and quantum computing for tasks such as feature scaling, regularization, distance metrics, convergence criteria, representing quantum states, ensuring unitarity of operations, error correction, and designing quantum algorithms and circuits. You will learn how to calculate the Euclidean (norm/distance), also known as the L2 norm, of a single-dimensional (1D) tensor in Python libraries like NumPy, SciPy, Scikit-Learn, TensorFlow, and…

Master Computer Vision by building a CNN from scratch all by yourself.Continue reading on Towards Data Science » Master Computer Vision by building a CNN from scratch all by yourself.Continue reading on Towards Data Science » FOLLOW US ON GOOGLE NEWS Read original article here Denial of responsibility! Techno Blender is an automatic aggregator of the all world’s media. In each content, the hyperlink to the primary source is specified. All trademarks belong to their rightful owners, all materials to their…

LLMs do Natural Language Processing (NLP) to represent the meaning of the text as a vector. This representation of the words of the text is an embedding. The Token Limit: The LLM Prompting Biggest Problem Currently, one of the biggest problems with LLM prompting is the token limit. When GPT-3 was released, the limit for both the prompt and the output combined was 2,048 tokens. With GPT-3.5, this limit increased to 4,096 tokens. Now, GPT-4 comes in two variants. One with a limit of 8,192 tokens and another with a limit of…

Cover Image by AuthorAcquire a deep understanding of the inner workings of t-SNE via implementation from scratch in pythonI have found that one of the best ways to truly understanding any statistical algorithm or methodology is to manually implement it yourself. On the flip side, coding these algorithms can sometimes be time consuming and a real pain, and when somebody else has already done it, why would I want to spend my time doing it — seems inefficient, no? Both are fair points, and I am not here to make an argument…

Multiprocessing is a powerful tool that enables a computer to perform multiple tasks at the same time, improving overall performance and speed. In this article, we will see how we can use multiprocessing with NumPy arrays.NumPy is a library for the Python programming language that provides support for arrays and matrices. In many cases, working with large arrays can be computationally intensive, and this is where multiprocessing can help. By dividing the work into smaller pieces, each of which can be executed…

Start Your Coding for Computer vision with PythonPhoto by Dan Smedley on UnsplashMotivationWe, human beings, perceive the environment and surroundings with our vision system. The human eye, brain, and limbs work together to perceive the environment and act accordingly. An intelligent system can perform those tasks which require some level of intelligence if done by a human. So, for performing intelligent tasks computer/machine needs a vision system. The camera and image are fed to the computer. Computer vision and Image…

Simple implementation of neural networks using standard Python libraryPhoto by Kevin Canlas on UnsplashAI has been developing at an alarming rate for the last several years. There are already plenty of high-level frameworks that enable you to build the model and run the training without having to worry about all the technical details. That’s great news. Not only does it make things easier, it also makes AI algorithms available to more people. On the other hand, it comes at a price. We don’t have to deeply understand the…

In this article, I’ll be explaining how to generate boolean arrays in NumPy and utilize them in your code.In NumPy, boolean arrays are straightforward NumPy arrays with array components that are either “True” or “False.”Note: 0 and None are considered False and everything else is considered True.Examples:Input: arr = Output: Explanation: 1 is considered as True and 0 is considered as False.Input: arr = Output: Method 1: Using dtypeEvery ndarray has an associated data type (dtype) object. This data type object informs us…

Beginner and advanced examples of Stacking in Numpy — Learn how to join a sequence of arrays easilyPhoto by Brigitte Tohm on UnsplashNumpy is an amazing library for data science and machine learning, so there’s no way around it if you want to become a data professional. Mastering the ins and outs of the package is mandatory because there’s no point in reinventing the wheel — pretty much anything you can think of has already been implemented.Today you’ll learn all about np stack — or the Numpy’s stack() function. Put…

Definitions, rules and examplesPhoto by Jean-Guy Nakars on UnsplashNumPy offers fast calculations via vectorisation that avoids the use of slow Python loops. Vectorisation is also available when using binary ufuncs, such as addition or multiplication, with the added benefit that arrays do not need to have the same shape. Operations with arrays of different shape is known as broadcasting and can be particularly confusing, especially with multidimensional arrays, or when both arrays need to be stretched.There are many…