#Deep learning algorithms

[krissym72]

How Can You Extend an Image with AI?

[frank-olivier]

The Mathematical Foundations of Machine Learning

In the world of artificial intelligence, machine learning is a crucial component that enables computers to learn from data and improve their performance over time. However, the math behind machine learning is often shrouded in mystery, even for those who work with it every day. Anil Ananthaswami, author of the book "Why Machines Learn," sheds light on the elegant mathematics that underlies modern AI, and his journey is a fascinating one.

Ananthaswami's interest in machine learning began when he started writing about it as a science journalist. His software engineering background sparked a desire to understand the technology from the ground up, leading him to teach himself coding and build simple machine learning systems. This exploration eventually led him to appreciate the mathematical principles that underlie modern AI. As Ananthaswami notes, "I was amazed by the beauty and elegance of the math behind machine learning."

Ananthaswami highlights the elegance of machine learning mathematics, which goes beyond the commonly known subfields of calculus, linear algebra, probability, and statistics. He points to specific theorems and proofs, such as the 1959 proof related to artificial neural networks, as examples of the beauty and elegance of machine learning mathematics. For instance, the concept of gradient descent, a fundamental algorithm used in machine learning, is a powerful example of how math can be used to optimize model parameters.

Ananthaswami emphasizes the need for a broader understanding of machine learning among non-experts, including science communicators, journalists, policymakers, and users of the technology. He believes that only when we understand the math behind machine learning can we critically evaluate its capabilities and limitations. This is crucial in today's world, where AI is increasingly being used in various applications, from healthcare to finance.

A deeper understanding of machine learning mathematics has significant implications for society. It can help us to evaluate AI systems more effectively, develop more transparent and explainable AI systems, and address AI bias and ensure fairness in decision-making. As Ananthaswami notes, "The math behind machine learning is not just a tool, but a way of thinking that can help us create more intelligent and more human-like machines."

The Elegant Math Behind Machine Learning (Machine Learning Street Talk, November 2024)

Matrices are used to organize and process complex data, such as images, text, and user interactions, making them a cornerstone in applications like Deep Learning (e.g., neural networks), Computer Vision (e.g., image recognition), Natural Language Processing (e.g., language translation), and Recommendation Systems (e.g., personalized suggestions). To leverage matrices effectively, AI relies on key mathematical concepts like Matrix Factorization (for dimension reduction), Eigendecomposition (for stability analysis), Orthogonality (for efficient transformations), and Sparse Matrices (for optimized computation).

The Applications of Matrices - What I wish my teachers told me way earlier (Zach Star, October 2019)

Transformers are a type of neural network architecture introduced in 2017 by Vaswani et al. in the paper “Attention Is All You Need”. They revolutionized the field of NLP by outperforming traditional recurrent neural network (RNN) and convolutional neural network (CNN) architectures in sequence-to-sequence tasks. The primary innovation of transformers is the self-attention mechanism, which allows the model to weigh the importance of different words in the input data irrespective of their positions in the sentence. This is particularly useful for capturing long-range dependencies in text, which was a challenge for RNNs due to vanishing gradients. Transformers have become the standard for machine translation tasks, offering state-of-the-art results in translating between languages. They are used for both abstractive and extractive summarization, generating concise summaries of long documents. Transformers help in understanding the context of questions and identifying relevant answers from a given text. By analyzing the context and nuances of language, transformers can accurately determine the sentiment behind text. While initially designed for sequential data, variants of transformers (e.g., Vision Transformers, ViT) have been successfully applied to image recognition tasks, treating images as sequences of patches. Transformers are used to improve the accuracy of speech-to-text systems by better modeling the sequential nature of audio data. The self-attention mechanism can be beneficial for understanding patterns in time series data, leading to more accurate forecasts.

Attention is all you need (Umar Hamil, May 2023)

Geometric deep learning is a subfield of deep learning that focuses on the study of geometric structures and their representation in data. This field has gained significant attention in recent years.

Michael Bronstein: Geometric Deep Learning (MLSS Kraków, December 2023)

Traditional Geometric Deep Learning, while powerful, often relies on the assumption of smooth geometric structures. However, real-world data frequently resides in non-manifold spaces where such assumptions are violated. Topology, with its focus on the preservation of proximity and connectivity, offers a more robust framework for analyzing these complex spaces. The inherent robustness of topological properties against noise further solidifies the rationale for integrating topology into deep learning paradigms.

Cristian Bodnar: Topological Message Passing (Michael Bronstein, August 2022)

Sunday, November 3, 2024

[bahadurislam011444]

Unveiling the Best SEO Worker in Bangladesh: Driving Digital Success

[billloguidice]

Become a better coder by harnessing math and deep learning

Become a better coder by harnessing math and deep learning #sale #math #programming #coding #education #mathematics #maths #algorithms #cryptography #datastructures #book #books

Level up your programming fundamentals with one of these great bundle options, available here. Dive into math, machine learning, and other crucial disciplines and take your programming skills to the next level! The latest bundle from Manning Publications will help you harness math to write better code, utilize deep learning across various languages and applications, and get up to speed on…

[villainessbian]

It has to be stated as a defiant position because despite there being "no need to inflict that boredom on other people - other artists," the boredom of a few re: actually doing art or respecting others' work was and still is inflicted on everyone through AI.

And for clarification:

"People who think the lack of autonomy is an interesting artistic statement"? Not when making art they don't. The statement can be about a lack of of autonomy, or about making things themselves despite constraints (which is how most forms of poetry function). Not having autonomy and not making something in the first place is not a statement, it's a lack of statement. Silence isn't speech. Definitionally.

"People who are physically disabled in a way that prevents them from engaging with 'traditional' art" is very exactly no one who would artistically benefit from the plagiarism machine. Watching, hearing, smelling, touching, reading, existing in, just knowing any piece of art in any shape or form is engaging with it. If they can't do that with the rest they can't do it with dall-e. You mean people who physically can't create things but somehow are still able to communicate something to the machine.

And to that:

The robot isn't making them able, it's literally a third party copying people who were able.

It's less involved than ordering at a subway, which doesn't make you a "sandwich maker" even if you decided what to put in it. Just another customer. The process is still handled by someone else, your options are still limited by outside forces, and you still only asked for the ingredients.

It all relies on the assumption that the skills displayed are irrelevant to the end product - that a flawless monochrome is equal in value to a click with the paint bucket tool, since they're the same production. There's a reason why art is considered a creative process, not an end result.

Ultimately, this line of thought about "making art accessible" is about the supposed tragedy of someone having a vision without the skills to realise it. But that was always a solved issue. If you can develop these skills, develop them. If you can't or don't want to, commission someone. They're the only ways for you to actually be involved in the creation. Tweaking a machine until it's "yeah, close enough" isn't involvement. It's boredom. It's not caring about what is there. And for some reason that only applies to a few types of art, hm? If I tweak an android to run faster than Usain Bolt it doesn't make me an athlete. If I input a recipe setting in my Thermomix it doesn't make me a competent cook. Installing an autopilot doesn't make me a great pilot. And with my body I can't be any of these things.... and they're all damn closer to accessibility than midjourney is. You want to know what disabled people need? If I need something fetched - e.g. at the pharmacy - and my joint issues prevent me, then a small, fast robot that knows the way would be great. My eyes aren't good enough to visually check for a number of important things in the kitchen and my brain doesn't process time normally, so an automatic timer for cooking times with things that are already checked everywhere saves me a lot of time and food and health issues. Not a single time have I needed openai to make something. If I draw something, maybe my poor vision shows and I get the colours wrong. I don't have a robot colour-pick for me from the top 10 reposted painters online. It looks the same to me but not to you, and that's a much stronger statement about lack of autonomy than you not seeing it or me not making it. If I write it'll be my author's voice, not predictive text with a non-confrontational, PC-according-to-Silicon-Valley-execs tone. If I decide to try composing it will never be "an epic tune in the style of <insert currently-viral group>". And that's the difference between inspiration and botting.

As gen-AI becomes more normalized (Chappell Roan encouraging it, grifters on the rise, young artists using it), I wanna express how I will never turn to it because it fundamentally bores me to my core. There is no reason for me to want to use gen-AI because I will never want to give up my autonomy in creating art. I never want to become reliant on an inhuman object for expression, least of all if that object is created and controlled by tech companies. I draw not because I want a drawing but because I love the process of drawing. So even in a future where everyone’s accepted it, I’m never gonna sway on this.

[toilunderthemoon]

if i start to read a post but notice that it has like 10k + notes, im always at least slightly more skeptical about how funny/true/logically sound it is

[la-principessa-nuova]

A fun way to think about this, for people who don’t know what an algorithm really is, is that algorithm is basically a fancy word for recipe. It’s a set of instructions to take a set of inputs (ingredients) and produce a desired output.

When a site produces an algorithm to recommend content, the input comes from the information they have about you, other users, and the content they are ranking, and the output is an ordered list of top picks for you. It basically takes in everything and then the person writing the algorithm chooses which inputs to actually use.

Imagine that like as the entire grocery store is available to be used, so the person writing the recipe lists out the specific ones used within the steps of the recipe as ingredients to use.

Then they write all the steps in a way that another cook could understand, maybe with helpful notes (comments) along the way describing why they did certain things. And in the end they have a recipe that someone else could follow, make informed changes to, explain the reasoning for decisions, etc.

That’s a traditional algorithm. Sorting by a single field like kudos is the simplest form of this, like a recipe for toast.

Ingredients:

Bread, 1 slice

Instructions:

Put the bread in the toaster.

Pull down the lever.

Wait until it pops up.

Enjoy your toast!

Is that a recipe? Yes, clearly.

Now let’s consider what the equivalent of ML-based recommendation systems (the key differentiator of what people often refer to as “the algorithm”) is:

Ingredients:

The entire grocery store

Instructions:

Put the grocery store into THE MACHINE

THE MACHINE should be set to 0.135, 0.765, 0.474, 0.8833… (this list continues for hundreds of entries)

Consume your personalized Feed™️

Is that a recipe? Technically yes.

“Ao3 needs an algorithm” no it doesn’t, part of the ao3 experience is scrolling through pages of cursed content looking for the one fic you want to read until you get distracted by a summary so cursed that it completely derails your entire search

[rosangelarodriguezpedro]

🔝 Top 3 Libros sobre Inteligencia Artificial (IA)

🥇 1. “Life 3.0: Being Human in the Age of Artificial Intelligence” – Max Tegmark https://amzn.to/3RkIXJf 📘 Idioma: Inglés (disponible en español) 🧠 Temas: Futuro de la humanidad con IA, ética, conciencia artificial, posibles escenarios. 🌐 Ideal para: Público general, futuristas, líderes, estudiantes. 💬 Frase clave: “La IA no es el futuro, es el presente.” ⭐️ Valoración: 4.7/5 🥈 2.…

[raffaellopalandri]

The Epistemology of Algorithmic Bias Detection: A Multidisciplinary Exploration at the Intersection of Linguistics, Philosophy, and Artificial Intelligence

We live in an increasingly data-driven world, where algorithms permeate nearly every facet of our existence, from the mundane suggestions of online retailers and products to the critical decisions impacting healthcare and justice systems. Photo by Tara Winstead on Pexels.com These algorithms, while often presented as objective and impartial, are inherently products of human design and the data…

[msn-technology]

Top 9 AI Tools for Data Analytics in 2025

In 2025, the landscape of data analytics is rapidly evolving, thanks to the integration of artificial intelligence (AI). AI-powered tools are transforming how businesses analyze data, uncover insights, and make data-driven decisions. Here are the top nine AI tools for data analytics that are making a significant impact: 1. ChatGPT by OpenAI ChatGPT is a powerful AI language model developed by…

[aifrontiers]

🌐 Explore the Foundations of Deep Learning in AI!

Curious about how machines learn, adapt, and think? In this lesson, we’re unpacking the essentials of deep learning—from neural networks and data processing to the ethics of AI. Discover how AI is transforming our world, one smart decision at a time!

[danieldavidreitberg]

🌟 Evolutionary Computation is shaping the future of AI! 🚀 Dive into Daniel Reitberg's insights on how algorithms evolve and innovate. Don't miss it! #AI #EvolutionaryComputation #FutureTech #Innovation

[billloguidice]

Learn AI and machine learning with this value-packed bundle of books!

Learn AI and machine learning with this value-packed bundle of books! #sale #ai #machinelearning #deeplearning #randomness #algorithmic #book #ebook #datascience #statistics #programming #coding

Here’s the link to check out the five book bundle options with up to 19 books! Learn the skills and techniques you need to succeed in artificial intelligence and machine learning with this bundle of computer engineering courses from No Starch Press. Inside this extensive library, you’ll explore the ins and outs of deep learning and algorithmic thinking, master the art of machine learning, and…

[niggadiffusion]

The Soul in the Circuit: How Generative AI is Flipping the Script on Art

In the quiet corners of digital imagination, something wild is happening. Machines are sketching scenes that never were, spinning beats no one’s ever danced to, and weaving pixels into poetry. This is generative AI art—where creativity isn’t a solo act anymore. It’s a conversation between human intuition and machine intelligence, a new kind of collaboration unfolding at the edge of what we…

[ingoampt]

Day 9 _ Deep Learning _ Perception

Here I explain perception in 3 different ways which can show same purpose but first only explain the mathematic behind perception to show what’s the mathematic behind Perception when we import perception in a deep learning code , we also show this mathematic in a code based so how the code look if we do not import perception and wanna do it with mathematic. Lastly, we show how it look like ima a…