AI-powered search & chat for Data / Computer Science Students
Evolutionary Architecture: Supporting Constant Change
Through continuous improvement, technology adoption, and not being complacent The first principle of an evolutionary architecture is to enable incremental change in an architecture over time — Though...
Read more at Better Programming
Evolving Neural Networks
For the past decade, deep learning has dominated the machine learning landscape, often to the exclusion of other techniques. As a data scientist, it’s important to have a variety of tools at your…
Read more at Towards Data Science
Evolving Deep Neural Networks
Deep learning architectures are getting harder to design, but evolutionary algorithms may help us overcome this. This review presents important recent research in this matter.
Read more at Towards Data Science
Use Fitness Functions for Evolving Architecture
Introduction to fitness functions concept with simple examples Continue reading on Better Programming
Read more at Better Programming
The evolution from monolithic applications to microservices
Evolution is the process by which species of organisms change over generations through mechanisms such as mutation, genetic drift, and natural selection.The concept of evolution can be applied not onl...
Read more at Javarevisited
From Blueprints to Realities: The Evolutionary Journey of Software Architecture
This article is an excerpt from my Udemy video course “ Decoding Software Architecture .” Today’s article tackles the complex and evolving landscape of software architecture. But it’s not another trea...
Read more at Level Up Coding
Architecture as a Graph
In this article, we unveil some of our recent results and methodologies implemented at Spacemaker AI over the past quarter. This project aimed at supporting Spacemaker’s long term vision, as one of…
Read more at Towards Data Science
Evolutionary approaches towards AI: past, present, and future
Since roughly 2012 [1], the explosive growth in AI has been almost entirely driven by neural network (deep learning) models trained by back-propagation (“backprop”). This includes models for image…
Read more at Towards Data Science
How Programming with Heuristic Emergence Can Lead to Artificial Evolution
Go on an adventure that explores how NP-Hard problems lead to the use of heuristic algorithms that might ultimately lead us to recreate what the Universe has already achieved. Intelligence.
Read more at Analytics Vidhya
NEAT: An Awesome Approach to NeuroEvolution
Recently, I’ve been doing a lot of reading about something called neuroevolution. At a high-level, the idea is very simple. Instead of relying on a fixed structure for a neural network, why not allow…...
Read more at Towards Data Science
Is Artificial Intelligence Evolving?
Artificial intelligence was once the dream of science fiction writers. Isaac Asimov devised three rules to govern robots that could think like humans well before computers were being put to use to…
Read more at Becoming Human: Artificial Intelligence Magazine
Evolving Neural Networks in JAX
“So why should I switch from
Evolution in Your Code — Understanding and Coding Genetic Algorithm From Scratch — Part 1
Have you ever looked at nature and wondered at how organisms evolved, adapted, and survived over millennia? What if I told you that you, sitting in front of your computer, hold the power to simulate a...
Read more at Towards AI
🔎🔍 Edge#69: Search Strategies in Neural Architecture Search
In this issue: we explore the search strategies in neural architecture search; we learn about Google’s evolved transformer that is a killer combination of transformers and NAS; we discuss Microsoft’s ...
Read more at TheSequence
Architectures — Part 2
In this lecture we explore deeper architectures auch as Inception V2 and V3 and explain the concept of exponential feature reuse.
Read more at Towards Data Science
A preliminary inquiry into the limits of AI evolution
The reason for this essay has far less to do with making assertions or answering questions, and more to do with asking questions or clearing the ground for the foundations of assertions. It began…
Read more at Towards Data Science
Evolutions in Data Science
Obtain, Scrub, Explore, Model, and iNterpret was the name of the game in 2010. What about now?
Read more at Towards Data Science
🦾Transformer Architectures Recap
As requested by many of our readers, before diving deeper into Self-Supervised Learning, we put together a recap of the Transformer Architectures series. As a proverb says: Repetition is the mother of...
Read more at TheSequence
Neuroevolution — evolving Artificial Neural Networks topology from the scratch
This article presents how to build and train Artificial Neural Networks by NEAT algorithm. It will consider weakness of current Gradient Descent based training methods and shows a way to improve it.
Read more at Becoming Human: Artificial Intelligence Magazine
Evolutionary Computation: A hidden gem of CI
Defining AI leads to many arguments, so let’s talk about something concrete: Computational Intelligence. Three branches make up Computational Intelligence — Neural Networks, Evolutionary Computation…
Read more at Towards Data Science
🔎◼️ Edge#71: What is Differentiable Architecture Search?
In this issue: we discuss Differentiable Architecture Search – DARTS; we explore how Facebook-Berkeley-Nets (FBNet) use NAS to produce efficient CNNs; we dive into Google’s AdaNet – a lightweight Auto...
Read more at TheSequence
The POE Model of Bio-Inspired Hardware Systems
AI-generated image (craiyon) A 1997 Classic, published in the inaugural issue of the IEEE Transactions on Evolutionary Computation: A Phylogenetic, Ontogenetic, and Epigenetic View of Bio-Inspired Ha...
Read more at Towards AI
Architectures — Part 3
In this lecture, we introduce ResNets and discuss their interpretation as ensemble and a gradual shift of representation. We also hint at a third interpretation.
Read more at Towards Data Science
Architectures — Part 5
In this lecture, we discuss ideas to build systems to automatically generate deep learning architectures and the limitations of these methods.
Read more at Towards Data Science- «
- ‹
- …