#Nan Computing Definition

What came first? Software or Hardware? 👨🏻‍💻 The evolution of computers began long before modern software existed. During World War II, engineers created the first general-purpose electronic computer, ENIAC, in 1945. It operated without any stored software or instruction memory, relying instead on manual wiring, switches, and plugboards for programming. Each task required physical reconfiguration, making ENIAC a purely hardware-driven system. This marked the foundation of electronic computing before the concept of “stored programs” even existed. In 1945, John von Neumann introduced the revolutionary idea of storing instructions in memory through his EDVAC report, which became the blueprint for modern computing. Just three years later, in 1948, the Manchester Baby became the world’s first stored-program computer capable of running real software. This breakthrough transformed hardware from a static machine into a dynamic system that could execute flexible programs. From this moment, software and hardware evolved together, forming the inseparable core of every computer ever built. #computer #software #hardware #history #it #origins

Most people hear “neural network”… but never see one think. This clip shows a tiny AI recognizing handwritten digits 0–9 in real time. Pixels flow in, neurons fire, connections activate, and one number rises above the rest. You can literally watch the network learn as neurons light up and the output boxes fill. AI isn’t copying the digit — it’s combining weighted signals and choosing the strongest pattern. This small network works the same way big AI models do… just at a scale humans can finally visualize. Wild to see it happen live. 👇 Want more breakdowns like this? Follow @infinitemindsai for daily insights content that keep you ahead in AI, Business & Tech ⚡ . . 👉 @infinitemindsai 👉 @infinitemindsai 👉 @infinitemindsai . Credit: brilliant.org neural network visualization, how neural networks work, AI neurons firing, handwritten digit recognition AI, neural network demo, machine learning visualization, AI decision making, deep learning basics, simple neural network example, neural network activation, AI pattern recognition, neural network layers explained, AI weighted signals, how AI predicts images, neural network tutorial, digit classification AI, machine learning beginner demo, AI thinking process, neural networks simplified, visualizing AI models #ArtificialIntelligence #MachineLearning #NeuralNetworks #DeepLearning #AIexplained #TechEducation #FutureOfAI #AIvisualization #MLtutorial #InfiniteMindsAI

Quantum computers, by definition, are devices that store and process data by representing it using the inherent properties of quantum systems. They consist of a small number of fundamental components called qubits, which are controlled by a straightforward set of rules—rotations of the vector that symbolizes the quantum state. But what are they actually? what can they actually do? Sign up for Black Opal here: https://black.q-ctrl.com/signup?utm_source=anastasia-marchenkova Now, before we start, pause the video, go ahead and sign up for Black Opal so you can learn along with me. #blackopal #learnquantum #quantumcomputing #physics #quantumphysics #physicist #scientist #sciencefacts #physicsfacts #quantumcomputer #security #quantumtechnologies #womanintech #tech #innovation #computing #computers

Mathematics of an Artificial Neural Network

“Look at this… this is an incredibly beautiful computer.” Jensen Huang just unveiled NVIDIA’s Vera Rubin system, and the numbers are insane. This single machine delivers 100 petaFLOPS of AI performance. For context: 🔹 The DGX-1 Jensen delivered to OpenAI ~9 years ago 🔹 This new unit = 100× the power of that system 🔹 And one Vera Rubin can replace the equivalent of 25 full racks of older AI compute hardware This is what real hardware innovation looks like. The entire AI industry is accelerating because NVIDIA keeps compressing a data center’s worth of compute into something you can roll through a door. AI isn’t just getting smarter — the machines powering it are becoming monsters. --- What do you think — Is NVIDIA too far ahead, or is this just the beginning? --- #nvidia #jensenhuang #aihardware #datacenters #technews

1)Single-Neuron Network (Core Learning) https://github.com/JohnMachado11/Neural-Networks-from-Scratch-in-Python 2) Binary Classifier Neural Network https://github.com/abdulwadoodfaazli/Neural-Network-From-Scratch 3) Multi-Layer Neural Network https://github.com/KirillShmilovich/MLP-Neural-Network-From-Scratch Want the full ML roadmap to get from zero to actual understanding? It’s in my bio.

Convolutional Neural Networks (CNNs) are deep learning models used to process structured grid data such as images. In the forward pass of a CNN, the input (such as an image) is passed through a kernel or filter that slides across the input with a stride. This kernel performs element-wise multiplications on small patches of input, resulting in a feature map that highlights key features like edges or textures. The stride determines how much the kernel moves at each step, which influences the size of the feature map. After applying multiple kernels to the input, the resulting feature maps are frequently flattened into a one-dimensional vector, which can then be passed through fully connected layers to produce the final output. C: far1din #machinelearning #deeplearning #math #computerscience #engineering

Mathematics of an Artificial Neural Network . . . #coding #programming #softwaredeveloper #artificialintelligence #aitypes #ai #ml #deeplearning #neuralnetwork #python #java #nlp #llm #models #gpt5 #genai #codehelping.com

Mathematics of an Artificial Neural Network

The world’s first neuromorphic supercomputer is moving closer to reality after researchers at Sandia National Laboratories (SNL) in the US demonstrated a novel algorithm that uses neuromorphic hardware to solve partial differential equations (PDEs). Comment down your thoughts below 👇 . . . . . . #equations #maths #science #physics #differentiation #mathsisfun #neuromorphiccomputing #computers #ai #artificialinteligence #development #follow #instagram

A Neural Processing Unit (NPU) is a specialized chip designed to handle artificial intelligence and machine learning tasks with remarkable speed and energy efficiency. It powers complex applications like image recognition, sound processing, and gaming, enabling our devices to work smarter and more efficiently. By boosting performance and optimizing resource use, NPUs have become a game-changing innovation in today’s AI-driven world. [Media: @codcoders]

TOP 5 REASONS TO BECOME A NETWORK ENGINEER 1. High Demand and Job Security With the constant growth of internet services, cloud computing, IoT, and remote work, network engineers are in high demand globally. Job security is strong due to the essential role networks play in nearly every industry. 2. Attractive Salary Potential Network engineering offers competitive salaries, even at entry-level. With certifications and experience (like CCNA, CCNP, or CISSP), the pay scale increases significantly. Senior roles such as Network Architects or Cloud Network Engineers can earn six figures. 3. Diverse Career Paths You can specialize in areas such as: Cybersecurity Cloud Networking (AWS, Azure, GCP) Wireless Networking DevOps and Automation This flexibility keeps the career engaging and future-proof. 4. Continuous Learning and Growth The field evolves rapidly, providing opportunities to stay on the cutting edge of technology. You’ll consistently learn new tools, protocols, and systems, making the job intellectually rewarding. 5. Critical Impact and Responsibility Network engineers play a vital role in maintaining connectivity, uptime, and security for businesses, governments, and users. Your work ensures communication, productivity, and security—core pillars of the digital world.