Ekimetrics’ Post

🔄 Understanding Recurrent Neural Networks (RNNs) 🔄 RNNs are designed for sequence data like time series and text. Key aspects include: 1️⃣ Sequential Data Handling: Memory: Retains information from previous steps to process current data. 2️⃣ Recurrent Layers: Hidden States: Pass information through the network over time steps. Feedback Loops: Enable connections back to previous layers. 3️⃣ Long Short-Term Memory (LSTM): Forget Gates: Decide what information to throw away from the cell state. Input Gates: Decide which new information to store. Output Gates: Control the output and what information should move forward. 4️⃣ Applications: Text Generation: Predicts the next word in a sequence. Time Series Forecasting: Predicts future data points in a sequence. #RNNs #DeepLearning #AI #MachineLearning #SequenceData #EncephAI

Did you know about the XOR problem? 🤔 In the early days of artificial intelligence, the XOR (exclusive OR) problem posed a significant challenge to neural networks. This problem, which couldn’t be solved by a single-layer perceptron, highlighted a crucial limitation in AI development during the 1960s. In fact, in 1969, Marvin Minsky and Seymour Papert famously pointed out this limitation in their book "Perceptrons" which led to a temporary decline in neural network research (often called the "AI Winter"). However, this roadblock ultimately inspired researchers to develop Multi-layer networks and Backpropagation algorithms in the 1980s, leading to the rise of deep learning as we know it today! The solution to the XOR problem wasn’t just a fix—it became the foundation for modern neural network architectures that power breakthroughs in AI today. I have tried to solve this using MLNs with help of GPT on colab, attaching the approach below. Open for suggestions, alternative approaches and discussions!!☁️ #AI #MachineLearning #NeuralNetworks #DeepLearning #TechHistory #XOR #problem

Overcoming 'catastrophic forgetting': Algorithm inspired by brain allows neural networks to retain knowledge. Inspired by the flexibility of human and animal brains, Caltech researchers have now developed a new type of algorithm that enables neural networks to be continuously updated with new data that they are able to learn from without having to start from scratch. The algorithm, called a functionally invariant path (FIP) algorithm, has wide-ranging applications from improving recommendations on online stores to fine-tuning self-driving cars - https://lnkd.in/gknDFGWA #AI #neuralnetworks

Do you want to know how AI systems operate from the inside out? Let's explore the fascinating field of neural networks today: 🧠 Artificial intelligence can be traced to neural networks which are the models of the human brain learning process. They are made up of interrelated layers of nodes that analyze complex information to arrive at conclusions and forecast outcomes. 💡 The input layer provides the means through which data is received and processed through the network. Hidden Layers: Use weighted connections to work with the data. Output Layer: Returns the last row with the outcome or forecast of the issue under evaluation. 🔍Now let’s take a closer look at how the neural networks are changing the world of finance and healthcare, for instance, by identifying patterns and trends. What do you think of Artificial Intelligence and its potential soon? Share your insights! #neuralnetworks #ai #technews #innovation #bigdataanalytics #computerscience #digitaltransformation #emergingtechnologies #datascientist #artificialintelligence #deeplearning #machinelearning #neuralnetworkarchitecture #technologicalinnovation #financialtechnology #healthcareindustry #trendingtechnology #aiapplications #neuralnetworks #aiexplained #insideneuralnetworks #datascienceworld #technologytrends #futureofai #braininspiredai #insightsofai #exploringneuralnetworks #Hattussa #Hattussaitsolutions #topitcompaniesus More details... 🌐 www.hattussa.com 📧 contact@hattussa.com ☎️ +91 9940710411

CNN??? Convolutional Neural Networks (CNNs) It all started with Kunihiko Fukushima, a renowned Japanese computer scientist, who developed “Neocognitron”, a multilayered Artificial Neural Network (ANN), in 1979. It was used for tasks like Japanese handwritten character recognition and pattern recognition, laying the foundation for CNNs. Building on this, Yann Lecun, a French computer scientist, developed a CNN named “LeNet” in the 1980s. While CNNs had immense potential, they didn’t immediately become popular due to their demand for large amounts of data and resources, especially for high-resolution images. Today, CNNs are widely used for classification, computer vision tasks, and recognition tasks. They’re at the heart of advancements like driverless cars, cancer detection, augmented reality in e-commerce, and social media algorithms. So, here’s to the incredible journey of CNN and the brilliant minds behind it! #ConvolutionalNeuralNetworks #AI #MachineLearning #DeepLearning #ComputerVision

Forward Propagation in Neural Networks. Forward propagation, or the forward pass, drives data flow from input to output in a neural network. By applying activation functions to the weighted sum of inputs, the equation Y_hat = output_activation_function(activation_function(X * W + B)) guides the transformation of inputs into predictions or outputs. Understanding this process is key to unlocking the power of neural networks for accurate predictions and intelligent decision-making. #neuralnetworks #forwardpropagation #forwardpass #deeplearning #aiengineer #ai

🔥 Paper of the Day goes to "KAN: Kolmogorov–Arnold Networks", a promising alternative to Multi-Layer Perceptrons (MLPs), the basic unit of Neural Networks! 💡 But how is it different? The key innovation is that KANs have learnable activation functions on edges instead of fixed activation functions on nodes like in MLPs. Additionally, KANs replace every weight parameter with a univariate function parametrized as a spline, eliminating linear weights altogether. The paper demonstrates that this change allows KANs to outperform MLPs in terms of accuracy and interpretability, with smaller KANs achieving comparable or better accuracy than larger MLPs in some tasks! 🤔 Will this really replace classical NNs? We need more experiments to say for sure. But this is an interesting and novel approach that is surely turning a lot of heads! 🔗 Paper link (powered by Sankshep 👋 ): https://lnkd.in/dYs-59eg #ai #ml #learning #research #learningeveryday #llm #DeepLearning #NeuralNetworks #Innovation #KANs #MLPs #TechResearch

Fun Fact Friday: The Birth of Neural Networks Did you know that the concept of neural networks dates back to the 1940s? The first artificial neural network, the "Perceptron," was developed by psychologist Frank Rosenblatt in 1957. Inspired by the human brain's neural structure, the Perceptron aimed to mimic how neurons recognize patterns and make decisions. Although the Perceptron had limitations, it laid the groundwork for future artificial intelligence and machine learning advancements. Today, neural networks are at the forefront of cutting-edge technologies, powering everything from voice assistants to self-driving cars. Dr. Murthurman at U.T. describes neural networks as "faking" human intelligence rather than replicating it. I adamantly agree with his POV; I see AI as a tool that will help augment human intelligence rather than replace it. What's your favorite milestone in the history of AI and machine learning? Share your thoughts below! #FunFactFriday #NeuralNetworks #AIHistory #MachineLearning #DidYouKnow

New Type Of Neural Network Just Dropped!! It Learns Better by Adjusting Connections Researchers created a new type of neural network called Kolmogorov-Arnold Networks (KANs) as alternatives to Multi-Layer Perceptrons (MLPs) that can learn more accurately and be understood more easily compared to standard neural networks. #AI #DeepLearning #NeuralNetwork

Although it's good to celebrate, is this attribution of "foundational discoveries and inventions that enable machine learning with artificial neural networks" problematic? Frank Rosenblatt's Perceptron?; LeCun's development of back-propagation; Fukushima; Werbos; McCarthy; Minsky; Papert; Fei-Fei Li...? Are developments in fields of machine learning & neural nets even reduceable to 2 people? Or are Hopfield & Hinton deserving of the separate accolade and kudos because of significantly more important pragmatic developments? Can we separate research in this way? Is this is a wider issue that points at the tendency to valorize the individual if NNs (perhaps all inventions?) are demonstrably developed through time via various incidents and groups of people? Is it time to update the heroic theory of invention and discovery? And how might this be done? #Nobel #Discovery #NeuralNetworks #AI