Big Data and Cognitive Computing MDPI’s Post

I’m happy to share that I’ve obtained a new certification: Advanced Learning Algorithms from Stanford University and DeepLearning.AI! #MachineLearning #NeuralNetworks #CNN #Stanford #DeeplearningAI

My latest project, where I personally collected a comprehensive dataset from LRH Hospital. This project not only includes detailed steps of data analysis and feature engineering but also explores the application of machine learning models like ANN (Artificial Neural Network Architecture) and CNN (Convolutional Neural Network) plus Embedded CNN and ANN. Additionally, I implemented and compared 8 baseline classification algorithms to establish robust benchmarks. This project serves as a fundamental guide for tackling real-world challenges and provides valuable insights for data science enthusiasts and professionals alike. I hope this work offers practical knowledge and inspires others to approach complex problems with data-driven strategies. #DataScience #MachineLearning #ArtificialIntelligence #Healthcare #DataAnalysis #FeatureEngineering #ANN #CNN #Classification #Algorithms

Automating machine learning is reaching new heights every single day. 🤖 Hey #connections ! I recently explored the field of Neural Architecture Search, or more commonly known as NAS, on my #blogpost.🧬 It is a sub-branch under the bigger chunk of AutoML, which takes AutoML to the next level by automating neural network architecture design. 💻NAS systems efficiently search for optimal architectures by balancing exploration and exploitation. The search process involves: - Defining the search space: The range of possible architectures based on modular blocks. 🔍 - Search strategy: Algorithms like Bayesian optimization, reinforcement learning, evolutionary methods, and gradient-based relaxation. 🗺️ - Performance estimation: Techniques like weight sharing, learning curve extrapolation and proxy tasks to evaluate architectures. 📈 I tried to describe my theoretical knowledge in a very concise way in the blog post. I will soon post about some practical approaches and implementation on the same. Do the read the post. Access here: https://lnkd.in/da3XG-NW #artificialintelliegence #machinelearning #neuralnetworks #deeplearning #technology #blog #theory #knowledge #community

🌐 Unveiling the secrets of crowd behavior with cutting-edge research into crowd anomaly detection! 🔬 Dive into this fascinating study, "Reactive and Proactive Anomaly Detection in Crowded Environments using Hierarchical Temporal Memory." Published in the International Journal of Machine Learning and Computing, the study explores how brains-inspired HTM models learn and adapt in real-time, heralding a new era in crowd anomaly detection. From tracking vital signs to recognising license plates, the findings have paved the way for smarter, safer crowds around the world! 🚀 Highlights: 👉 Breakthroughs in anomaly detection using HTM technology 👉 Comparative analysis with other neural network models 👉 Real-world applications: from vital sign detection to license plate recognition 🔍 "Continuous online sequence learning with an unsupervised neural network model." - Y. Cui et al. 📚 Access the full paper (.PDF), here 👉 https://lnkd.in/ewB3c2JZ #CrowdManagement #HTMResearch #AIForSafety #AnomalyDetection #IJMLC #events #eventprofs #eventsindustry #crowds #crowdprofs #globalcrowds

Introducing My PAdic Moonshine Network: A Fusion of Mathematics and AI I'm thrilled to share my latest blog post on Medium where I dive deep into the development of my innovative PAdic Moonshine Network. This project combines the advanced mathematical concepts of p-adic numbers and moonshine theory with cutting-edge neural network architectures. The result is a unique and powerful tool for tackling complex data patterns and enhancing predictive performance. In this post, I walk through the intricate details of the network's architecture, including the use of custom lambda layers, residual connections, and batch normalization. I also cover the rigorous training process, hyperparameter tuning, and the impressive results achieved through this approach. Whether you're a machine learning enthusiast or a mathematics aficionado, there's something in this post for you. Check out the full article on Medium to explore the fascinating intersection of mathematics and artificial intelligence, and see how the PAdic Moonshine Network can push the boundaries of what's possible in neural network design. Read the full article #MachineLearning #ArtificialIntelligence #NeuralNetworks #Mathematics #DeepLearning #Innovation

Excited to announce that our paper on 'Smart Contract Vulnerabilities Detection using Deep Learning' has been successfully uploaded for review at the 2024 Sixteenth International Conference on Contemporary Computing (IC3)! 🚀📝 Looking forward to contributing to the advancement of research in this field. #Research #DeepLearning #SmartContracts"

“On the Geometry of Deep Learning” by Randall Balestriero , Ahmed Imtiaz Humayun and Richard Baraniuk “In this paper, we overview one promising avenue of progress at the mathematical foundation of deep learning: the connection between deep networks and function approximation by affine splines (continuous piecewise linear functions in multiple dimensions). In particular, we will overview work over the past decade on understanding certain geometrical properties of a deep network's affine spline mapping, in particular how it tessellates its input space. As we will see, the affine spline connection and geometrical viewpoint provide a powerful portal through which to view, analyze, and improve the inner workings of a deep network.” Paper: https://lnkd.in/dBevDtF6 #deeplearning

https://lnkd.in/eqavH-BR A practical and comprehensive introduction of everything related to deep learning in the context of physical #simulations, focuses on physical loss constraints, more tightly coupled learning #algorithms. #deeplearning #machinelearning #learningalgorithms

"Unlocking the Power of Visual Data: Convolutional Neural Networks As we continue to navigate the rapidly evolving landscape of artificial intelligence, one technology stands out for its ability to unlock the power of visual data: Convolutional Neural Networks (CNNs). CNNs are a type of neural network architecture that have revolutionized the field of computer vision, enabling us to extract valuable insights and patterns from images, videos, and other visual data. From image classification and object detection to segmentation and generation, CNNs have numerous applications across industries, including: Healthcare: Diagnosing diseases from medical images Retail: Enhancing customer experience with facial recognition Security: Detecting anomalies in surveillance footage The key to CNNs' success lies in their ability to automatically extract features from raw visual data, leveraging techniques like convolution, pooling, and activation functions. Whether you're a seasoned AI practitioner or just starting your journey, it's essential to understand the fundamentals of CNNs and how they can be applied to drive business value. #ConvolutionalNeuralNetworks #ComputerVision #ArtificialIntelligence #MachineLearning #DataScience"

In this paper we show that L0 sparsification prior to Stein variational inference is a robust and efficient approach for uncertainty quantification in scientific machine learning applications.