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

Qiang Ma's deep learning pipeline for neonatal cortical surface extraction is now published in MedIA with code available on Github. It's so good the dHCP are switching to it for all image processing. It even works on lower resolution historical datasets https://lnkd.in/eATuMCQS https://lnkd.in/e2xUCKXz

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

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

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

I’m thrilled to share that our paper (Adnan Dzelihodzic , Amila Žunić ) "Predictive Modeling of Stock Prices Using Machine Learning: A Comparative Analysis of LSTM, GRU, CNN, and RNN Models," has been published by Springer publication: Advanced Technologies, Systems, and Applications IX🚀📊 I want to express my appreciation to my coauthors👏🏻🙌. I look forward to engaging with the community on these findings and exploring future collaborations!🌟 Check out our paper to learn more: https://lnkd.in/dv9FHftD #MachineLearning #StockPricePrediction #Research #LSTM #GRU #CNN #RNN #DataScience

Thrilled to have presented my research paper "Satellite Image Classification Using Deep Learning" at SCOPES 2024, an IEEE-approved international conference. This work explores CNNs and ResNet architectures for accurate satellite image classification using the EuroSAT dataset. Grateful for the opportunity to share insights and connect with experts in the field. Looking forward to more advancements in Deep Learning! 🚀💡 #IEEE #SCOPES2024 #DeepLearning #MachineLearning

🚀 Check out my latest article, "Modern CNNs: Pre-trained Models (AlexNet)," where I explore AlexNet's groundbreaking impact on computer vision! 🔍 Key insights include: The evolution from LeNet to AlexNet. The crucial role of large datasets and advanced hardware. An in-depth look at AlexNet's architecture and training methods. Dive in to discover how these advancements have transformed deep learning! 📚 👉 https://lnkd.in/epypMQCW #ArtificialIntelligence #MachineLearning #DeepLearning #NeuralNetworks #ComputerVision #AlexNet