Unraveling Insights About Properties and Places with Deep Learning Computer Vision
Explore how deep learning computer vision uncovers insights about beautiful places, property preferences, and societal well-being, with applications in real estate and environmental analysis.
Practical Geometric Deep Learning in Python
Explore Geometric Deep Learning with StellarGraph, an open-source Python library for network-structured data analysis. Learn its applications, design philosophy, and implementation in real-world scenarios.
Scaling Machine Learning with Spark
Learn to build scalable machine learning solutions using Apache Spark, MLlib, MLflow, TensorFlow, and PyTorch. Explore distributed computing concepts, manage ML lifecycles, and combine Spark with deep learning for end-to-end workflows.
Hello Deep Learning
Explore deep learning from scratch, building an OCR solution for handwritten letters. Learn the fundamentals of machine learning and gain insights into the world of AI and neural networks.
AI Without Python - An Intro to Machine Learning for C++ Programmers
Beginner-friendly introduction to AI and deep neural networks for C++ programmers, exploring alternatives to Python for improved performance in machine learning applications.
Accelerate Functional Coverage Closure Using Co-simulation and Machine Learning
Explore co-simulation and machine learning techniques to enhance functional coverage closure in complex designs, showcasing a novel autoencoder-based test selection system for efficient verification.
Neuropots - Realtime Proactive Defense against Bit-Flip Attacks in Neural Networks
Innovative defense strategy against bit-flip attacks in neural networks using "honey neurons" to lure attackers, enabling efficient detection and model recovery in safety-critical AI applications.
Smart Learning to Find Dumb Contracts
Deep learning tool for detecting vulnerabilities in Ethereum smart contracts, offering faster analysis and high accuracy without manual feature engineering or expert rules.
Calpric - Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active Learning
Innovative approach combining crowdsourcing and active learning for efficient, cost-effective labeling of privacy policies, resulting in more accurate and comprehensive deep learning models with balanced datasets.
Towards a Proactive ML Approach for Detecting Backdoor Poison Samples
Proactive approach to detect backdoor poison samples in ML models, using Confusion Training to expose backdoor patterns and improve defense against various attacks.
Machine Duping - Pwning Deep Learning Systems
Explore techniques for exploiting vulnerabilities in deep learning systems, covering neural networks, convolutional networks, and recurrent neural networks, with a focus on practical attack methods.
Deep Learning for Enterprise - Solving Business Problems with AI
Explore deep learning's enterprise applications, AI market trends, and essential tools for building AI solutions. Learn about classification, machine learning, and cybersecurity implications.
Stable 30 Advanced Deception Technology Through Behavioral Biometrics
Explore advanced deception technology using behavioral biometrics and machine learning. Learn about deep neural networks, production ability value, and the promise of deception in cybersecurity.
Cryptanalysis of Round-Reduced LED
Explore cryptanalysis techniques for round-reduced LED cipher, including differential characteristics, complexity analysis, and advanced attack methods.
Non-Profiled Deep Learning-Based Side-Channel Attacks with Sensitivity Analysis
Explore non-profiled deep learning techniques for side-channel attacks, focusing on sensitivity analysis to enhance attack effectiveness and efficiency in cryptographic systems.
Deep Learning to Evaluate Secure RSA Implementations
Explore deep learning techniques for evaluating secure RSA implementations, focusing on cryptographic hardware and embedded systems security.
Real-World Machine Learning with TensorFlow and Cloud ML
Explore real-world applications of TensorFlow and Cloud ML in various industries, including agriculture, automotive, and food manufacturing, showcasing innovative solutions to business challenges.
Remaining Challenges in Deep Learning Based NLP
Explores remaining challenges in Deep Learning-based NLP, including data limitations, performance metrics, robustness issues, representation problems, and lack of interpretability, despite recent advancements and media hype.
Deep Learning and Spark
Learn to classify millions of messages using trained deep learning models (fasttext) with Spark. Includes explanations of key concepts and a practical demo on model training and message classification.
Deep Learning for Recommender Systems
Explore state-of-the-art deep learning techniques for recommender systems, including collaborative filtering, content-based methods, and complex architectures like autoencoders and RNNs for personalized recommendations.
Making Machine Learning Accessible Using Keras
Explore Keras, a user-friendly deep learning API, to make AI accessible for solving business problems. Learn about its features, APIs, model building, and integration with TensorFlow.