Deep Learning and Transfer Learning for NLP
Explore deep learning and transfer learning for NLP with expert Dipanjan Sarkar. Gain insights on challenges, deployment, popular models, tools, and recent advances in multilingual AI and model interpretability.
Long Short-Term Memory (LSTM), Clearly Explained
Clear explanation of Long Short-Term Memory (LSTM) neural networks, their advantages over basic RNNs, and how they handle larger data sequences without gradient problems. Includes step-by-step breakdown of LSTM stages.
Making a 2D GAN 3D Aware
Explore 3D-aware image generation by modifying StyleGANv2 with multiplane alpha maps and pose-conditioned discrimination, achieving high-quality, view-consistent results across diverse datasets.
State of the Art Natural Language Understanding at Scale - David Talby
Explore state-of-the-art NLP techniques using Apache Spark, covering core algorithms, distributed pipelines, and performance optimizations for large-scale language understanding applications.
A Review of Machine Learning Techniques for Anomaly Detection - Dr. David Green
Explore machine learning techniques for anomaly detection, covering traditional methods, deep neural networks, and practical applications in various domains.
Nonlinear Approximation by Deep ReLU Networks - Ron DeVore, Texas A&M
Explores deep ReLU networks for nonlinear approximation, covering architecture, error analysis, and manifold approximation. Bridges mathematics, statistics, and computer science in data science applications.
From Shallow to Deep Learning for Inverse Imaging Problems - Carola-Bibiane Schönlieb, Cambridge
Explore deep learning for inverse imaging problems, covering regularization techniques, neural networks, and optimization strategies for improved image reconstruction and analysis.
MOSIP Research Platform - Integrating Anomaly Detection Prototypes into Centralized ID Systems
Explore novel anomaly detection methods for digital identity systems using autoencoders, with a focus on fraud prevention during registration in MOSIP architecture.
Inference with Neural Nets
Explore neural network inference, covering mathematical foundations, data science concepts, and practical tools like PyTorch. Gain insights into advanced topics and real-world applications.
Graph Convolutional Networks - GCNs
Explore Graph Convolutional Networks: from traditional ConvNets to spectral and spatial approaches. Learn architectures, implementations, and applications of GCNs in this comprehensive lecture.
Affine Spline Insights into Deep Learning - Richard Baraniuk, Rice University
Explore deep learning through affine splines, understanding neural networks as matched filterbanks and partition-based signal distances. Gain insights into data augmentation and future research directions.
Deep Learning Volatility - Blanka Horvath, Kings College London
Explore deep learning for option pricing and calibration, including neural network perspectives, rough volatility, and model recognition in financial mathematics.
Information Manipulation - How the Media Ecosystem Is Being Gamed and Exploited - Craig Silverman
Explore how digital media is manipulated, from Russian trolls to ad fraud, and learn strategies to reclaim the information space from frauds and fakers in this eye-opening talk.
Convolutional Neural Networks Explained Easily
Explore CNNs, their components, and application to audio data. Learn about convolutions, pooling, and architectural decisions for effective deep learning in sound processing.
A Very First Look at H2O Hydrogen Torch
Explore H2O Hydrogen Torch: a no-code deep learning tool for diverse AI tasks. Learn its capabilities, experiment as novice and expert, and understand prediction pipelines and deployment options.
Intermittent Demand Forecasting at Scale Using Meta-Modeling - Deep Auto-Regressive Linear Dynamic Systems
Discover Walmart's innovative solution for predicting demand of slow-moving items using meta-modeling, combining linear dynamic systems and deep auto-regressive networks for accurate forecasts across thousands of SKUs and stores.
Deep Learning Pipelines for High Energy Physics Using Apache Spark and Distributed Keras
Explore CERN's Apache Spark-based data pipeline for deep learning in High Energy Physics. Learn about distributed training of neural network classifiers using BigDL and Analytics Zoo for improved event filtering at LHC experiments.
ACM-IMS Interdisciplinary Summit on the Foundations of Data Science
Explore cutting-edge data science topics with leading experts, including deep learning, reinforcement learning, robustness, fairness, and ethics, through keynote talks and panel discussions.
Deep Learning Day - Advances in Theory, Systems, and Applications
Comprehensive exploration of deep learning featuring expert talks on scaling, debugging, and advanced techniques. Covers theoretical foundations and practical applications across various domains.
KDD 2020: Robust Deep Learning Methods for Anomaly Detection
Explore robust deep learning techniques for anomaly detection, focusing on auto-encoders and their applications in video surveillance and image analysis.
Deeply Learning Derivatives - From Hilbert to Riskfuel
Explore advanced derivatives pricing using deep learning, from Hilbert's 13th problem to modern neural networks for valuing high-dimensional contingent claims in quantitative finance.