Generating Faces with a Generative Adversarial Networks - GAN in Keras-Tensorflow 2.0
Learn to implement a Generative Adversarial Network (GAN) from scratch using TensorFlow and Keras to generate realistic human faces, with step-by-step guidance and practical code examples.
Keras Convolutional Neural Networks for MNIST and Fashion MNIST
Explore convolutional neural networks for image processing, focusing on MNIST and Fashion MNIST datasets. Learn about CNN architecture, training, and evaluation for advanced deep learning applications.
The Deep Learning Revolution
Explore the evolution of deep learning, from its origins to modern applications, covering key milestones, breakthroughs, and future prospects in AI and neuroscience.
Deep Learning Tutorial
Explore deep learning fundamentals, from supervised learning to convolutional networks, with practical examples and historical context. Gain insights into advanced concepts and applications.
Deep Learning - Joan Bruna NYU
Explore deep learning concepts, including lazy regime, relative scale, and practical applications in this comprehensive lecture by Joan Bruna from NYU.
Deep Learning II - Joan Bruna NYU
Explore advanced deep learning concepts including symmetry, transformations, and geometric stability in neural networks with NYU professor Joan Bruna.
Deep Learning I - Joan Bruna NYU
Explore supervised learning, empirical risk, and fundamental theorems in deep learning with NYU professor Joan Bruna's comprehensive lecture on neural network foundations.
Deja Vu - The Story of Vision & AI
Explore the evolution of computer vision and AI with Ali Farhadi, covering deep neural networks, object detection, visual reasoning, and future predictions in this insightful seminar.
Quantum Computing - Guest Lecture
Explore quantum machine learning with Dr. Maria Schuld, covering linear models, deep learning, quantum software, and the intersection of quantum computing and AI.
MIT Introduction to Deep Learning
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques.
TinyML - Using Machine Learning on Microcontrollers to Recognize Speech
Learn to implement speech recognition on microcontrollers using TinyML. Train a neural network, convert it to TensorFlow Lite, and deploy it on an ARM board for real-time wake word detection.
Building a Wireless Community Sensor Network with LoRa
Explore LoRa technology for wireless community sensor networks, focusing on agricultural applications in Temecula Wine Country and the potential for widespread adoption.
Analysis of Large-Scale Visual Recognition - Bay Area Vision Meeting
Explore large-scale visual recognition challenges, focusing on IMAGENET competitions, object classification, localization techniques, and advancements in computer vision for diverse object classes.
Deep Learning for Vision: Tricks of the Trade
Explore deep learning's evolution in computer vision, from unsupervised algorithms to modern object recognition systems. Learn practical tips and future challenges in this field.
In-memory Computing and Dynamic Vision Sensors for Internet of Video Things
Exploring neuromorphic technologies, dynamic vision sensors, and in-memory computing for energy-efficient IoT video applications, with focus on innovative solutions for intelligent transportation and unattended ground sensors.
Continual On-Device Learning on Multi-Core RISC-V Microcontrollers
Explore continual on-device learning for multi-core RISC-V microcontrollers, focusing on system architecture, memory-energy-accuracy trade-offs, and efficient implementation of backpropagation techniques using latent replays.
Mastering the Three Pillars of AI Acceleration: Algorithms, Hardware, and Software
Explore IBM Research's holistic approach to AI acceleration, covering low-precision DNN models, scalable hardware techniques, and efficient software methodologies for optimal performance.
Programmable In-Memory Computing Accelerator with 100 SRAM IMC Macros
Explore programmable in-memory computing accelerators for efficient deep neural network processing, featuring 100 SRAM IMC macros and innovative architecture design for improved energy efficiency.
Building a Winning Deep Learning Recommender System - Grandmaster Series Episode 5
Learn how Kaggle Grandmasters built a winning Deep Learning Recommender System for Booking.com, using matrix factorization, ensemble models, and data augmentation techniques. Gain insights into GPU-accelerated recommender systems.
Accelerating Vision AI Applications Using NVIDIA Transfer Learning Toolkit and Pre-Trained Models
Simplify vision AI model creation using NVIDIA's Transfer Learning Toolkit and pre-trained models. Accelerate development with zero-coding tools, fine-tuning options, and optimization techniques for high-performance inference across various industries.
Efficient Data Loading Using DALI for Deep Learning Applications
Accelerate deep learning applications with efficient data loading pipelines using NVIDIA's DALI library. Explore augmentation, documentation, and performance optimization techniques.