Adversarial Examples for Deep Neural Networks
Explore adversarial examples in deep neural networks, covering white/black box attacks, real-world scenarios, and defense strategies like adversarial training. Gain insights into various attack methods and their implications.
Neural Network Architectures for Images
Explore neural network architectures for image processing, including classification, segmentation, and enhancement. Learn about CNNs, ResNets, autoencoders, and their applications in various imaging tasks.
Unlearned Neural Networks as Image Priors for Inverse Problems
Explore unlearned neural networks as image priors for inverse problems in imaging, covering Deep Image Prior, Deep Decoder, and Deep Geometric Prior techniques and their applications.
Theory of GANs for Compressed Sensing
Explore the theory of GAN priors in compressed sensing, covering key concepts, proofs, and optimization problems. Gain insights into visual representation and set restricted eigen value conditions.
Diffusion Models - Paper Explanation - Math Explained
Comprehensive explanation of diffusion models, covering theory, architecture, math derivation, algorithms, improvements, and results. Includes comparisons to other methods and references to state-of-the-art applications.
VQ-GAN - PyTorch Implementation
Implement Vector Quantized Generative Adversarial Networks (VQGAN) using PyTorch, covering encoder, decoder, codebook, discriminator, and transformer stages for high-resolution image synthesis.
Transfer Learning Using Keras
Learn to classify car brands using transfer learning with Keras and ResNet50. Hands-on tutorial covers dataset preparation, model configuration, training, and prediction visualization.
Convolutional Neural Networks - Deep Learning Basics with Python, TensorFlow and Keras
Learn to build and train a convolutional neural network for chess piece image classification using Python, TensorFlow, and Keras. Covers dataset preparation, model creation, and prediction testing.
How to Classify Natural Scenes Using Tensorflow, Python, and CNN
Learn to classify natural scenes using TensorFlow and CNN. Build, train, and test a model for image classification with Python, covering dataset preparation, model creation, and prediction.
Transfer Learn in Weather Images
Learn to classify weather images using transfer learning with Python, TensorFlow, and VGG19. Covers dataset preparation, model implementation, and image prediction.
Deep Learning in Weather Images - Using TensorFlow and CNN
Learn to build a CNN model using TensorFlow and Keras to predict weather scenes from images. Covers dataset preparation, model creation, training, and testing.
Cancer Detection in Brain Using Deep Learning
Learn to detect brain tumors using deep learning, CNN, and TensorFlow. Build, train, and test a model for classifying brain images with hands-on coding and practical implementation.
How to Classify Flowers Using Deep Learning
Learn to build a convolutional neural network for flower image classification using TensorFlow and Keras, from dataset preparation to model testing and prediction.
Train StyleGAN with Your Own Images
Learn to train StyleGAN with your own images using Colab, covering image preparation, training setup, and resuming training for both StyleGAN2 and StyleGAN3 models.
Training StyleGAN2 ADA PyTorch Images with Low GPU Memory - NVIDIA GEFORCE RTX 3060 Ti
Learn to train StyleGAN2 ADA PyTorch on NVIDIA GEFORCE RTX 3060 Ti with limited GPU memory. Optimize settings, increase swap space, and adjust evaluation metrics for efficient 256x256 GAN training.
Training a GAN from Your Own Images - StyleGAN2 ADA
Learn to create custom GANs using your own images, from setup to training. Covers image collection, Docker setup, TFRecord conversion, and understanding GAN outputs for various projects like FishGAN and MinecraftGAN.
Training a GAN on Your Own Images - StyleGAN2 ADA PyTorch
Learn to install and use NVIDIA StyleGAN2 ADA for PyTorch on Windows 10, including setup, training, and image generation with new Ampere GPUs. Step-by-step guide for both beginners and advanced users.
Introduction to Q-Learning for Game Play - Lecture 12.2
Explore Q-Learning for game AI: build policy tables, suggest optimal actions, and apply Python techniques for environment-based decision-making in this introduction.
Cloud-Based Convolution Neural Network Ensembles for Computer Vision Counting
Explore ensemble CNN techniques for computer vision counting, featuring cloud computing evaluation and strategies for achieving low RMSE scores in competitive settings.
Using FFMPEG to Encode-Decode Video for Offline Video-Based Machine Learning
Learn to use FFMPEG for video encoding/decoding in machine learning, enabling frame-by-frame processing and video generation from individual images.
Exploring Style GAN2 Latent Vector - Controlling Facial Properties
Dive into StyleGAN2's latent vectors to control facial features in generated images, including a technique for adding glasses to faces.