SRGAN Implementation From Scratch
Learn to implement SRGAN from scratch, covering model architecture, VGG loss term, and essential components for super-resolution image enhancement.
Detecting Facial Keypoints with Deep Learning
Learn to implement a top-performing deep learning solution for facial keypoint detection, covering data analysis, model development, and submission strategies for Kaggle competitions.
Detecting Eye Disease Using Deep Learning - Kaggle Top 1% Solution, No Ensemble
Learn to detect eye disease using deep learning, with a top-ranked Kaggle solution. Covers data preprocessing, loss functions, augmentation, and resolution techniques for improved accuracy in medical image analysis.
ProGAN Implementation From Scratch
Learn to implement ProGAN from scratch, covering model architecture, training setup, and evaluation. Gain hands-on experience in advanced generative adversarial network techniques.
ProGAN Paper Walkthrough
Detailed walkthrough of the ProGAN paper, covering progressive growing, MiniBatch Std, layer fading, normalization techniques, and implementation details for advanced GAN enthusiasts.
CycleGAN Implementation From Scratch
Learn to implement CycleGAN from scratch, covering discriminator, generator, dataset preparation, and training process for image-to-image translation tasks.
EfficientNet From Scratch in Pytorch
Learn to implement EfficientNet from scratch using PyTorch, covering key concepts like CNNBlock, SqueezeExcitation, and InvertedResidualBlock with stochastic depth.
PyTorch Image Segmentation Tutorial with U-NET - Everything From Scratch
Learn to implement U-NET for image segmentation from scratch, covering model architecture, dataset preparation, training process, and evaluation techniques in PyTorch.
Albumentations Tutorial for Data Augmentation - PyTorch Focused
Learn data augmentation techniques using Albumentations library for image classification, segmentation, and object detection tasks, with a focus on PyTorch implementation and practical examples.
WGAN Implementation From Scratch With Gradient Penalty
Implement WGAN and WGAN-GP in PyTorch, focusing on improving GAN training stability through loss function modifications. Includes theoretical explanations and practical coding.
DCGAN Implementation From Scratch
Learn to implement DCGAN from scratch, focusing on convolutional neural networks for image generation. Covers discriminator and generator implementation, weight initialization, and training on MNIST and CelebA datasets.
YOLOv1 from Scratch
Comprehensive tutorial on implementing YOLOv1 from scratch, covering architecture, loss function, dataset preparation, training, and evaluation for object detection.
Complete TensorBoard Guide
Comprehensive guide to TensorBoard features including loss plots, image visualization, confusion matrices, hyperparameter tuning, embedding projections, and TensorFlow profiler for model optimization and analysis.
TensorFlow Tutorial - TensorFlow Datasets
Learn to use TensorFlow Datasets for loading, preprocessing, and modeling image and text data. Covers MNIST image classification and IMDB sentiment analysis with practical examples and code demonstrations.
How to Build Custom Datasets for Text in PyTorch
Learn to build custom datasets for text in PyTorch, focusing on image captioning with the Flickr8k dataset. Covers dataset creation, vocabulary setup, batch padding, and data loading for various NLP tasks.
Pytorch Transformers for Machine Translation
Build a Seq2Seq Transformer model in PyTorch for German-to-English translation using the Multi30k dataset. Learn data preprocessing, model architecture, training setup, and evaluation with BLEU score.
Pytorch Transformers from Scratch - Attention Is All You Need
Implement Pytorch Transformers from scratch, exploring the "Attention is all you need" paper. Learn attention mechanisms, encoder-decoder architecture, and practical application.
Pytorch Seq2Seq Tutorial for Machine Translation
Comprehensive tutorial on building a Seq2Seq model for German-English machine translation, covering implementation, training, and evaluation using PyTorch and the Multi30k dataset.
Pytorch TensorBoard Tutorial
Comprehensive guide to TensorBoard in PyTorch: plotting functions, hyperparameter search, image and weight visualization, and embedding projector. Practical examples included.
Train Mask R-CNN for Image Segmentation
Learn to train and implement Mask R-CNN for image segmentation using free online GPU resources. Covers dataset preparation, model training, and practical application for object detection and segmentation.
How to Build a PC for Deep Learning - On a Budget
Learn to build a budget-friendly PC for deep learning, covering essential components like CPU, GPU, and RAM. Gain insights on selecting hardware based on your specific needs and financial constraints.