Writing
Thoughts on machine learning, LLMs, and building things that work.
Weights & Biases Reports
Technical articles and tutorials I’ve written on the W&B platform—covering deep learning, computer vision, LLMs, and practical ML engineering.
LLMs & RLHF
- Understanding Reinforcement Learning from Human Feedback (RLHF): Part 1
- An Introduction to Training LLMs Using Reinforcement Learning From Human Feedback (RLHF)
- How to Evaluate, Compare, and Optimize LLM Systems
- Building Advanced Query Engine and Evaluation with LlamaIndex and W&B
- Implementing RLHF: Learning to Summarize with trlX
- RLHF: Hyperparameter Optimization for trlX
Computer Vision & GANs
- Towards Deep Generative Modeling With Weight & Biases
- How to Evaluate GANs using Frechet Inception Distance (FID)
- Train Generative Adversarial Networks (GANs) With Limited Data
- In-Domain GAN Inversion for Real Image Editing
- An Introduction to Adversarial Latent Autoencoders
- Interpretability in Deep Learning With Weights & Biases: CAM and Grad-CAM
- An Overview of Instance Aware Image Colorization
- Image Segmentation Using Keras and Weights & Biases
- Object Localization With Keras and Weights & Biases
- Image Classification Using Vision Transformer and KerasCV
PyTorch & PyTorch Lightning
- How To Use GPU with PyTorch
- How to save and load models in PyTorch
- How To Check If PyTorch Is Using The GPU
- Image Classification Using PyTorch Lightning and Weights & Biases
- Transfer Learning Using PyTorch Lightning
- Multi-GPU Training Using PyTorch Lightning
- An Introduction To The PyTorch View Function
- PyTorch Dropout for regularization - tutorial
- Implementing and Tracking the Performance of a CNN in PyTorch
Keras & TensorFlow
- Keras Dense Layer: How to Use It Correctly
- Input Keras Layer Explanation With Code Samples
- Batch Normalization in Keras - An Example
- How to Use Cosine Decay Learning Rate Scheduler in Keras
- Using GPUs With Keras: A Tutorial With Code
- Integrating Keras with Weights & Biases
- How To Install TensorFlow With GPU Support on Windows
- How to Prevent TensorFlow From Fully Allocating GPU Memory
- LSTM RNN in Keras: Examples of One-to-Many, Many-to-One & Many-to-Many
- A Guide to Multi-Label Classification on Keras
HuggingFace & Transformers
- How To Fine-Tune Hugging Face Transformers on a Custom Dataset
- Examples of Early Stopping in HuggingFace Transformers
- SimpleTransformers: Transformers Made Easy
- Taming Transformers for High-Resolution Image Synthesis
Deep Learning Fundamentals
- What’s the Optimal Batch Size to Train a Neural Network?
- ReLU vs. Sigmoid Function in Deep Neural Networks
- How to Handle Images of Different Sizes in a Convolutional Neural Network
- Intuitive understanding of 1D, 2D, and 3D convolutions in convolutional neural networks
- What’s the Difference Between Strided Convolution and Pooling?
- Comparing Sigmoid-MSE With Softmax Cross-Entropy for Image Classification
- How One-Hot Encoding Improves Machine Learning Performance
- Understanding the Effectivity of Ensembles in Deep Learning
Data Augmentation & Training Techniques
- Modern Data Augmentation Techniques for Computer Vision
- Simple Ways to Tackle Class Imbalance
- Exploring Adaptive Gradient Clipping and NFNets
- Metric Learning for Image Search With Weights & Biases
- How to avoid checkerboard pattern in your generated images?
3D Vision & Novel View Synthesis
- 3D Image Inpainting With Weights & Biases
- Paper Summary: One Shot 3D Photography
- X-Fields: Implicit Neural View-, Light- and Time-Image Interpolation
- Overview: Neural Scene Flow Fields (NSFF) for Space-Time View Synthesis of Dynamic Scenes
- Dynamic Sky Replacement: The Sky Is Within Our Grasp!
Video & Animation
- One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing
- An Introduction to Egocentric Videoconferencing
- An Intro to Retiming Instances in a Video
- Automating Animations with the Help of Robust In-Betweening
Other
- Self-Supervised Learning with SwAV
- How To Use Weights & Biases With MMDetection
- Generating Digital Painting Lighting Effects via RGB-space Geometry
- An Overview of DeepFaceDrawing
- Rewriting a Deep Generative Model: An Overview
- From EMNIST to Wikisplit: Sentence Composition Using W&B
- Modeling Drivable Areas for Autonomous Vehicles with Real and Synthetic Data
Medium
Kaggle Notebooks
- [Train] COVID-19 Detection using YOLOv5 (800 upvotes)
- Experiment Tracking with Weights and Biases (420 upvotes)
- [Train] Brain Tumor as Video Classification + W&B (175 upvotes)
- [Train] NFL Extra Images YOLOv5 with W&B (174 upvotes)
- Brain Tumor EDA and Interactive Viz with W&B (158 upvotes)
- Train YOLOv5 + Cross Validation + Ensemble + W&B (155 upvotes)
- TPS June [Weights and Biases] (148 upvotes)
- Submission Covid19 (132 upvotes)
- [G2Net] Understand the Data (129 upvotes)
- SD 2.0: Download Dataset from W&B Artifacts (111 upvotes)
- Visualize Bounding Boxes Interactively (111 upvotes)
- [V2] Self-Supervised Pretraining with SwAV (103 upvotes)
- EfficientNet+Mixup+K-Fold using TF and wandb (63 upvotes)
- HPA: Segmentation Mask Visualization with W&B (62 upvotes)
- Comparative Study: SETI Experiments (61 upvotes)
- Create Constant Q Transformed Dataset (51 upvotes)
- HPA: Multi-Label Classification with TF and W&B (50 upvotes)
- Easy PCA TSNE and UMAP (49 upvotes)
- [Train] Study Level Classifier + W&B (49 upvotes)
- [Train] COTS Detection using YOLOv5 and W&B (49 upvotes)
- Quick Data EDA+ Segmentation Viz using W&B (49 upvotes)
- [Train][PyTorch] FineTune ConvNeXt (44 upvotes)
- [Train] TensorFlow+EfficientNet+Kfold+W&B (42 upvotes)
- Use PyTorch Lightning With Weights and Biases (41 upvotes)
- Visualize the COTS interactively with W&B (36 upvotes)
- [MLOps Tutorial] XGBoost with W&B (36 upvotes)
- TensorFlow Inference [No-TTA] (28 upvotes)
- BirdCLEF: Quick EDA with W&B (26 upvotes)
- APTOS Blindness Detection - EDA and Keras ResNet50 (25 upvotes)
- Interactive EDA using W&B Tables (23 upvotes)
- Cassava 2019+2020 Dataset Prep+Versioning w/ W&B (23 upvotes)
- GradCAM: Implementation+Visualization in TF & W&B (22 upvotes)
- XGBoost Hyperparameter Tuning with W&B Sweeps (22 upvotes)
- Train EfficientNetv2 with TF and W&B (20 upvotes)
- [HappyWhale] EDA - IDs to Images using W&B Tables (19 upvotes)
- Ubiquant EDA (18 upvotes)
- Transformer Baseline with TF/Keras and W&B (18 upvotes)
- Get the most out of LightGBM using W&B (17 upvotes)
- Sartorius: Instance Mask Viz with W&B (16 upvotes)
- [TensorFlow] Working with TIFF (15 upvotes)
- Rainforest Create Image Dataset with W&B (14 upvotes)
- Resampled Voxel Dataset - with KaggleRecipes (W&B) (14 upvotes)
- AutoEncoders implementation (13 upvotes)
- G2Net: Quick EDA and CQT + W&B (13 upvotes)
- Style Transfer using CycleGAN with TF and W&B (13 upvotes)
- BMS: EDA and Dataset Visualization w/ W&B (12 upvotes)
- Single Label Cell Label Dataset Creation w/ W&B (12 upvotes)
- Comparing Different Modeling Methods w/ W&B (11 upvotes)
- Better Data Understanding with W&B Tables (10 upvotes)
- HPA: Inference (10 upvotes)
- Investigate Imgur5k dataset (10 upvotes)
- Images to TFRecord (10 upvotes)
- Experiment Tracking w/ Weights and Biases [Russia] (10 upvotes)
- Rainforest Train Baseline Model with W&B (9 upvotes)
- All in one MLB Quick EDA with W&B Tables (8 upvotes)
- RGB 512x512 Dataset Creation+Versioning with W&B (7 upvotes)
- Save the Dataset as W&B Artifacts (7 upvotes)
- Visualize Dataset Interactively using W&B Tables (6 upvotes)
- Dataset Versioning for Single Label Dataset (6 upvotes)
- Testing W&B Offline Flow (6 upvotes)
- Batch Normalization ft Batch Size with W&B (6 upvotes)
- Jigsaw BERT Model (5 upvotes)
- Rainforest: Which model is better? (5 upvotes)
- Adaptive Gradient Clipping: Ablation Study w/ W&B (5 upvotes)
- Using W&B DSViz for Cell Level Dataset Creation (5 upvotes)
- Kaggle Survey 2020: W&B vs All (5 upvotes)
- Kaggle Survey 2021: W&B vs All (5 upvotes)
- Cell Level Extra Data Download (5 upvotes)
- Test wandbcode (4 upvotes)
- Multiprocessing 5 Second Audio Clips (4 upvotes)
- HuBMAP: TF with TPU EfficientUNetB7 512x512 [Train] (4 upvotes)
- CommonLit Readability Prize: EDA + Baseline (3 upvotes)
- Visualize the Dataset using W&B Tables (3 upvotes)