Authoring
Co-authored a book Hands-On Python Deep Learning for the Web with Anubhav Singh.
Authored the following liveProjects with Manning:
- Use Machine Learning to Detect Phishing Websites
- Summarize News Articles with NLP and TensorFlow (with Souradip Chakraborty)
Authored two DataCamp Projects (Predicting Credit Card Approvals and Analyze International Debt Statistics) and a DataCamp Practice Pool on Advanced Deep Learning with Keras.
Below are the blogs, articles, and tutorials I have written on Data Science, Machine Learning, and more. I am fortunate enough to collaborate with amazing folks from all around the globe. I am grateful to the GDE Program (ML Developer Programs team) that provides me with Google Cloud Platform credits which I use to run various experiments for my own curiosity and for blog posts.
Keras
- MixUp augmentation for image classification
- RandAugment for Image Classification for Improved Robustness
- Self-supervised contrastive learning with SimSiam
- Consistency Training with Supervision
- Learning to Resize in Computer Vision
- Keypoint Detection with Transfer Learning
- Video Classification with a CNN-RNN Architecture
- Video Classification with Transformers
- Semi-supervision and domain adaptation with AdaMatch
- Compact Convolutional Transformers
- Conditional GAN
- Vector-Quantized Variational Autoencoders
- Knowledge distillation recipes 1
- Multimodal entailment
- Handwriting recognition (joint work with Aakash Kumar Nain)
- Near-duplicate image search
- Large-scale multi-label text classification (joint work with Soumik Rakshit)
- FixRes: Fixing train-test resolution discrepancy
- Image classification with ConvMixer
- MobileViT: A mobile-friendly Transformer-based model for image classification
- Point cloud segmentation with PointNet (joint work with Soumik Rakshit) 2
- Masked image modeling with Autoencoders (joint work with Aritra Roy Gosthipaty)
- Learning to tokenize in Vision Transformers (joint work with Aritra Roy Gosthipaty)
- GauGAN for conditional image generation (joint work with Soumik Rakshit)
- Distilling Vision Transformers 3
- Investigating Vision Transformer representations (joint work with Aritra Roy Gosthipaty) 4
- Class Attention Image Transformers with LayerScale
- Fine-tuning Stable Diffusion (joint work with Chansung Park)
- Semantic segmentation with SegFormer and Hugging Face Transformers
- DreamBooth (joint work with Chansung Park)
- Training a language model from scratch with 🤗 Transformers and TPUs (joint work with Matthew Carrigan)
- Segment Anything Model with 🤗 Transformers (joint work with Merve Noyan)
Datacamp
- KMeans clustering with
scikit-learn
- Demystifying Crucial Statistics in Python 5
- Turning Machine Learning Models into APIs in Python 6
- Essentials of Linear Regression in Python 7
- Simplifying Sentiment Analysis in Python
- Introduction to Indexing in SQL
- Understanding Recursive Functions in Python
- Beginner’s Guide to Google’s Vision API in Pytho
- Beginner’s Guide to PostgreSQL
- Managing Databases in PostgreSQL
- Working with Spreadsheets in SQL
- Installing PostgreSQL on Windows and Mac OS X
- Using Order By Keyword in SQL
- Introduction to Alter Table Statement in SQL
- SQLite in Python
- Introduction to Where Clause in SQL
- Introduction to SQL Joins
- 10 command-line utilities in PostgreSQL
- CASE Statements in PostgreSQL
- Cleaning Data in SQL
- Materialized Views in PostgreSQL
- Argument Parsing in Python
- Ten Important Updates from TensorFlow 2.0
- Implementing Neural Style Transfer using TensorFlow 2.0
- TensorFlow 2.0 Case Study
FloydHub
Weights and Biases
- Running Hyperparameter Sweeps to Pick the Best Model
- arXiv Search: Generating Tags from Paper Titles
- How to Use GCP with Weights & Biases
- Mixed precision training with
tf.keras
- Customizing Training Loops in TensorFlow 2.0
- Bayesian Hyperparameter Optimization - A Primer
- Visualize models in TensorBoard with Weights and Biases
- The effects of weight initialization on neural nets
- Introduction to image inpainting with deep learning (joint work with Ayush Thakur)
- Reproducible Models with W&B
- EvoNorm layers in TensorFlow 2
- A Tale of Model Quantization in TF Lite
- Towards self-supervised image understanding with SimCLR
- The Power of Random Features of a CNN
- Plotting top loss images while training models (joint work with Tulasi)
- Improving Image Classifiers with Supervised Contrastive Learning (joint work with Sweta Shaw)
- Model Pruning in Deep Learning
- Understanding the Effectivity of Ensembles in Deep Learning (joint work with Ayush Thakur)
- An Introduction to Adversarial Examples in Deep Learning 9
- Unsupervised Visual Representation Learning with SwAV (joint work with Ayush Thakur)
- Distilling Knowledge in Neural Networks
- Keras XLA Benchmarks (in collaboration with Soumik Rakshit and Ayush Thakur)
Google Cloud Platform
- Streamline your ML training workflow with Vertex AI (joint work with Karl Weinmeister of Google)
- Image search with natural language queries (joint work with Chansung Park)
- Dual deployments on Vertex AI (joint work with Chansung Park) 10
- Model training as a CI/CD system - Part I, Part II (joint work with Chansung Park)
TensorFlow
- How to Create a Cartoonizer with TensorFlow Lite (joint work with Margaret Maynard-Reid)
- TensorFlow Addons Optimizers: CyclicalLearningRate
- Graph regularization for image classification using synthesized graphs 11
- Continuous Adaptation for Machine Learning System to Data Changes (joint work with Chansung Park) 12
- Load-testing TensorFlow Serving’s REST Interface (joint work with Chansung Park)
- Automated Deployment of TensorFlow Models with TensorFlow Serving and GitHub Actions (joint work with Chansung Park)
- End-to-End Pipeline for Segmentation with TFX, Google Cloud, and Hugging Face (joint work with Chansung Park) 13
- Serving With TF and GKE: Stable Diffusion (joint work with Chansung Park)
Hugging Face 🤗
Series on the deployment of TF vision models in 🤗
- Deploying TensorFlow Vision Models in Hugging Face with TF Serving
- Deploying 🤗 ViT on Kubernetes with TF Serving (with Chansung Park)
- Deploying 🤗 ViT on Vertex AI (with Chansung Park)
Notebooks and Scripts
- Fine-tuning VideoMAE for Video Classification
- Fine-tuning TF SegFormer for Semantic Segmentation
- Fine-tuning Stable Diffusion on Text2Image with LoRA
- Fine-tuning a ViT for Image Classification with LoRA
- Fine-tuning a SegFormer for Semantic Segmentation with LoRA
- InstructPix2Pix training
- Custom Diffusion (in collaboration with Nupur Kumari)
- Training a language model with 🤗 Transformers using TensorFlow and TPUs - code, blog (with Matthew Carrigan)
- Fine-tuning Stable Diffuson XL with DreamBooth and LoRA - code, guide.
- Stable Diffusion XL + DreamBooth + LoRA
- Stable Diffusion XL + ControlNet
- LCM LoRA SDXL
- Faster text-to-image diffusion models in pure PyTorch (code).
Guides
- Semantic segmentation dataset preparation guide
- Depth estimation dataset preparation guide
- Video classification task guide
- Image captioning task guide
- XLA Integration for TensorFlow Models (with Joao Gante)
- LoRA support in Diffusers
- Using KerasCV Stable Diffusion Checkpoints in Diffusers
- Zero-shot Image-to-Image Translation
- Evaluating Diffusion Models
- Token Merging
- Inference with PEFT (with Sourab Mangrulkar and Younes Belkada)
Blogs
- Image Similarity with Hugging Face Datasets and Transformers
- The State of Computer Vision at Hugging Face 🤗
- A Dive into Vision-Language Models (with Alara Dirik)
- 🤗 PEFT: Parameter-Efficient Fine-Tuning of Billion-Scale Models on Low-Resource Hardware (with Sourab Mangrulkar)
- ControlNet in 🧨 Diffusers (with YiYiXu and Patrick von Platen)
- Instruction-tuning Stable Diffusion with InstructPix2Pix
- Optimizing Stable Diffusion for Intel CPUs with NNCF and 🤗 Optimum (in collaboration with the Intel team)
- Controlling Stable Diffusion with JAX, diffusers, and Cloud TPUs (with Merve Noyan) (Tweet from Google OSS)
- Accelerated Diffusers with PyTorch 2.0 (with Pedro Cuenca, Patrick von Platen, and Suraj Patil)
- Happy 1st anniversary 🤗 Diffusers! (with Steven Liu and Pedro Cuenca)
- Efficient Controllable Generation for SDXL with T2I-Adapters (with Chong Mou, Suraj Patil, Xintao Wang, and Toshihiro Hayashi).
- Introducing Würstchen: Fast Diffusion for Image Generation (with Dominic Rampas, Pablo Pernías, Kashif Rasul, and Pedro Cuenca)
- Finetune Stable Diffusion Models with DDPO via TRL (with Luke Meyers, Kashif Rasul, and Leandro von Werra)
- Exploring simple optimizations for SDXL (with Steven Liu)
- Personal Copilot: Train Your Own Coding Assistant (with Sourab Mangrulkar)
- SDXL in 4 steps with Latent Consistency LoRAs (multiple authors; please refer to the blog post to know more)
- Accelerating Generative AI Part III: Diffusion, Fast (with Patrick von Platen). Tweet from PyTorch.
- Welcome aMUSEd: Efficient Text-to-Image Generation (multiple authors; please refer to the blog post to know more).
- 🤗 PEFT welcomes new merging methods (with Sourab Mangrulkar).
- 🧨 Diffusers welcomes Stable Diffusion 3 (Diffusers team).
- Memory-efficient Diffusion Transformers with Quanto and Diffusers (with David Corvoysier).
Carted
The first four posts were co-authored with Nilabhra Roy Chowdhury.
- Improving Dataflow Pipelines for Text Data Processing
- Variable-Length Sequences in TensorFlow
- Building an Efficient Machine Learning API
- Better Hardware Provisioning for ML Experiments
Others
- Lessons learned from a Deep Learning Hackathon
- “Reparameterization” trick in Variational Autoencoders
- Your First Machine Learning Project: Q and A with Sayak Paul, Google Developer Expert (GDE) in Machine Learning (Ep. 4)
- AMA with Sayak Paul - Hacktoberfest’19
- Predicting the publisher’s name from an article: A case study 14
- GDE Journey — Sayak Paul
- Multi-part tutorial series on Selfie2Anime with TFLite (joint work with Margaret Maynard-Reid) - Part I, Part II, Part III
- Multi-part tutorial series on Create Artistic Effect by Stylizing Image Background (joint work with Margaret Maynard-Reid and George Soloupis) - Part I, Part II, Part III 15
- Load-testing TensorFlow Serving and FastAPI on GKE (with Chansung Park) 16
- [ML Story] DreamBoothing Your Way into Greatness
Footnotes
This article got featured in “Python Top 10 Articles for the Past Month (v.Oct 2018)” and secured a rank of 4.↩︎
This article got featured in “Machine Learning Top 10 Articles for the Past Month (v.Nov 2018)” and secured a rank of 9.↩︎
This article got featured in “Python Top 10 Articles for the Past Month (v.Dec 2018)” and secured a rank of 10.↩︎
Featured in Sebastian Ruder’s monthly newsletter.↩︎
This one ranked eighth at a blogging competition.↩︎
In collaboration with the Neural Structured Learning team at Google.↩︎
This one won the ML GDE Dev Challenge.↩︎