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

• Introduction to Anomaly Detection in Python
• Introduction to K-Means Clustering in Python with scikit-learn
• An introduction to Q-Learning: Reinforcement Learning
• How to plan and execute your ML and DL projects
• Becoming One With the Data ⁸
• Training Neural Nets: a Hacker’s Perspective

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)

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).
• 🧨 Diffusers welcomes Stable Diffusion 3.5 Large (Diffusers team).
• Open Preference Dataset for Text-to-Image Generation by the 🤗 Community (multiple authors from Hugging Face).

Apart from these, I contribute to numerous documentation-related efforts, and they can be found by visiting these links: link 1, link 2.

Carted

The first four posts were co-authored with Nilabhra Roy Chowdhury.

• Improving Dataflow Pipelines for Text Data Processing
• Variable-Length Sequences in TensorFlow
  • Part I
  • Part II
  • Part III
• 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

1. #TFCommunitySpotlight awarded.

2. #TFCommunitySpotlight awarded.

3. #TFCommunitySpotlight awarded.

4. #TFCommunitySpotlight awarded.

5. This article got featured in “Python Top 10 Articles for the Past Month (v.Oct 2018)” and secured a rank of 4.

6. This article got featured in “Machine Learning Top 10 Articles for the Past Month (v.Nov 2018)” and secured a rank of 9.

7. This article got featured in “Python Top 10 Articles for the Past Month (v.Dec 2018)” and secured a rank of 10.

8. Featured in Sebastian Ruder’s monthly newsletter.

9. This one ranked eighth at a blogging competition.

10. #TFCommunitySpotlight awarded.

11. In collaboration with the Neural Structured Learning team at Google.

12. #TFCommunitySpotlight awarded.

13. #TFCommunitySpotlight awarded.

14. This one won the ML GDE Dev Challenge.

15. #TFCommunitySpotlight awarded.

16. #TFCommunitySpotlight awarded.