Hugging Face

🚀 Build a Qwen 2.5 VL API endpoint with Hugging Face spaces and Docker!

Simplifying Alignment: From RLHF to Direct Preference Optimization (DPO)

Timm ❤️ Transformers: Use any timm model with transformers

Controlling Language Model Generation with NVIDIA's LogitsProcessorZoo

Welcome PaliGemma 2 – New vision language models by Google

Faster Text Generation with Self-Speculative Decoding

Hugging Face Welcomes the Qwen2.5-Coder Series

PyTorchModelHubMixin: Bridging the Gap for Custom AI Models on Hugging Face

Hugging Face welcomes the Aya Expanse family of multilingual models

🧨 Diffusers welcomes Stable Diffusion 3.5 Large

Llama can now see and run on your device - welcome Llama 3.2

Speedup Llama 3 70B with Speculative Decoding

Speedup Llama 3 8B with Speculative Decoding

Understanding Vector Quantization in VQ-VAE

Building DoRA Support for Embedding Layers in PEFT

How to communicate in a Pull Request?

The Workflow of peft

Announcing New Hugging Face and Keras NLP Integration

Counting 'n' objects

What is probability?

Conditional Probability

PyImageSearch

Step-by-Step Guide to Open-Source Implementation of Generative Fill: Part 1

ML Days in Tashkent — Day 1: City Tour

ML Days in Tashkent — Day 2: Sprints and Sessions

ML Days in Tashkent — Day 3: Demos and Workshops

AugMix with KerasCV Breakdown (Part 1): Introduction to AugMix

What Is Keras Core?

DETR Breakdown Part 3: Architecture and Details

DETR Breakdown Part 2: Methodologies and Algorithms

DETR Breakdown Part 1: Introduction to DEtection TRansformers

Introduction to Causality in Machine Learning

Learning JAX in 2023: Part 3 — A Step-by-Step Guide to Training Your First Machine Learning Model with JAX

Learning JAX in 2023: Part 2 — JAX’s Power Tools grad, jit, vmap, and pmap

Learning JAX in 2023: Part 1 — The Ultimate Guide to Accelerating Numerical Computation and Machine Learning

Automatic Differentiation Part 2: Implementation Using Micrograd

Automatic Differentiation Part 1: Understanding the Math

A Deep Dive into Transformers with TensorFlow and Keras: Part 1

A Deep Dive into Transformers with TensorFlow and Keras: Part 2

A Deep Dive into Transformers with TensorFlow and Keras: Part 3

Neural Machine Translation with Luong’s Attention Using TensorFlow and Keras

Neural Machine Translation with Bahdanau’s Attention Using TensorFlow and Keras

Neural Machine Translation

Introduction to TFRecords

Long Short-Term Memory Networks

Introduction to RNNs with TensorFlow and Keras

Fast Neural Network Training with Distributed Training and Google TPUs

Computer Graphics and Deep Learning with NeRF using TensorFlow and Keras: Part 1

Computer Graphics and Deep Learning with NeRF using TensorFlow and Keras: Part 2

Computer Graphics and Deep Learning with NeRF using TensorFlow and Keras: Part 3

Keras

When Recurrence meets Transformers^[4]

Focal Modulation: A replacement for Self-Attention

Investigating Vision Transformer representations^[1]

A Vision Transformer without Attention

Augmenting convnets with aggregated attention

Video Vision Transformer

Train a Vision Transformer on small datasets

Learning to tokenize in Vision Transformers

Masked image modeling with Autoencoders^[2]

Neural Style Transfer with AdaINs

Involutional neural networks

3D volumetric rendering with NeRF^[3]

1: This tutorial won the Google Open Soruce Expert Prize.
2: This tutorial won the Google Open Soruce Expert Prize.
3: This tutorial won the TensorFlow community spotlight award.
4: This tutorial won the TensorFlow community spotlight award.

Weights and Biases

Ablations on NMT with attention | Part 4

Effective Approaches to Attention-based Neural Machine Translation | Part 3

Neural Machine Translation by Jointly Learning to Align and Translate | Part 2

An Introduction to Attention | Part 1

Enriching Word Vectors with Sub-word Information

Sequence to Sequence with `tf.keras`

Keras Tuner with W&B

Show and Tell

GloVe

Word2Vec

Survival of the Fittest CNN Model

Under the hood of RNNs

Under the hood of LSTMs

Part 1: Deep Representations, a way towards neural style transfer

Part 2: Deep Representations, a way towards neural style transfer

Towards Representation Learning for an Image Retrieval Task

Simple Ways to Tackle Class Imbalance

Kaggle

SimCLR

LanceDB

Training a Variational AutoEncoder from scratch with Lance file format

Vector Arithmetic with LanceDB: An intro to Vector Embeddings

Personal Space

Easy switching to backends in Keras Core with conda

TensorFlow and GPU

Character level language model RNN

Similarity of neuron activations between similar classes

C++ Progress

Creating a static website in minutes

Back Propagation in Batch Normalization

Medium

[ML Story] My Keras Chronicles

Hexato

Tad Bit of Java

MISC

Choosing Between SigLIP and CLIP for Language Image Pretraining

Aritra Roy Gosthipaty - Deep Learning Associate at PyImageSearch