Proposed Tutorials :)

[diksha-shrivastava13]

Please create a new issue with each of the subtasks and comment "- ] #issue_number" to keep the task list updated. Let's edit it slowly and remove all the Keras and tf mentions lmao :)

Computer Vision

Image classification

• [ ] ★ Image classification from scratch
• [ ] ★ Simple MNIST convnet
• [ ] ★ Image classification via fine-tuning with EfficientNet
• [ ] Image classification with Vision Transformer
• [ ] Image Classification using BigTransfer (BiT)
• [ ] Classification using Attention-based Deep Multiple Instance Learning
• [ ] Image classification with modern MLP models
• [ ] A mobile-friendly Transformer-based model for image classification
• [ ] Pneumonia Classification on TPU
• [ ] Compact Convolutional Transformers
• [ ] Image classification with ConvMixer
• [ ] Image classification with EANet (External Attention Transformer)
• [ ] Involutional neural networks
• [ ] Image classification with Perceiver
• [ ] Few-Shot learning with Reptile
• [ ] Semi-supervised image classification using contrastive pretraining with SimCLR]
• [ ] Image classification with Swin Transformers
• [ ] Train a Vision Transformer on small datasets
• [ ] A Vision Transformer without Attention

Image segmentation

• [ ] ★ Image segmentation with a U-Net-like architecture
• [ ] Multiclass semantic segmentation using DeepLabV3+
• [ ] Highly accurate boundaries segmentation using BASNet

Object detection

• [ ] Object Detection with RetinaNet
• [ ] Keypoint Detection with Transfer Learning
• [ ] Object detection with Vision Transformers

3D

• [ ] 3D image classification from CT scans
• [ ] Monocular depth estimation
• [ ] 3D volumetric rendering with NeRF
• [ ] Point cloud classification

OCR

• [ ] ★ OCR model for reading Captchas
• [ ] Handwriting recognition

Image enhancement

• [ ] Convolutional autoencoder for image denoising
• [ ] Low-light image enhancement using MIRNet
• [ ] Image Super-Resolution using an Efficient Sub-Pixel CNN
• [ ] Enhanced Deep Residual Networks for single-image super-resolution
• [ ] Zero-DCE for low-light image enhancement

Data augmentation

• [ ] CutMix data augmentation for image classification
• [ ] MixUp augmentation for image classification
• [ ] RandAugment for Image Classification for Improved Robustness

Image & Text

• [ ] Image captioning
• [ ] Natural language image search with a Dual Encoder

Vision models interpretability

• [ ] Visualizing what convnets learn
• [ ] Model interpretability with Integrated Gradients
• [ ] Investigating Vision Transformer representations
• [ ] Grad-CAM class activation visualization

Image similarity search

• [ ] Near-duplicate image search
• [ ] Semantic Image Clustering
• [ ] Image similarity estimation using a Siamese Network with a contrastive loss
• [ ] Image similarity estimation using a Siamese Network with a triplet loss
• [ ] Metric learning for image similarity search
• [ ] Metric learning for image similarity search using TensorFlow Similarity

Video

• [ ] Video Classification with a CNN-RNN Architecture
• [ ] Next-Frame Video Prediction with Convolutional LSTMs
• [ ] Video Classification with Transformers
• [ ] Video Vision Transformer

Other

• [ ] Semi-supervision and domain adaptation with AdaMatch
• [ ] Barlow Twins for Contrastive SSL
• [ ] Class Attention Image Transformers with LayerScale
• [ ] Consistency training with supervision
• [ ] Distilling Vision Transformers
• [ ] FixRes: Fixing train-test resolution discrepancy
• [ ] Focal Modulation: A replacement for Self-Attention
• [ ] Using the Forward-Forward Algorithm for Image Classification
• [ ] Image Segmentation using Composable Fully-Convolutional Networks
• [ ] Gradient Centralization for Better Training Performance
• [ ] Knowledge Distillation
• [ ] Learning to Resize in Computer Vision
• [ ] Masked image modeling with Autoencoders
• [ ] Self-supervised contrastive learning with NNCLR
• [ ] Augmenting convnets with aggregated attention
• [ ] Point cloud segmentation with PointNet
• [ ] Semantic segmentation with SegFormer and Hugging Face Transformers
• [ ] Self-supervised contrastive learning with SimSiam
• [ ] Supervised Contrastive Learning
• [ ] When Recurrence meets Transformers
• [ ] Learning to tokenize in Vision Transformers
• [ ] Efficient Object Detection with YOLOV8 and KerasCV

Natural Language Processing

Text classification

• [ ] ★ Text classification from scratch
• [ ] Review Classification using Active Learning
• [ ] Text Classification using FNet
• [ ] Large-scale multi-label text classification
• [ ] Text classification with Transformer
• [ ] Text classification with Switch Transformer
• [ ] Text classification using Decision Forests and pretrained embeddings
• [ ] Using pre-trained word embeddings
• [ ] Bidirectional LSTM on IMDB

Machine translation

• [ ] ★ English-to-Spanish translation with KerasNLP
• [ ] English-to-Spanish translation with a sequence-to-sequence Transformer
• [ ] Character-level recurrent sequence-to-sequence model

Entailment prediction

• [ ] Multimodal entailment

Named entity recognition

• [ ] Named Entity Recognition using Transformers

Sequence-to-sequence

• [ ] Text Extraction with BERT
• [ ] Sequence to sequence learning for performing number addition

Text similarity search

• [ ] Semantic Similarity with BERT

Language modeling

• [ ] End-to-end Masked Language Modeling with BERT
• [ ] Pretraining BERT with Hugging Face Transformers

Other

• [ ] Data Parallel Training with KerasNLP
• [ ] Training a language model from scratch with 🤗 Transformers and TPUs
• [ ] Parameter-efficient fine-tuning of GPT-2 with LoRA
• [ ] Question Answering with Hugging Face Transformers
• [ ] Sentence embeddings using Siamese RoBERTa-networks
• [ ] Abstractive Summarization with Hugging Face Transformers

Structured Data

Structured data classification

• [ ] ★Structured data classification with FeatureSpace
• [ ] ★ Imbalanced classification: credit card fraud detection
• [ ] Structured data classification from scratch
• [ ] Structured data learning with Wide, Deep, and Cross networks
• [ ] Classification with Gated Residual and Variable Selection Networks
• [ ] Classification with TensorFlow Decision Forests
• [ ] Classification with Neural Decision Forests
• [ ] Structured data learning with TabTransformer

Recommendation

• [ ] Collaborative Filtering for Movie Recommendations
• [ ] A Transformer-based recommendation system

Timeseries

Timeseries classification

• [ ] ★ Timeseries classification from scratch
• [ ] Timeseries classification with a Transformer model
• [ ] Electroencephalogram Signal Classification for action identification

Anomaly detection

• [ ] Timeseries anomaly detection using an Autoencoder]

Timeseries forecasting

• [ ] Traffic forecasting using graph neural networks and LSTM
• [ ] Timeseries forecasting for weather prediction

Generative Deep Learning

Image generation

• [ ] ★ Denoising Diffusion Implicit Models
• [ ] ★ A walk through latent space with Stable Diffusion
• [ ] DreamBooth
• [ ] Denoising Diffusion Probabilistic Models
• [ ] Teach StableDiffusion new concepts via Textual Inversion
• [ ] Fine-tuning Stable Diffusion
• [ ] Variational AutoEncoder
• [ ] GAN overriding Model.train_step
• [ ] WGAN-GP overriding Model.train_step
• [ ] Conditional GAN
• [ ] CycleGAN
• [ ] Data-efficient GANs with Adaptive Discriminator Augmentation
• [ ] Deep Dream
• [ ] GauGAN for conditional image generation
• [ ] PixelCNN
• [ ] Face image generation with StyleGAN
• [ ] Vector-Quantized Variational Autoencoders

Style transfer

• [ ] Neural style transfer
• [ ] Neural Style Transfer with AdaIN

Text generation

• [ ] ★ GPT2 Text Generation with KerasNLP
• [ ] GPT text generation from scratch with KerasNLP
• [ ] Text generation with a miniature GPT
• [ ] Character-level text generation with LSTM
• [ ] Text Generation using FNet

Graph generation

• [ ] Drug Molecule Generation with VAE
• [ ] WGAN-GP with R-GCN for the generation of small molecular graphs

Other

• [ ] Density estimation using Real NVP

Audio Data

• [ ] Automatic Speech Recognition using CTC
• [ ] MelGAN-based spectrogram inversion using feature matching
• [ ] Speaker Recognition
• [ ] Automatic Speech Recognition with Transformer
• [ ] English speaker accent recognition using Transfer Learning
• [ ] Audio Classification with Hugging Face Transformers

Reinforcement Learning

• [ ] Actor Critic Method
• [ ] Deep Deterministic Policy Gradient (DDPG)
• [ ] Deep Q-Learning for Atari Breakout
• [ ] Proximal Policy Optimization

Graph Data

• [ ] Graph attention network (GAT) for node classification
• [ ] Node Classification with Graph Neural Networks
• [ ] Message-passing neural network (MPNN) for molecular property prediction
• [ ] Graph representation learning with node2vec