EXAMPLES_MASTER = {
"path": "examples/",
"title": "Code examples",
"toc": False,
"children": [
{
"path": "vision/",
"title": "Computer Vision",
"toc": True,
"children": [
{
"path": "image_classification_from_scratch",
"title": "Image classification from scratch",
"subcategory": "Image classification",
"highlight": True,
"keras_3": True,
},
{
"path": "mnist_convnet",
"title": "Simple MNIST convnet",
"subcategory": "Image classification",
"highlight": True,
"keras_3": True,
},
{
"path": "image_classification_efficientnet_fine_tuning",
"title": "Image classification via fine-tuning with EfficientNet",
"subcategory": "Image classification",
"highlight": True,
"keras_3": True,
},
{
"path": "image_classification_with_vision_transformer",
"title": "Image classification with Vision Transformer",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "attention_mil_classification",
"title": "Classification using Attention-based Deep Multiple Instance Learning",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "mlp_image_classification",
"title": "Image classification with modern MLP models",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "mobilevit",
"title": "A mobile-friendly Transformer-based model for image classification",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "xray_classification_with_tpus",
"title": "Pneumonia Classification on TPU",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "cct",
"title": "Compact Convolutional Transformers",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "convmixer",
"title": "Image classification with ConvMixer",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "eanet",
"title": "Image classification with EANet (External Attention Transformer)",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "involution",
"title": "Involutional neural networks",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "perceiver_image_classification",
"title": "Image classification with Perceiver",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "reptile",
"title": "Few-Shot learning with Reptile",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "semisupervised_simclr",
"title": "Semi-supervised image classification using contrastive pretraining with SimCLR",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "swin_transformers",
"title": "Image classification with Swin Transformers",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "vit_small_ds",
"title": "Train a Vision Transformer on small datasets",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "shiftvit",
"title": "A Vision Transformer without Attention",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "image_classification_using_global_context_vision_transformer",
"title": "Image Classification using Global Context Vision Transformer",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "temporal_latent_bottleneck",
"title": "When Recurrence meets Transformers",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "oxford_pets_image_segmentation",
"title": "Image segmentation with a U-Net-like architecture",
"subcategory": "Image segmentation",
"highlight": True,
"keras_3": True,
},
{
"path": "deeplabv3_plus",
"title": "Multiclass semantic segmentation using DeepLabV3+",
"subcategory": "Image segmentation",
"keras_3": True,
},
{
"path": "basnet_segmentation",
"title": "Highly accurate boundaries segmentation using BASNet",
"subcategory": "Image segmentation",
},
{
"path": "fully_convolutional_network",
"title": "Image Segmentation using Composable Fully-Convolutional Networks",
"subcategory": "Image segmentation",
"keras_3": True,
},
{
"path": "retinanet",
"title": "Object Detection with RetinaNet",
"subcategory": "Object detection",
},
{
"path": "keypoint_detection",
"title": "Keypoint Detection with Transfer Learning",
"subcategory": "Object detection",
"keras_3": True,
},
{
"path": "object_detection_using_vision_transformer",
"title": "Object detection with Vision Transformers",
"subcategory": "Object detection",
"keras_3": True,
},
{
"path": "3D_image_classification",
"title": "3D image classification from CT scans",
"subcategory": "3D",
"keras_3": True,
},
{
"path": "depth_estimation",
"title": "Monocular depth estimation",
"subcategory": "3D",
"keras_3": True,
},
{
"path": "nerf",
"title": "3D volumetric rendering with NeRF",
"subcategory": "3D",
"keras_3": True,
"highlight": True,
},
{
"path": "pointnet_segmentation",
"title": "Point cloud segmentation with PointNet",
"subcategory": "3D",
"keras_3": True,
},
{
"path": "pointnet",
"title": "Point cloud classification",
"subcategory": "3D",
"keras_3": True,
},
{
"path": "captcha_ocr",
"title": "OCR model for reading Captchas",
"subcategory": "OCR",
"keras_3": True,
},
{
"path": "handwriting_recognition",
"title": "Handwriting recognition",
"subcategory": "OCR",
"keras_3": True,
},
{
"path": "autoencoder",
"title": "Convolutional autoencoder for image denoising",
"subcategory": "Image enhancement",
"keras_3": True,
},
{
"path": "mirnet",
"title": "Low-light image enhancement using MIRNet",
"subcategory": "Image enhancement",
"keras_3": True,
},
{
"path": "super_resolution_sub_pixel",
"title": "Image Super-Resolution using an Efficient Sub-Pixel CNN",
"subcategory": "Image enhancement",
"keras_3": True,
},
{
"path": "edsr",
"title": "Enhanced Deep Residual Networks for single-image super-resolution",
"subcategory": "Image enhancement",
"keras_3": True,
},
{
"path": "zero_dce",
"title": "Zero-DCE for low-light image enhancement",
"subcategory": "Image enhancement",
"keras_3": True,
},
{
"path": "cutmix",
"title": "CutMix data augmentation for image classification",
"subcategory": "Data augmentation",
"keras_3": True,
},
{
"path": "mixup",
"title": "MixUp augmentation for image classification",
"subcategory": "Data augmentation",
"keras_3": True,
},
{
"path": "randaugment",
"title": "RandAugment for Image Classification for Improved Robustness",
"subcategory": "Data augmentation",
"keras_3": True,
},
{
"path": "image_captioning",
"title": "Image captioning",
"subcategory": "Image & Text",
"highlight": True,
"keras_3": True,
},
{
"path": "nl_image_search",
"title": "Natural language image search with a Dual Encoder",
"subcategory": "Image & Text",
},
{
"path": "visualizing_what_convnets_learn",
"title": "Visualizing what convnets learn",
"subcategory": "Vision models interpretability",
"keras_3": True,
},
{
"path": "integrated_gradients",
"title": "Model interpretability with Integrated Gradients",
"subcategory": "Vision models interpretability",
"keras_3": True,
},
{
"path": "probing_vits",
"title": "Investigating Vision Transformer representations",
"subcategory": "Vision models interpretability",
"keras_3": True,
},
{
"path": "grad_cam",
"title": "Grad-CAM class activation visualization",
"subcategory": "Vision models interpretability",
"keras_3": True,
},
{
"path": "near_dup_search",
"title": "Near-duplicate image search",
"subcategory": "Image similarity search",
},
{
"path": "semantic_image_clustering",
"title": "Semantic Image Clustering",
"subcategory": "Image similarity search",
"keras_3": True,
},
{
"path": "siamese_contrastive",
"title": "Image similarity estimation using a Siamese Network with a contrastive loss",
"subcategory": "Image similarity search",
"keras_3": True,
},
{
"path": "siamese_network",
"title": "Image similarity estimation using a Siamese Network with a triplet loss",
"subcategory": "Image similarity search",
"keras_3": True,
},
{
"path": "metric_learning",
"title": "Metric learning for image similarity search",
"subcategory": "Image similarity search",
"keras_3": True,
},
{
"path": "metric_learning_tf_similarity",
"title": "Metric learning for image similarity search using TensorFlow Similarity",
"subcategory": "Image similarity search",
},
{
"path": "nnclr",
"title": "Self-supervised contrastive learning with NNCLR",
"subcategory": "Image similarity search",
"keras_3": True,
},
{
"path": "video_classification",
"title": "Video Classification with a CNN-RNN Architecture",
"subcategory": "Video",
"keras_3": True,
},
{
"path": "conv_lstm",
"title": "Next-Frame Video Prediction with Convolutional LSTMs",
"subcategory": "Video",
"keras_3": True,
},
{
"path": "video_transformers",
"title": "Video Classification with Transformers",
"subcategory": "Video",
"keras_3": True,
},
{
"path": "vivit",
"title": "Video Vision Transformer",
"subcategory": "Video",
"keras_3": True,
},
{
"path": "bit",
"title": "Image Classification using BigTransfer (BiT)",
"subcategory": "Image classification",
"keras_3": True,
},
{
"path": "gradient_centralization",
"title": "Gradient Centralization for Better Training Performance",
"subcategory": "Performance recipes",
"keras_3": True,
},
{
"path": "token_learner",
"title": "Learning to tokenize in Vision Transformers",
"subcategory": "Performance recipes",
"keras_3": True,
},
{
"path": "knowledge_distillation",
"title": "Knowledge Distillation",
"subcategory": "Performance recipes",
"keras_3": True,
},
{
"path": "fixres",
"title": "FixRes: Fixing train-test resolution discrepancy",
"subcategory": "Performance recipes",
"keras_3": True,
},
{
"path": "cait",
"title": "Class Attention Image Transformers with LayerScale",
"subcategory": "Performance recipes",
"keras_3": True,
},
{
"path": "patch_convnet",
"title": "Augmenting convnets with aggregated attention",
"subcategory": "Performance recipes",
"keras_3": True,
},
{
"path": "learnable_resizer",
"title": "Learning to Resize",
"subcategory": "Performance recipes",
"keras_3": True,
},
],
},
{
"path": "nlp/",
"title": "Natural Language Processing",
"toc": True,
"children": [
{
"path": "text_classification_from_scratch",
"title": "Text classification from scratch",
"subcategory": "Text classification",
"highlight": True,
"keras_3": True,
},
{
"path": "active_learning_review_classification",
"title": "Review Classification using Active Learning",
"subcategory": "Text classification",
"keras_3": True,
},
{
"path": "fnet_classification_with_keras_hub",
"title": "Text Classification using FNet",
"subcategory": "Text classification",
"keras_3": True,
},
{
"path": "multi_label_classification",
"title": "Large-scale multi-label text classification",
"subcategory": "Text classification",
},
{
"path": "text_classification_with_transformer",
"title": "Text classification with Transformer",
"subcategory": "Text classification",
"keras_3": True,
},
{
"path": "text_classification_with_switch_transformer",
"title": "Text classification with Switch Transformer",
"subcategory": "Text classification",
"keras_3": True,
},
{
"path": "tweet-classification-using-tfdf",
"title": "Text classification using Decision Forests and pretrained embeddings",
"subcategory": "Text classification",
},
{
"path": "pretrained_word_embeddings",
"title": "Using pre-trained word embeddings",
"subcategory": "Text classification",
"keras_3": True,
},
{
"path": "bidirectional_lstm_imdb",
"title": "Bidirectional LSTM on IMDB",
"subcategory": "Text classification",
"keras_3": True,
},
{
"path": "data_parallel_training_with_keras_hub",
"title": "Data Parallel Training with KerasHub and tf.distribute",
"subcategory": "Text classification",
"keras_3": True,
},
{
"path": "neural_machine_translation_with_keras_hub",
"title": "English-to-Spanish translation with KerasHub",
"subcategory": "Machine translation",
"keras_3": True,
},
{
"path": "neural_machine_translation_with_transformer",
"title": "English-to-Spanish translation with a sequence-to-sequence Transformer",
"subcategory": "Machine translation",
"highlight": True,
"keras_3": True,
},
{
"path": "lstm_seq2seq",
"title": "Character-level recurrent sequence-to-sequence model",
"subcategory": "Machine translation",
"keras_3": True,
},
{
"path": "multimodal_entailment",
"title": "Multimodal entailment",
"subcategory": "Entailment prediction",
},
{
"path": "ner_transformers",
"title": "Named Entity Recognition using Transformers",
"subcategory": "Named entity recognition",
"keras_3": True,
},
{
"path": "text_extraction_with_bert",
"title": "Text Extraction with BERT",
"subcategory": "Sequence-to-sequence",
},
{
"path": "addition_rnn",
"title": "Sequence to sequence learning for performing number addition",
"subcategory": "Sequence-to-sequence",
"keras_3": True,
},
{
"path": "semantic_similarity_with_keras_hub",
"title": "Semantic Similarity with KerasHub",
"subcategory": "Text similarity search",
"keras_3": True,
},
{
"path": "semantic_similarity_with_bert",
"title": "Semantic Similarity with BERT",
"subcategory": "Text similarity search",
"keras_3": True,
},
{
"path": "sentence_embeddings_with_sbert",
"title": "Sentence embeddings using Siamese RoBERTa-networks",
"subcategory": "Text similarity search",
"keras_3": True,
},
{
"path": "masked_language_modeling",
"title": "End-to-end Masked Language Modeling with BERT",
"subcategory": "Language modeling",
"keras_3": True,
},
{
"path": "abstractive_summarization_with_bart",
"title": "Abstractive Text Summarization with BART",
"subcategory": "Language modeling",
"keras_3": True,
},
{
"path": "parameter_efficient_finetuning_of_gpt2_with_lora",
"title": "Parameter-efficient fine-tuning of GPT-2 with LoRA",
"subcategory": "Parameter efficient fine-tuning",
"keras_3": True,
},
],
},
{
"path": "structured_data/",
"title": "Structured Data",
"toc": True,
"children": [
{
"path": "structured_data_classification_with_feature_space",
"title": "Structured data classification with FeatureSpace",
"subcategory": "Structured data classification",
"highlight": True,
"keras_3": True,
},
{
"path": "feature_space_advanced",
"title": "FeatureSpace advanced use cases",
"subcategory": "Structured data classification",
"highlight": True,
"keras_3": True,
},
{
"path": "imbalanced_classification",
"title": "Imbalanced classification: credit card fraud detection",
"subcategory": "Structured data classification",
"highlight": True,
"keras_3": True,
},
{
"path": "structured_data_classification_from_scratch",
"title": "Structured data classification from scratch",
"subcategory": "Structured data classification",
"keras_3": True,
},
{
"path": "wide_deep_cross_networks",
"title": "Structured data learning with Wide, Deep, and Cross networks",
"subcategory": "Structured data classification",
"keras_3": True,
},
{
"path": "customer_lifetime_value",
"title": "Deep Learning for Customer Lifetime Value",
"subcategory": "Structured data regression",
"keras_3": True,
},
{
"path": "classification_with_grn_and_vsn",
"title": "Classification with Gated Residual and Variable Selection Networks",
"subcategory": "Structured data classification",
"keras_3": True,
},
{
"path": "classification_with_tfdf",
"title": "Classification with TensorFlow Decision Forests",
"subcategory": "Structured data classification",
},
{
"path": "deep_neural_decision_forests",
"title": "Classification with Neural Decision Forests",
"subcategory": "Structured data classification",
"keras_3": True,
},
{
"path": "tabtransformer",
"title": "Structured data learning with TabTransformer",
"subcategory": "Structured data classification",
"keras_3": True,
},
{
"path": "collaborative_filtering_movielens",
"title": "Collaborative Filtering for Movie Recommendations",
"subcategory": "Recommendation",
"keras_3": True,
},
{
"path": "movielens_recommendations_transformers",
"title": "A Transformer-based recommendation system",
"subcategory": "Recommendation",
"keras_3": True,
},
],
},
{
"path": "timeseries/",
"title": "Timeseries",
"toc": True,
"children": [
{
"path": "timeseries_classification_from_scratch",
"title": "Timeseries classification from scratch",
"subcategory": "Timeseries classification",
"highlight": True,
"keras_3": True,
},
{
"path": "timeseries_classification_transformer",
"title": "Timeseries classification with a Transformer model",
"subcategory": "Timeseries classification",
"keras_3": True,
},
{
"path": "eeg_signal_classification",
"title": "Electroencephalogram Signal Classification for action identification",
"subcategory": "Timeseries classification",
"keras_3": True,
},
{
"path": "event_classification_for_payment_card_fraud_detection",
"title": "Event classification for payment card fraud detection",
"subcategory": "Timeseries classification",
"keras_3": True,
},
{
"path": "timeseries_anomaly_detection",
"title": "Timeseries anomaly detection using an Autoencoder",
"subcategory": "Anomaly detection",
"keras_3": True,
},
{
"path": "timeseries_traffic_forecasting",
"title": "Traffic forecasting using graph neural networks and LSTM",
"subcategory": "Timeseries forecasting",
"keras_3": True,
},
{
"path": "timeseries_weather_forecasting",
"title": "Timeseries forecasting for weather prediction",
"subcategory": "Timeseries forecasting",
"keras_3": True,
},
],
},
{
"path": "generative/",
"title": "Generative Deep Learning",
"toc": True,
"children": [
{
"path": "ddim",
"title": "Denoising Diffusion Implicit Models",
"subcategory": "Image generation",
"highlight": True,
"keras_3": True,
},
{
"path": "random_walks_with_stable_diffusion_3",
"title": "A walk through latent space with Stable Diffusion 3",
"subcategory": "Image generation",
"highlight": True,
"keras_3": True,
},
{
"path": "dreambooth",
"title": "DreamBooth",
"subcategory": "Image generation",
},
{
"path": "ddpm",
"title": "Denoising Diffusion Probabilistic Models",
"subcategory": "Image generation",
},
{
"path": "fine_tune_via_textual_inversion",
"title": "Teach StableDiffusion new concepts via Textual Inversion",
"subcategory": "Image generation",
},
{
"path": "finetune_stable_diffusion",
"title": "Fine-tuning Stable Diffusion",
"subcategory": "Image generation",
},
{
"path": "vae",
"title": "Variational AutoEncoder",
"subcategory": "Image generation",
"keras_3": True,
},
{
"path": "dcgan_overriding_train_step",
"title": "GAN overriding Model.train_step",
"subcategory": "Image generation",
"keras_3": True,
},
{
"path": "wgan_gp",
"title": "WGAN-GP overriding Model.train_step",
"subcategory": "Image generation",
"keras_3": True,
},
{
"path": "conditional_gan",
"title": "Conditional GAN",
"subcategory": "Image generation",
"keras_3": True,
},
{
"path": "cyclegan",
"title": "CycleGAN",
"subcategory": "Image generation",
"keras_3": True,
},
{
"path": "gan_ada",
"title": "Data-efficient GANs with Adaptive Discriminator Augmentation",
"subcategory": "Image generation",
"keras_3": True,
},
{
"path": "deep_dream",
"title": "Deep Dream",
"subcategory": "Image generation",
"keras_3": True,
},
{
"path": "gaugan",
"title": "GauGAN for conditional image generation",
"subcategory": "Image generation",
"keras_3": True,
},
{
"path": "pixelcnn",
"title": "PixelCNN",
"subcategory": "Image generation",
"keras_3": True,
},
{
"path": "stylegan",
"title": "Face image generation with StyleGAN",
"subcategory": "Image generation",
},
{
"path": "vq_vae",
"title": "Vector-Quantized Variational Autoencoders",
"subcategory": "Image generation",
},
{
"path": "random_walks_with_stable_diffusion",
"title": "A walk through latent space with Stable Diffusion",
"subcategory": "Image generation",
"keras_3": True,
},
{
"path": "neural_style_transfer",
"title": "Neural style transfer",
"subcategory": "Style transfer",
"keras_3": True,
},
{
"path": "adain",
"title": "Neural Style Transfer with AdaIN",
"subcategory": "Style transfer",
},
{
"path": "gpt2_text_generation_with_keras_hub",
"title": "GPT2 Text Generation with KerasHub",
"subcategory": "Text generation",
"highlight": True,
"keras_3": True,
},
{
"path": "text_generation_gpt",
"title": "GPT text generation from scratch with KerasHub",
"subcategory": "Text generation",
"keras_3": True,
},
{
"path": "text_generation_with_miniature_gpt",
"title": "Text generation with a miniature GPT",
"subcategory": "Text generation",
"keras_3": True,
},
{
"path": "lstm_character_level_text_generation",
"title": "Character-level text generation with LSTM",
"subcategory": "Text generation",
"keras_3": True,
},
{
"path": "text_generation_fnet",
"title": "Text Generation using FNet",
"subcategory": "Text generation",
},
{
"path": "midi_generation_with_transformer",
"title": "Music Generation with Transformer Models",
"subcategory": "Audio generation",
"keras_3": True,
},
{
"path": "molecule_generation",
"title": "Drug Molecule Generation with VAE",
"subcategory": "Graph generation",
"keras_3": True,
},
{
"path": "wgan-graphs",
"title": "WGAN-GP with R-GCN for the generation of small molecular graphs",
"subcategory": "Graph generation",
},
],
},
{
"path": "audio/",
"title": "Audio Data",
"toc": True,
"children": [
{
"path": "vocal_track_separation",
"title": "Vocal Track Separation with Encoder-Decoder Architecture",
"subcategory": "Vocal track separation",
"keras_3": True,
},
{
"path": "transformer_asr",
"title": "Automatic Speech Recognition with Transformer",
"subcategory": "Speech recognition",
"keras_3": True,
},
],
},
{
"path": "rl/",
"title": "Reinforcement Learning",
"toc": True,
"children": [
{
"path": "actor_critic_cartpole",
"title": "Actor Critic Method",
"subcategory": "RL algorithms",
"keras_3": True,
},
{
"path": "ppo_cartpole",
"title": "Proximal Policy Optimization",
"subcategory": "RL algorithms",
"keras_3": True,
},
{
"path": "deep_q_network_breakout",
"title": "Deep Q-Learning for Atari Breakout",
"subcategory": "RL algorithms",
"keras_3": True,
},
{
"path": "ddpg_pendulum",
"title": "Deep Deterministic Policy Gradient (DDPG)",
"subcategory": "RL algorithms",
"keras_3": True,
},
],
},
{
"path": "graph/",
"title": "Graph Data",
"toc": True,
"children": [
],
},
{
"path": "keras_recipes/",
"title": "Quick Keras Recipes",
"toc": True,
"children": [
{
"path": "parameter_efficient_finetuning_of_gemma_with_lora_and_qlora",
"title": "Parameter-efficient fine-tuning of Gemma with LoRA and QLoRA",
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