Here’s a significantly more advanced and entirely different Keras project — focused on automated hyperparameter tuning for deep CNNs using Keras Tuner on CIFAR‑100. This takes your last project to the next level by integrating cutting‑edge tuning strategies (Hyperband, Bayesian), custom callbacks, and distributed training for high scalability. Project Title ai-ml-ds-TjXqZ1rYpNw — Auto‑Tuned Deep CNN on CIFAR‑100 with Keras Tuner…

Here’s a far more advanced Keras project focused on automated hyperparameter tuning for a convolutional neural network on CIFAR-100, using Keras Tuner’s Hyperband and Bayesian Optimization. This takes your last project to the next level by bringing optimization into architecture and training pipeline, with multi-algorithm search, advanced callbacks, checkpointing, and evaluation. Project Title ai-ml-ds-XkLrP6Zg8N1cifar100_advanced_hpo_keras_tuner.py 📌 Short Description…

Here’s a high-level advanced Keras project focused on hyperparameter optimization using Keras Tuner with a modern dataset and state-of-the-art techniques. Project Title ai-ml-ds-KerasTunerCIFAR100File name: auto_hyperparam_cifar100_tuner.py 📄 Short Description Build a convolutional neural network (CNN) for CIFAR‑100 image classification, using Keras Tuner (Hyperband + Bayesian) to automatically optimize architecture (depth, width, dropout, data‑aug) and training hyperparameters. Advanced components: preprocessing layers, transfer learning trunk, tuner subclass for custom trial logic.

Here’s a far more advanced Keras project—leveraging Keras Tuner for hyperparameter optimization on a ResNet‑based CIFAR‑10 classifier. Most of the content is Python code with type annotations; only the essentials are summarized. Project Title ai‑ml‑ds‑HjKqRt9BFile: tuned_resnet_cifar10_with_keras_tuner.py 📌 Short Description A CIFAR‑10 image classification project using a HyperResNet model optimized via Bayesian or Hyperband search. It dynamically tunes number of filters, depth, dropout rates, learning rate, augmentation strategies, and dense layer size—all with Keras Tuner.

Below is an advanced Keras Tuner-based image classification project — far beyond basic examples, designed for a seasoned AI/ML expert like you: Project Title ai-ml-ds-Kr4ZpjXqN20 – Hyperparameter‑Optimized Deep Residual CNN for CIFAR‑10File: hyperopt_resnet_cifar10.py Short Description:Build a Keras model using the Functional API: a ResNet‑style CNN tuned end‑to‑end using keras_tuner (Hyperband, Bayesian), optimizing architecture depth, width, learning rate, dropout, and data augmentation.

Here’s an advanced Keras project using hyperparameter tuning (Bayesian+Hyperband) on CIFAR‑10 classification—a significant step beyond standard model training: Project Title ai-ml-ds-TyN8PqZfLm — Hyperparameter-Optimized Deep CNN with Ensemble via Keras Tuner Filename: hyperopt_deep_cifar10.py 🔍 Short Description Build a deep CNN on CIFAR‑10 using Keras functional API, optimize architecture and training hyperparameters via Bayesian+Hyperband tuner, then ensemble best models for improved accuracy. Ultra‑advanced, optimized, and modular for production pipelines.

Here’s an advanced Keras Neural Architecture Search (NAS) project that builds upon your expertise and pushes beyond previous work: 🚀 Project Title ai-ml-ds-Zx7KPLQWemFilename: keras_neural_architecture_search_nas.py Short Description:Automate design of optimal convolutional architectures using Keras Tuner and modular NAS blocks. On CIFAR‑10 this uses Bayesian optimization with search-space blocks and callbacks for dynamic pruning—far more advanced than standard CNNs. # keras_neural_architecture_search_nas.py import tensorflow as tf from tensorflow import keras from keras import layers import kerastuner as kt from kerastuner import HyperParameters from typing import Tuple # Load dataset (x_train: tf.Tensor, y_train: tf.Tensor), (x_test: tf.Tensor, y_test: tf.Tensor) = keras.datasets.cifar10.load_data() x_train, x_test = x_train / 255.0, x_test / 255.0 y_train = keras.utils.to_categorical(y_train, 10) y_test = keras.utils.to_categorical(y_test, 10) def model_builder(hp: HyperParameters) -> keras.Model: inputs: layers.Input = keras.Input(shape=(32,32,3)) x = inputs # Search space: number of conv blocks for i in range(hp.Int('conv_blocks', 2, 4, default=3)): filters: int = hp.Choice(f'filters_{i}', ) kernel: int = hp.Choice(f'kernel_{i}', ) x = layers.Conv2D(filters, kernel, activation='relu', padding='same')(x) x = layers.BatchNormalization()(x) if hp.Boolean(f'maxpool_{i}'): x = layers.MaxPooling2D()(x) x = layers.Flatten()(x) # Dense hyperparameters for j in range(hp.Int('dense_layers',1,2,default=1)): units: int = hp.Int(f'units_{j}', 64,256, step=64) x = layers.Dense(units, activation=hp.Choice(f'act_{j}', ['relu','tanh']))(x) outputs = layers.Dense(10, activation='softmax')(x) model: keras.Model = keras.Model(inputs, outputs) # Compile model.compile( optimizer=keras.optimizers.Adam( hp.Float('lr',1e-4,1e-2, sampling='log')), loss='categorical_crossentropy', metrics=['accuracy'] ) return model tuner = kt.BayesianOptimization( model_builder, objective='val_accuracy', max_trials=20, directory='nas_dir', project_name='cifar10_nas' ) tuner.search(x_train, y_train, epochs=20, validation_split=0.2, callbacks=) best_model: keras.Model = tuner.get_best_models(num_models=1)[0] loss, acc = best_model.evaluate(x_test, y_test) print(f"Test acc: {acc:.4f}") # Save/export best_model.save('best_nas_cifar10.h5') …

Here’s a highly advanced Keras + TensorFlow project—far beyond standard CNN/RNN work—focused on Neural Architecture Search (NAS) for object detection, utilizing EfficientDet-style compound scaling. This leverages keras_tuner for automatic architecture exploration. 🧠 Project Title ai-ml-ds-QpLmZ8xCvBn: Auto‑Scaled Object Detector via NASFilename: auto_scaled_detector_nas.py Short Description:Automatically searches and tunes a mobile-friendly object detector architecture, scaling depth, width, resolution, and FPN levels.

Here’s a highly advanced Keras + Spektral project leveraging graph neural networks for multivariate time-series forecasting on spatial-temporal graphs—a big leap from previous work. 🧠 Project Title ai-ml-ds-xYwZa8GhTkM: Spatial‑Temporal Forecasting with Graph Neural NetworksFilename: spatial_temporal_gnn_forecasting.py Short Description:A Spektral-based Keras model that extends T‑GCN and StemGNN principles: it embeds a dynamic graph structure (e.g., sensor network), models temporal dependencies via GRU, and leverages GCN for spatial graph convolution.

Here’s an advanced Keras + Spektral project using temporal Graph Neural Networks for multivariate time‑series forecasting—a major leap beyond typical CNN/RNN tasks: 🧠 Project Title ai‑ml‑ds‑JfV9kLpXzNf – Temporal Graph Neural Network for Multivariate Time‑Series ForecastingFilename: temporal_graph_forecasting_spektral.py 🔍 Short Description Build and train a Temporal Graph Neural Network (TGNN) in Keras using Spektral to forecast multiple time‑series that have inter-node dependencies.

Here’s an advanced Graph Neural Network project using Keras + Spektral, leveraging Graph Attention Layers (GAT) for multi-graph temporal forecasting on synthetic dynamic graph data. This is a significant leap beyond static classification, tackling structural evolution over time. 🚀 Project Title ai-ml-ds-KvWpL1xBtQzFile: temporal_graph_attention_forecasting.py 📌 Short Description This project builds a spatio‑temporal GAT model that predicts future node features given a sequence of evolving graphs.

Here’s an ultra-advanced Keras‑based project using Graph Neural Networks (GNNs) via the Spektral library on the Cora citation dataset. It focuses on Graph Attention Networks (GATs) with multi-layer attention, custom relational embeddings, and over‑smoothing mitigation strategies—a significant step up from standard CNN/RNN tasks. Project Title ai-ml-ds-XlqGAT4Hn2Filename: advanced_gat_cora_with_spektral.py Short Description Build a multi‑layered GAT with relational embeddings (e.g. abs-diff of node features) and skip‑connections using Spektral and Keras to boost deep GAT performance beyond standard on Cora, addressing the over‑smoothing problem (

Here's a high‑level summary of this advanced Keras project using graph neural networks (GNN): Project Title:ai-ml-ds-GraphGAT-ExpFalcoFile: graph_gat_node_classification.py Short Description:Implement a Graph Attention Network (GAT) in Keras using the Cora citation dataset for semi-supervised node classification. This version uses Spektral library for efficiency, multi-head attention, early stopping, GPU acceleration, and visualization of learned embeddings—significantly more advanced than standard dense or CNN models.

Here’s a highly advanced Keras project leveraging Graph Attention Networks (GATs)—a major leap beyond conventional CNNs/RNNs. This is markedly different from common image-based or sequence projects and taps into state-of-the-art graph-based deep learning. 🧠 Project Title & File Project Title: ai-ml-ds-Xh5QzYpRtL2 – Graph Attention Network for Node Classification (Cora Dataset)Filename: graph_attention_node_classification_gat.py Short Description:Build a multi-head Graph Attention Network (GAT) in pure Keras/TensorFlow to classify nodes in the Cora citation graph based on node features and citation structure.

Here’s a next-level, advanced Graph Attention Network (GAT) project using Keras—ideal for someone with your expertise looking for a fresh challenge in graph-structured learning. 🧠 Project Title ai-ml-ds-GatXNodeClassifyFilename: gat_node_classification_cora.py 🎯 Short Description (Keep it brief) Build a Graph Attention Network on the Cora citation dataset using Keras and TensorFlow. This model uses multi-head attention to learn node representations and performs node classification with transductive learning.

Here’s a significantly more advanced Keras project designed for a seasoned engineer like you: it implements a Graph Neural Network (GNN) using Graph Attention Networks (GAT) in Keras, trained on a citation dataset for node classification. It’s markedly different, tackling graphs, attention, and deep embedding—far beyond typical CNN or VAE workflows. Project Title ai-ml-ds-VaZnkfTgQwL – Graph Attention Network for Citation Node Classification…

Here’s a far more advanced Keras project, building on your extensive experience—this time implementing a Variational Autoencoder (VAE) with attention modules on the Fashion-MNIST dataset, and then using it for controlled image generation. The goal is to teach representation learning, disentanglement, and generative modeling with attention. The code comprises ~98% of the content; summary and usage follow. Project Title…

Below is a highly advanced Keras project—far more complex than typical examples—leveraging contrastive self-supervised learning (SimCLR) on the STL‑10 dataset, then fine-tuning for semi-supervised classification. The code is ~98% of this response; remaining sections provide context and further guidance. Project Title ai-ml-ds-SrmZNuoOhMk – Self‑Supervised SimCLR + Semi‑supervised Fine‑Tuning File: self_supervised_simclr_semi_finetune.py import tensorflow as tf import tensorflow_datasets as tfds from keras import layers, models, optimizers, losses, callbacks import numpy as np import datetime # Settings num_epochs: int = 50 batch_size: int = 512 temperature: float = 0.1 image_size: tuple = (96, 96, 3) width: int = 128 logdir: str = "logs/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S") # Data pipeline def augment_for_contrastive(img: tf.Tensor) -> tf.Tensor: img = tf.image.random_crop(img, size=image_size) img = tf.image.random_flip_left_right(img) img = tf.image.random_brightness(img, 0.5) img = tf.image.random_contrast(img, 0.8, 1.2) img = tf.clip_by_value(img / 255.0, 0.0, 1.0) return img def prepare_datasets(): ds_unlabeled = tfds.load("stl10", split="unlabelled", as_supervised=True) ds_labeled = tfds.load("stl10", split="train", as_supervised=True) ds_test = tfds.load("stl10", split="test", as_supervised=True) ds = ds_unlabeled.map(lambda x,_: augment_for_contrastive(x)) ds = ds.shuffle(10000).batch(batch_size).map(lambda x: (tf.concat(,0))) ds = ds.prefetch(tf.data.AUTOTUNE) ds_labeled = ds_labeled.map(lambda x,y: (tf.image.resize(x, image_size[:2])/255.0, y)) ds_labeled = ds_labeled.shuffle(5000).batch(batch_size).prefetch(tf.data.AUTOTUNE) ds_test = ds_test.map(lambda x,y: (tf.image.resize(x, image_size[:2])/255.0, y)) ds_test = ds_test.batch(batch_size).prefetch(tf.data.AUTOTUNE) return ds, ds_labeled, ds_test ds_contrastive, ds_labeled, ds_test = prepare_datasets() # Model def get_encoder() -> tf.keras.Model: inp = layers.Input(shape=image_size) x = layers.Conv2D(64,3,activation="relu")(inp) x = layers.MaxPool2D()(x) x = layers.Conv2D(128,3,activation="relu")(x) x = layers.GlobalAveragePooling2D()(x) return models.Model(inp, x, name="encoder") def get_projection_head() -> tf.keras.Model: inp = layers.Input(shape=(width,)) x = layers.Dense(width, activation="relu")(inp) x = layers.Dense(width)(x) return models.Model(inp, x, name="proj_head") encoder = get_encoder() proj_head = get_projection_head() # Contrastive loss def nt_xent_loss(z_i: tf.Tensor, z_j: tf.Tensor) -> tf.Tensor: z = tf.concat(, axis=0) z = tf.math.l2_normalize(z, axis=1) logits = tf.matmul(z, z, transpose_b=True) / temperature batch = tf.shape(z_i)[0] labels = tf.concat(, axis=0) loss = losses.sparse_categorical_crossentropy(labels, logits, from_logits=True) return tf.reduce_mean(loss) # Training loop optimizer = optimizers.Adam(1e-3) for epoch in range(num_epochs): for batch in ds_contrastive: imgs1, imgs2 = tf.split(batch, num_or_size_splits=2, axis=0) with tf.GradientTape() as tape: h1 = encoder(imgs1, training=True); h2 = encoder(imgs2, training=True) z1 = proj_head(h1, training=True); z2 = proj_head(h2, training=True) loss = nt_xent_loss(z1, z2) grads = tape.gradient(loss, encoder.trainable_variables + proj_head.trainable_variables) optimizer.apply_gradients(zip(grads, encoder.trainable_variables + proj_head.trainable_variables)) print(f"Epoch {epoch+1}/{num_epochs}, Contrastive Loss: {loss.numpy():.4f}") # Fine-tuning classifier fine_input = layers.Input(shape=image_size) h = encoder(fine_input, training=False) h = layers.Dense(256, activation="relu")(h) out = layers.Dense(10)(h) classifier = models.Model(fine_input, out) classifier.compile(optimizers.Adam(1e-4), loss=losses.SparseCategoricalCrossentropy(from_logits=True), metrics=["accuracy"]) classifier.fit(ds_labeled, epochs=20, validation_data=ds_test, callbacks=) # Save encoder.save("encoder_simclr.h5") classifier.save("classifier_simclr_finetuned.h5") …

Here’s a high‑level advanced Keras project: implementing SimCLR-style self‑supervised contrastive learning on the STL-10 dataset, enabling your model to learn powerful embeddings without labels and then fine‑tune for classification. This is significantly more advanced than typical supervised models and introduces complex augmentation pipelines, projection heads, contrastive loss (NT-Xent), and semi‑supervised tuning. Project Title ai-ml-ds-ZeP7QdXkVyAFilename: simclr_ssl_stl10.py Short Description Self‑supervised training using SimCLR on STL‑10: learn representations via contrastive loss on augmented image pairs then fine‑tune a linear classifier using a small labeled subset.

Here’s a significantly advanced Keras project building upon your stellar background in ML/AI—with substantial code bulk and multiple real-world use cases. 🧠 Project Title ai-ml-ds-X7bQZahlRt – Semi‑Supervised Contrastive Pretraining with SimCLR File name: semi_supervised_contrastive_simclr.py 🔎 Description We implement a semi-supervised SimCLR pipeline using Keras 3 with TensorFlow backend. The model is pretrained in a self-supervised manner on unlabeled STL‑10 data, then fine-tuned with a labeled subset.

Here’s an advanced Keras project—self-supervised contrastive learning with SimCLR—designed to elevate your previous work substantially: Project Title ai-ml-ds-ZxYTan3LwpQsimclr_contrastive_pretraining.py 📌 Short Description Build a full SimCLR self‑supervised pipeline using Keras: dual-augmentation streams, ResNet50 encoder with projection head, NT-Xent contrastive loss, and linear evaluation. Pretrain on unlabeled STL-10, then fine-tune on its small labeled split. # simclr_contrastive_pretraining.py import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers import tensorflow_datasets as tfds import numpy as np # hyperparameters IMG_SIZE: int = 96 BATCH_SIZE: int = 256 EPOCHS_PRETRAIN: int = 100 EPOCHS_FINETUNE: int = 30 PROJ_DIM: int = 128 TEMPERATURE: float = 0.5 LEARNING_RATE: float = 1e-3 # augmentations def augment_view(image): image = tf.image.random_crop(image, size=) image = tf.image.random_flip_left_right(image) image = tf.image.random_brightness(image, max_delta=0.5) return tf.clip_by_value(image, 0., 1.) def prepare_stl10() -> tf.data.Dataset: data_unlabeled = tfds.load('stl10', split='unlabeled', as_supervised=True) ds = (data_unlabeled.map(lambda x,_: tf.image.resize(x, ) / 255.0) .map(lambda x: (augment_view(x), augment_view(x))) .shuffle(10000).batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)) fine = tfds.load('stl10', split='train', as_supervised=True) fine = (fine.map(lambda x,y: (tf.image.resize(x,) / 255.0, y)) .shuffle(5000).batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)) test = tfds.load('stl10', split='test', as_supervised=True) test = (test.map(lambda x,y: (tf.image.resize(x,) / 255.0, y)) .batch(BATCH_SIZE).prefetch(tf.data.AUTOTUNE)) return ds, fine, test # NT-Xent contrast loss def nt_xent_loss(z_i, z_j): z = tf.concat(, axis=0) sim = tf.matmul(z, z, transpose_b=True) / TEMPERATURE mask = tf.linalg.tensor_diag(tf.zeros(2*BATCH_SIZE)) + tf.linalg.tensor_diag(tf.ones(2*BATCH_SIZE), k=BATCH_SIZE) + tf.linalg.tensor_diag(tf.ones(2*BATCH_SIZE), k=-BATCH_SIZE) sim_max = tf.reduce_max(sim, axis=1, keepdims=True) logits = sim - sim_max exp = tf.exp(logits) * (1 - mask) positives = tf.exp(tf.linalg.diag_part(sim, k=BATCH_SIZE)) loss = -tf.math.log(positives / tf.reduce_sum(exp, axis=1)) return tf.reduce_mean(loss) # build model def build_simclr_model(): base = keras.applications.ResNet50(include_top=False, weights=None, input_shape=(IMG_SIZE,IMG_SIZE,3), pooling='avg') inp = layers.Input(shape=(IMG_SIZE,IMG_SIZE,3)) h = base(inp) z = layers.Dense(PROJ_DIM, activation='relu')(h) z = layers.Dense(PROJ_DIM)(z) return keras.Model(inp, z, name='simclr_encoder') # training loops @tf.function def pretrain_step(model, opt, x1, x2): with tf.GradientTape() as tape: z1, z2 = model(x1, training=True), model(x2, training=True) loss = nt_xent_loss(z1, z2) grads = tape.gradient(loss, model.trainable_variables) opt.apply_gradients(zip(grads, model.trainable_variables)) return loss @tf.function def train_linear_step(encoder, clf, opt, images, labels): h = encoder(images, training=False) with tf.GradientTape() as tape: logits = clf(h, training=True) loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)(labels, logits) grads = tape.gradient(loss, clf.trainable_variables) opt.apply_gradients(zip(grads, clf.trainable_variables)) return loss # main def main(): ds_pre, ds_fine, ds_test = prepare_stl10() simclr_model = build_simclr_model() optimizer = keras.optimizers.Adam(LEARNING_RATE) for epoch in range(EPOCHS_PRETRAIN): for x1, x2 in ds_pre: loss = pretrain_step(simclr_model, optimizer, x1, x2) print(f'Epoch {epoch+1}/{EPOCHS_PRETRAIN}, contrastive loss: {loss:.4f}') simclr_model.trainable = False clf = keras.Sequential() opt2 = keras.optimizers.Adam(LEARNING_RATE) for epoch in range(EPOCHS_FINETUNE): for imgs, labs in ds_fine: l = train_linear_step(simclr_model, clf, opt2, imgs, labs) print(f'Linear Epoch {epoch+1}/{EPOCHS_FINETUNE}, loss: {l:.4f}') acc = keras.metrics.SparseCategoricalAccuracy() for imgs, labs in ds_test: preds = clf(simclr_model(imgs, training=False)) acc.update_state(labs, preds) print('Test Accuracy:', acc.result().numpy()) if __name__=='__main__': main() …

Here’s a truly advanced Keras project—this time implementing self-supervised contrastive learning (SimCLR) on the CIFAR-10 dataset. It’s a big leap from typical CNN-based classification. The full code dominates the answer, with brief framing and notes following. 🧠 Project Title ai-ml-ds-SrmZNuoOhMk – Self-Supervised Visual Representation Learning with SimCLRFilename: simclr_self_supervised_cifar10.py # simclr_self_supervised_cifar10.py import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers import numpy as np import datetime import os # 1.

Here’s a highly advanced Keras project that’s significantly different—and more advanced—than typical supervised tasks: we’re diving into self-supervised contrastive learning using SimCLR. It’s a full implementation with encoder, projection head, NT‑Xent loss, and linear probing. Project Title ai-ml-ds-SrmZNuoOhMkSelf‑Supervised Contrastive Learning with Keras (SimCLR)simclr_contrastive_learning.py Short Description:Leverage unlabeled data to learn powerful image embeddings via contrastive learning (SimCLR), then evaluate quality via linear probing on downstream classification.

Here’s an advanced Keras project that builds on self‑supervised contrastive learning—going beyond conventional supervised tasks. It will challenge your expertise and stretch your skills. 🧠 Project Title ai-ml-ds‑XjKLmPqRZb – “Self‑Supervised Contrastive Representation Learning with SimCLR in Keras”Filename: simclr_contrastive_learning_keras.py 🗂 Description Implement the SimCLR framework to learn rich image embeddings without labels. The pipeline: Download an image dataset (e.g. CIFAR‑10 or STL‑10 via Keras).

Here's a challenging and advanced Keras project aimed at seasoned ML/AI engineers: a multimodal contrastive learning model inspired by CLIP, using the Hugging Face's Flickr8k dataset (image-caption pairs). This is significantly more advanced than typical classification or autoencoder projects. Project Title ai-ml-ds-XzYk9QvLpRt — Multimodal Contrastive Learning with Keras (CLIP‑style)Filename: multimodal_contrastive_clip_style.py 🔍 Short Description Train an image encoder and text encoder jointly with a contrastive (InfoNCE) loss so matching captions and images get close in a shared embedding space, while mismatches are pushed apart.