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Project Title: automated hyperparameter tuning for deep CNNs using Keras Tuner on CIFAR‑100 - Keras-Exercise-107

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…

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Project Title: automated hyperparameter tuning for a convolutional neural network on CIFAR-100, using Keras Tuner’s Hyperband and Bayesian Optimization - Keras-Exercise-106

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…

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Project Title: Build a convolutional neural network (CNN) for CIFAR‑100 image classification, using Keras Tuner (Hyperband + Bayesian) to automatically optimize architecture - Keras-Exercise-105

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…

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Project Title: leveraging Keras Tuner for hyperparameter optimization on a ResNet‑based CIFAR‑10 classifier - Keras-Exercise-104

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…

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Project Title: Hyperparameter‑Optimized Deep Residual CNN for CIFAR‑10 - Keras-Exercise-103

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…

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Project Title: Hyperparameter-Optimized Deep CNN with Ensemble via Keras Tuner - Keras-Exercise-102

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…

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Project Title: Automate design of optimal convolutional architectures using Keras Tuner and modular NAS blocks - Keras-Exercise-101

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…

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Project Title: Neural Architecture Search (NAS) for object detection, utilizing EfficientDet-style compound scaling - Keras-Exercise-100

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…

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Project Title: advanced Keras + Spektral project leveraging graph neural networks for multivariate time-series forecasting on spatial-temporal graphs - Keras-Exercise-099

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…

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Project Title: advanced Keras + Spektral project using temporal Graph Neural Networks for multivariate time‑series forecasting - Keras-Exercise-098

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…

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Project Title: advanced Graph Neural Network project using Keras + Spektral - Keras-Exercise-097

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…

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Project Title: using Graph Neural Networks (GNNs) via the Spektral - Keras-Exercise-096

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:…

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Project Title: advanced Keras project using graph neural networks (GNN) - Keras-Exercise-095

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…

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Project Title: advanced Keras project leveraging Graph Attention Networks (GATs) - Keras-Exercise-094

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:…

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Project Title: advanced Graph Attention Network (GAT) project using Keras - Keras-Exercise-093

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…

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Project Title: Graph Attention Network for Citation Node Classification - Keras-Exercise-092

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…

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Project Title: VAE with Spatial-Attention and Label-Controlled Generation - Keras-Exercise-091

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…