TensorFlow Projects
Projects covering TensorFlow and Keras from basic neural networks to production serving — building real models that solve real problems.
Beginner Projects
1. House Price Predictor
Build a regression model for the Boston/California housing dataset using a 3-layer Dense network. Add batch normalization and dropout. Compare against sklearn’s LinearRegression baseline.
What you’ll practice: Sequential API, Dense layers, BatchNormalization, MSE loss
2. Fashion MNIST Classifier
Classify 10 clothing categories using a CNN. Implement early stopping via callbacks, save the best model checkpoint, and display misclassified examples per class.
What you’ll practice: Conv2D, ModelCheckpoint, EarlyStopping, classification report
3. Binary Sentiment Classifier (IMDb)
Build a text sentiment classifier using Embedding + GlobalAveragePooling1D + Dense. Use TextVectorization layer. Compare 1D-CNN vs. simple dense approach.
What you’ll practice: TextVectorization, Embedding, GlobalAveragePooling1D, binary cross-entropy
4. Image Autoencoder
Build a convolutional autoencoder to compress and reconstruct CIFAR-10 images. Visualize the bottleneck representation with t-SNE. Use the encoder as a feature extractor.
What you’ll practice: Encoder-decoder architecture, Functional API, reconstruction loss
5. Time Series Anomaly Detector
Train an LSTM autoencoder on normal server metric data. Flag time windows where reconstruction error exceeds a threshold as anomalies. Evaluate with known anomaly labels.
What you’ll practice: LSTM autoencoder, reconstruction error thresholding, time series evaluation
6. Multi-Output Model
Build a model that simultaneously predicts house price (regression) and price tier (classification) from the same features. Use the Functional API with two output heads and weighted losses.
What you’ll practice: Functional API, multiple outputs, custom loss weights
7. Transfer Learning Classifier
Fine-tune MobileNetV2 on a 5-class flower dataset (tf_flowers). Freeze base, train classifier, then unfreeze top layers for full fine-tuning. Plot learning curves for each phase.
What you’ll practice: tf.keras.applications, trainable=False, fine-tuning phases
8. Text Generation with LSTM
Train a character-level LSTM on a text corpus (Shakespeare, code, etc.). Implement temperature sampling. Generate text samples at different temperatures to see creativity vs. coherence tradeoff.
What you’ll practice: Stateful LSTM, character tokenization, temperature sampling
9. Regression with Uncertainty (Dropout as Bayesian Approximation)
Use Monte Carlo Dropout to estimate prediction uncertainty: keep dropout active during inference, run N forward passes, report mean and std. Show how uncertainty increases for out-of-distribution inputs.
What you’ll practice: MC Dropout, inference uncertainty, out-of-distribution detection
10. Custom Training Loop
Reimplement a MNIST classifier using a manual training loop with tf.GradientTape. Add custom per-step metrics and compare performance to model.fit(). Understand what model.fit() abstracts away.
What you’ll practice: tf.GradientTape, manual gradient application, tf.function
Intermediate Projects
1. Object Detection with TF Object Detection API
Fine-tune SSD MobileNet v2 on a custom dataset (e.g., 5 object classes labeled with LabelImg). Export to TFLite for mobile deployment. Compute mAP on a held-out test set.
What you’ll practice: TF Object Detection API, TFRecord format, mAP evaluation, TFLite
2. Image Segmentation (U-Net)
Implement U-Net for binary semantic segmentation. Train on the Oxford Pets dataset (pet vs. background). Report IoU and Dice coefficient. Visualize predicted masks.
What you’ll practice: U-Net architecture, skip connections, segmentation metrics (IoU, Dice)
3. Generative Adversarial Network
Implement DCGAN on CelebA face dataset: discriminator and generator as Keras Models, alternating training steps, progressive image quality monitoring. Understand mode collapse and how to detect it.
What you’ll practice: GAN training loop, tf.GradientTape for two models, generator evaluation
4. BERT Fine-Tuning for NER
Fine-tune BERT (via HuggingFace TF backend) for Named Entity Recognition on CoNLL-2003. Handle token-subword alignment, compute entity-level F1 (not token-level).
What you’ll practice: Token classification, subword alignment, entity-level evaluation, TFAutoModel
5. Recommendation System (Neural Collaborative Filtering)
Build a neural collaborative filtering model: user and item embedding layers, dot product + MLP head, binary cross-entropy loss on implicit feedback. Evaluate with NDCG@10.
What you’ll practice: Embedding layers, implicit feedback training, ranking evaluation
6. Multi-Modal Classifier
Build a model that combines image features (CNN) and text features (LSTM) to classify product listings. Use the Functional API to concatenate both feature streams before the classifier.
What you’ll practice: Multi-input Functional API, concatenation, multi-modal fusion
7. Data Augmentation Pipeline with tf.data
Build a high-performance training pipeline: random crop, flip, color jitter, mixup augmentation, all inside tf.data. Benchmark with/without augmentation, measure throughput (images/sec).
What you’ll practice: tf.data map/cache/prefetch, custom augmentation ops, pipeline profiling
8. Quantization-Aware Training
Apply quantization-aware training (QAT) using TFLite Model Optimization Toolkit on MobileNetV2. Compare float32 vs int8 accuracy, model size, and inference latency on target hardware.
What you’ll practice: QAT, TFLite optimization, accuracy-vs-speed tradeoff
9. Custom Layer and Loss Function
Build a custom Attention layer (Bahdanau) and a custom Focal Loss for imbalanced classification, both as proper Keras classes. Verify gradients flow correctly and integrate into a standard pipeline.
What you’ll practice: tf.keras.layers.Layer, call(), get_config(), custom loss
10. Distributed Training with MirroredStrategy
Train a ResNet-50 on ImageNet (or a subset) using tf.distribute.MirroredStrategy across multiple GPUs. Measure linear scaling of throughput. Handle the gradient accumulation required for large batch sizes.
What you’ll practice: MirroredStrategy, distributed datasets, gradient accumulation
Advanced Projects
1. Production Model Serving with TF Serving + Kubernetes
Deploy a TensorFlow SavedModel to TF Serving via Docker, expose via REST and gRPC, implement blue-green deployment with Kubernetes, and set up health checks + autoscaling.
What you’ll practice: SavedModel format, TF Serving config, Kubernetes deployments, rolling updates
2. TFX ML Pipeline
Build a full TFX pipeline: ExampleGen, StatisticsGen, SchemaGen, Transform, Trainer, Evaluator, Pusher. Use tfrecord data, run locally and on Kubeflow, and trigger automated retraining on data drift.
What you’ll practice: TFX components, tf.Transform, automated ML pipelines, Kubeflow
3. Custom Optimizer (Lookahead + RAdam)
Implement the Lookahead and RAdam optimizers from scratch as tf.keras.optimizers.Optimizer subclasses. Benchmark convergence speed and final accuracy against Adam on CIFAR-10.
What you’ll practice: Custom optimizer protocol, variable management, optimizer wrapping
4. Neural ODE
Implement a Neural ODE (node) using tf.odeint: replace residual blocks with continuous-depth dynamics. Train on MNIST, compare parameters and memory vs. ResNet of similar accuracy.
What you’ll practice: ODE solvers, adjoint method, continuous-depth networks
5. Efficient Video Classification
Build an efficient video classifier using frame sampling + temporal 3D convolutions (or two-stream: spatial + optical flow). Optimize for real-time inference with TFLite.
What you’ll practice: 3D convolutions, Conv3D, temporal modeling, video data pipelines
Portfolio Projects
1. End-to-End Computer Vision Service
Fine-tune EfficientNetB4 on a custom dataset, deploy with TF Serving + FastAPI, implement A/B testing between two model versions, track prediction logs, and build a Streamlit monitoring dashboard.
Tech stack: TensorFlow, TF Serving, FastAPI, Streamlit, Docker
Demonstrates: Production deployment, model versioning, monitoring
2. Conversational AI with Transformer + BERT
Build a question-answering system: fine-tune BERT for extractive QA (SQuAD), build a retrieval layer for long documents, serve via FastAPI, and benchmark accuracy and latency vs. baseline approaches.
Tech stack: TensorFlow, HuggingFace, FAISS, FastAPI
Demonstrates: NLP pipeline, QA system design, production serving
3. Autonomous Driving Perception Module
Build a multi-task perception model: simultaneously predict lane markings (segmentation) and detect vehicles (object detection) from dashcam video. Export to TFLite for edge deployment.
Tech stack: TensorFlow, TFLite, OpenCV, custom data pipeline
Demonstrates: Multi-task learning, autonomous driving, edge deployment
4. Real-Time Speech Emotion Recognizer
Build an audio classification pipeline: MFCC feature extraction, CNN classifier, real-time audio streaming with PyAudio, TFLite model for low-latency inference, and a visualization dashboard.
Tech stack: TensorFlow, librosa, TFLite, PyAudio, Streamlit
Demonstrates: Audio ML, real-time processing, edge deployment
5. Personalized News Recommendation Engine
Build a BERT-based news encoder, train on click-through data (MIND dataset), implement a two-tower model (user + article), and serve recommendations via a REST API with sub-100ms latency.
Tech stack: TensorFlow, HuggingFace, FAISS, FastAPI, Redis
Demonstrates: Recommendation systems, neural retrieval, production scale