PyTorch Projects
Projects that take you from tensor basics to training and deploying custom neural networks — covering vision, NLP, and production deployment.
Beginner Projects
1. XOR Neural Network
Train a 2-layer MLP to solve XOR (not linearly separable). Manually write the training loop, plot loss over epochs, and visualize the decision boundary. Understand why a linear model fails.
What you’ll practice: Tensor ops, manual training loop, BCELoss, optimizer step
2. MNIST Digit Classifier
Build a simple fully-connected network for MNIST. Implement training and validation loops from scratch. Track accuracy per epoch and plot a confusion matrix on the test set.
What you’ll practice: DataLoader, CrossEntropyLoss, training loop, evaluation
3. Linear Regression from Scratch
Implement linear regression as a nn.Module. Compare analytical solution (np.linalg.lstsq) vs. gradient descent. Plot loss curves for different learning rates.
What you’ll practice: nn.Module, MSELoss, SGD, learning rate sensitivity
4. Image Denoiser (Autoencoder)
Train a simple encoder-decoder to remove Gaussian noise from images. Use MSELoss between clean and reconstructed images. Visualize noisy input vs. denoised output.
What you’ll practice: Autoencoder architecture, reconstruction loss, image visualization
5. Sentiment Classifier (Bag of Words)
Build a text classifier using a simple embedding bag + linear layer (no RNN). Train on SST-2. Show how this simple model achieves surprisingly good results.
What you’ll practice: nn.EmbeddingBag, text tokenization, binary cross-entropy
6. CIFAR-10 CNN
Build a CNN with 3 Conv blocks (Conv2d + BatchNorm + ReLU + MaxPool) and a classifier head. Track training/validation accuracy and identify which classes are hardest.
What you’ll practice: nn.Conv2d, BatchNorm2d, MaxPool2d, multi-class training
7. Transfer Learning with ResNet-18
Fine-tune a pre-trained ResNet-18 on a custom 5-class image dataset. Freeze the backbone, train only the classifier head. Compare vs. training from scratch.
What you’ll practice: torchvision.models, param.requires_grad, fine-tuning loop
8. Time Series Forecaster (Simple LSTM)
Predict the next N values of a sine wave (or stock price) using a single-layer LSTM. Implement data windowing, train the model, and plot predictions vs. actuals.
What you’ll practice: nn.LSTM, sequence data formatting, regression with LSTM
9. Variational Autoencoder
Implement a VAE on MNIST: encoder outputs μ and σ, reparameterization trick, decoder, ELBO loss (reconstruction + KL divergence). Generate new digit samples from the latent space.
What you’ll practice: Reparameterization trick, ELBO loss, latent space sampling
10. Gradient Descent Visualizer
Build an interactive visualization of gradient descent on 2D loss landscapes (Rosenbrock, saddle points). Show SGD, Momentum, Adam, and RMSProp trajectories. Understand why Adam is usually better.
What you’ll practice: Custom optimizer loops, tensor autograd, optimization landscapes
Intermediate Projects
1. Object Detection from Scratch (Tiny YOLO)
Implement a simplified single-class object detector: grid-based prediction, anchor boxes, IoU computation, confidence + bounding box losses. Train on a synthetic dataset of colored shapes.
What you’ll practice: Custom loss functions, IoU, bounding box regression, detection heads
2. Neural Machine Translation
Implement a seq2seq model with attention for English-to-French translation. Implement Bahdanau attention from scratch. Compute BLEU score and visualize attention heatmaps.
What you’ll practice: Encoder-decoder, attention mechanism, teacher forcing, BLEU
3. Contrastive Learning (SimCLR)
Implement SimCLR self-supervised learning: random augmentations, projection head, NT-Xent loss. Train on CIFAR-10 without labels, then evaluate the representations with a linear probe.
What you’ll practice: Contrastive loss, augmentation pipelines, self-supervised evaluation
4. Graph Neural Network
Implement a basic GCN (Graph Convolutional Network) from scratch: adjacency normalization, message passing, node classification. Train on Cora citation network.
What you’ll practice: Graph data structures, message passing, PyG or custom GCN
5. Diffusion Model (DDPM)
Implement a simple denoising diffusion probabilistic model on MNIST. Forward process adds noise over T steps, reverse process denoises. Generate new digit samples.
What you’ll practice: Noise scheduling, U-Net architecture, reverse diffusion sampling
6. Transformer Language Model
Build a character-level or BPE-level transformer language model. Implement causal masking, sinusoidal positional encoding, and temperature sampling. Train on a text corpus.
What you’ll practice: Causal attention, autoregressive training, text generation sampling
7. Multi-Task Learning
Train a model simultaneously on two related tasks (e.g., sentiment + subjectivity classification). Compare task-specific models vs. shared representation. Analyze task interference.
What you’ll practice: Multi-task loss balancing, shared encoder, task-specific heads
8. Neural Style Transfer
Implement Gatys et al. style transfer: extract content features from VGG19, compute Gram matrices for style loss, optimize input image pixels via gradient descent.
What you’ll practice: Feature extraction hooks, Gram matrices, image optimization
9. Reinforcement Learning (Deep Q-Network)
Implement DQN for CartPole or LunarLander: experience replay buffer, target network, epsilon-greedy exploration, TD learning. Plot reward curves and episode length over training.
What you’ll practice: Experience replay, target network, TD loss, gym integration
10. Knowledge Distillation
Train a large “teacher” model, then distill its knowledge into a small “student” model using soft targets (temperature-scaled logits). Compare student performance vs. training student from scratch.
What you’ll practice: Soft target distillation loss, temperature scaling, model compression
Advanced Projects
1. Custom CUDA Kernel
Write a custom CUDA kernel for a non-standard activation function or attention variant using torch.utils.cpp_extension. Benchmark against the pure-Python equivalent.
What you’ll practice: C++/CUDA extensions, PyTorch C++ API, performance profiling
2. Efficient Transformer (Flash Attention-like)
Implement memory-efficient attention using chunked computation and gradient checkpointing. Compare memory usage and throughput against standard scaled dot-product attention.
What you’ll practice: Memory optimization, gradient checkpointing, attention variants
3. Model Pruning Pipeline
Implement structured and unstructured pruning on a ResNet: magnitude-based weight pruning, channel pruning, fine-tuning after pruning, and accuracy-vs-sparsity tradeoff curves.
What you’ll practice: torch.nn.utils.prune, sparsity measurement, re-training schedule
4. Distributed Training Benchmark
Implement and benchmark DataParallel vs. DistributedDataParallel (DDP) across multiple GPUs (or simulated with CPU). Measure throughput scaling, gradient sync overhead, and memory per device.
What you’ll practice: DDP, DistributedSampler, NCCL backend, scaling efficiency
5. Neural Architecture Search (NAS, simplified)
Implement a simple DARTS-like differentiable NAS: parameterize operation choices with softmax weights, jointly train weights and architecture parameters, discretize the final architecture.
What you’ll practice: Mixed-ops, bi-level optimization, architecture search
Portfolio Projects
1. Production Vision API
Build a complete computer vision service: fine-tuned EfficientNet on a custom dataset, ONNX export, TorchServe deployment, REST API with batch support, latency benchmarking, and a Gradio demo UI.
Tech stack: PyTorch, torchvision, ONNX, TorchServe, FastAPI, Gradio
Demonstrates: Full ML deployment lifecycle, production thinking, demo skills
2. Multimodal Search Engine
Build an image-text search engine using CLIP: encode 10k images and captions, store embeddings in a vector database (FAISS), implement image-to-text and text-to-image search with re-ranking.
Tech stack: PyTorch, HuggingFace CLIP, FAISS, FastAPI
Demonstrates: Multimodal models, vector search, end-to-end system design
3. LLM Fine-Tuning Pipeline
Fine-tune a GPT-2 or LLaMA-2 model on a domain-specific corpus using LoRA (PEFT). Implement gradient accumulation, mixed precision, checkpoint saving, and evaluation with perplexity.
Tech stack: PyTorch, HuggingFace transformers, PEFT, accelerate
Demonstrates: LLM fine-tuning expertise, parameter-efficient training, generation quality
4. Real-Time Video Classifier
Build a real-time webcam activity classifier: stream video frames, classify every N frames with a fine-tuned MobileNetV3, buffer predictions for temporal smoothing, display overlaid results.
Tech stack: PyTorch, torchvision, OpenCV, threading
Demonstrates: Real-time inference, edge deployment, video understanding
5. Neural Architecture from a Paper
Reproduce a published neural architecture from a paper (e.g., Vision Transformer, Mamba, or RetNet). Implement from the paper’s equations, verify on the paper’s benchmark, and write a blog-style explanation.
Tech stack: PyTorch (pure implementation preferred)
Demonstrates: Research comprehension, implementation rigor, communication skills