Deep Learning with PyTorch
Build, train, and debug neural networks in PyTorch: autograd, training loops, regularization, transfer learning, and the practices that make results reproducible.
Course Overview
PyTorch is the default framework for deep learning research and, increasingly, for production. This course teaches it the way it is actually used: writing explicit training loops, understanding what autograd does when you call backward, and being able to diagnose a model that trains but does not learn.
The emphasis throughout is on debugging and judgment rather than on stacking layers. Most deep learning failures are not exotic, they are silent: a learning rate off by an order of magnitude, a data loader shuffling labels away from inputs, normalization applied inconsistently between training and inference, or a validation set contaminated by the training data. You will hit these deliberately in labs and learn the diagnostics that identify each one.
Later sessions cover the techniques that carry the most practical weight: transfer learning from pretrained backbones, which is how nearly all applied computer vision work actually gets done, plus mixed-precision training, gradient accumulation for constrained memory, learning rate scheduling, and the experiment tracking that makes a result reproducible weeks later.
Duration: 3 days|Delivery: onsite, virtual, or hybrid
Prerequisites
- Strong Python including classes and NumPy
- Basic machine learning concepts: train/validation split, overfitting, loss functions
- Comfort with linear algebra notation (matrices, dot products)
- No prior PyTorch experience required
Who Should Attend
- Software engineers moving into deep learning work
- Data scientists switching to PyTorch from scikit-learn or TensorFlow
- ML engineers who need to debug and modify existing PyTorch models rather than only run them
- Teams standardizing on PyTorch who want consistent practices
Course Outline
- 1Tensors, devices, and memory: the mental model that prevents most beginner errors
- 2Autograd: computation graphs, backward, and reading a gradient flow
- 3Building models with nn.Module and composing reusable components
- 4Datasets, DataLoaders, and collation, including the bugs that silently corrupt batches
- 5Writing a training loop explicitly: forward, loss, backward, step, and zero_grad
- 6Optimizers compared: SGD with momentum, Adam, AdamW, and when each is appropriate
- 7Diagnosing training failures: loss not decreasing, exploding gradients, and overfitting
- 8Regularization: dropout, weight decay, early stopping, and data augmentation
- 9Convolutional networks and transfer learning from pretrained backbones
- 10Recurrent and attention-based models for sequence data
- 11Mixed precision, gradient accumulation, and gradient checkpointing
- 12Learning rate schedules and finding a workable rate quickly
- 13Reproducibility: seeding, determinism, checkpointing, and experiment tracking
- 14Exporting for inference: TorchScript, ONNX, and quantization
Learning Outcomes
- Write and debug PyTorch training loops without relying on a high-level wrapper
- Read gradient behavior to diagnose why a model is not learning
- Apply transfer learning to reach strong results on small datasets
- Train larger models within fixed GPU memory using mixed precision and accumulation
- Set up experiment tracking so results are reproducible and comparable
- Recognize and fix the data pipeline bugs that quietly ruin training runs
- Export a trained model for efficient inference
What You Will Build
- An image classifier built by transfer learning from a pretrained backbone
- A debugging notebook covering the common training failure modes and their signatures
- A reproducible training setup with seeding, checkpointing, and tracked experiments
- An exported model with measured inference latency
Frequently Asked Questions
- PyTorch or TensorFlow?
- PyTorch has become the default in research and is now dominant in production too, which matters mainly because it determines what pretrained models, tutorials, and hiring pool you have access to. If your organization already runs TensorFlow in production, the concepts here transfer directly; the framework-specific syntax is the smaller part of the course.
- Do we need our own GPUs?
- No. Labs run in a browser-based cloud environment with GPU access provided, so there is nothing to install and no hardware to procure. The course also covers what happens on CPU and how to size GPU hardware for your own workloads.
- Is this course about LLMs?
- Not primarily. It teaches the PyTorch foundations that LLM work rests on. If you specifically want transformer architecture and language models, take NLP with Transformers; if you want to adapt an existing LLM, take LLM Fine-Tuning. This course is the groundwork under both.
- How much math is required?
- Comfort with matrix notation and derivatives conceptually. The course does not derive backpropagation formally, but it does explain what autograd computes and why gradient behavior tells you what is wrong with your model.
- Can we use our own datasets in the labs?
- Yes, and for private cohorts we encourage it. Working through data loading and augmentation on your real data surfaces the problems you will actually face far better than a clean benchmark dataset does.
Related Courses
NLP with Transformers
Understand transformer architecture properly, then apply it: tokenization, attention, embeddings, and fine-tuning encoder models for classification and extraction.
LLM Fine-Tuning
Decide when fine-tuning is warranted, then do it properly: dataset construction, LoRA and QLoRA, hyperparameters, evaluation, and serving the result.
ML Model Deployment
Serve models reliably: inference APIs, batching, autoscaling, GPU utilization, model optimization, and hitting a latency target without overspending.
MLOps for Production AI
Operationalize machine learning: reproducible pipelines, model registries, automated retraining, drift detection, and the practices that keep models correct after launch.
Ready to Get Started?
Contact us to schedule training for your team or inquire about upcoming sessions.