One of the biggest challenges is obtaining a large amount of high-quality labeled data for training deep learning models. Deep learning algorithms require vast amounts of data, often in the range of millions or billions of samples, in order to learn meaningful patterns and generalize well to new examples. Collecting and labeling large datasets can be an extremely time-consuming and expensive process, sometimes requiring human experts and annotators. The quality and completeness of the data labels is also important. Noise or ambiguity in the labels can negatively impact a model’s performance.

Securing adequate computing resources for training complex deep learning models can pose difficulties. Training large state-of-the-art models from scratch requires high-performance GPUs or GPU clusters to achieve reasonable training times. This level of hardware can be costly, and may not always be accessible to students or those without industry backing. Alternatives like cloud-based GPU instances or smaller models/datasets have to be considered. Organizing and managing distributed training across multiple machines also introduces technical challenges.

Choosing the right deep learning architecture and techniques for the given problem/domain is not always straightforward. There are many different model types (CNNs, RNNs, Transformers etc.), optimization algorithms, regularization methods and hyperparameters to experiment with. Picking the most suitable approach requires a thorough understanding of the problem as well as deep learning best practices. Significant trial-and-error may be needed during development. Transfer learning from pretrained models helps but requires domain expertise.

Overfitting, where models perform very well on the training data but fail to generalize, is a common issue due to limited data. Regularization methods and techniques like dropout, batch normalization, early stopping, data augmentation must be carefully applied and tuned. Detecting and addressing overfitting risks requiring analysis of validation/test metrics vs training metrics over multiple experiments.

Evaluating and interpreting deep learning models can be non-trivial, especially for complex tasks. Traditional machine learning metrics like accuracy may not fully capture performance. Domain-specific evaluation protocols have to be followed. Understanding feature representations and decision boundaries learned by the models helps debugging but is challenging. Bias and fairness issues also require attention depending on the application domain.

Integrating deep learning models into applications and production environments involves additional non-technical challenges. Aspects like model deployment, data/security integration, ensuring responsiveness under load, continuous monitoring, documentation and versioning, assisting non-technical users require soft skills and a software engineering mindset on top of ML expertise. Agreeing on success criteria with stakeholders and reporting results is another task.

Documentation of the entire project from data collection to model architecture to training process to evaluation takes meticulous effort. This not only helps future work but is essential in capstone reports/theses to gain appropriate credit. A clear articulation of limitations, assumptions, future work is needed along with code/result reproducibility. Adhering to research standards of ethical AI and data privacy principles is also important.

While deep learning libraries and frameworks help development, they require proficiency which takes time to gain. Troubleshooting platform/library specific bugs introduces delays. Software engineering best practices around modularity, testing, configuration management become critical as projects grow in scope and complexity. Adhering to strict schedules in academic capstones with the above technical challenges can be stressful. Deep learning projects involve an interdisciplinary skillset beyond conventional disciplines.

Deep learning capstone projects, while providing valuable hands-on experience, can pose significant challenges in areas like data acquisition and labeling, computing resource requirements, model architecture selection, overfitting avoidance, performance evaluation, productionizing models, software engineering practices, documentation and communication of results while following research standards and schedules. Careful planning, experimentation, and holistic consideration of non-technical aspects is needed to successfully complete such ambitious deep learning projects.