IBM Data Science Practice Test

Deep learning systems are known for their complexity and capability to process large amounts of data. One significant limitation of these systems is their requirement for substantial computational power. This is due to their architecture, which typically consists of multiple layers of neurons, each requiring extensive calculations during both training and inference phases. Furthermore, deep learning models often involve processing high-dimensional data, making them resource-intensive. The reliance on powerful hardware, such as GPUs or TPUs, is essential to efficiently train these models within a reasonable timeframe. This can be cost-prohibitive and may limit accessibility for individuals or organizations without the necessary computational resources. In contrast, the other options do not accurately describe the limitations of deep learning. Interpretability is generally seen as a challenge in deep learning, as their complex structures can make understanding decision processes difficult. Moreover, deep learning typically requires more training data compared to traditional algorithms, which can learn effectively from a smaller number of examples. Lastly, the development of deep learning models can be quite intricate due to the tuning of hyperparameters, selection of architectures, and the necessity of large datasets, making them generally more complex to develop compared to simpler machine learning approaches.