The introduction to deep learning course offered by CMU has sparked discussion among students regarding its curriculum focus and complexity. While participants find the assignments and homework valuable, there are concerns about the ambitious nature of the course material. Particularly, students highlight that newcomers might struggle with foundational concepts such as backpropagation, yet be expected to understand advanced topics like diffusion models by the end. The curriculum appears to emphasize convolutional neural networks (CNNs) heavily, with four dedicated lectures, raising questions about balance. Moreover, commentators noted the absence of embeddings as a topic despite their significance in industry applications, suggesting the importance of teaching the creation and retrieval of high-quality embeddings. They recommend inclusion of multimodal learning examples, such as CLIP, to enrich the learning experience. Additionally, questions surrounding the course’s delivery method and access (open vs. closed source) remain unanswered, indicating a need for more transparency from the course organizers.