  Deep learning continues to re-shape numerous fields, from natural language
processing and imaging to data analytics and recommendation systems. This
report studies two research papers that represent recent progress on deep
learning from two largely different aspects: The first paper applied the
transformer networks, which are typically used in language models, to improve
the quality of synthetic aperture radar image by effectively reducing the
speckle noise. The second paper presents an in-storage computing design
solution to enable cost-efficient and high-performance implementations of deep
learning recommendation systems. In addition to summarizing each paper in terms
of motivation, key ideas and techniques, and evaluation results, this report
also presents thoughts and discussions about possible future research
directions. By carrying out in-depth study on these two representative papers
and related references, this doctoral candidate has developed better
understanding on the far-reaching impact and efficient implementation of deep
learning models.
