Deep learning with Python
Impact in
- Authors
- François Chollet
- Journal
- CERN Document Server (European Organization for Nuclear Research)
In The Last Decade
doi.org/w1086873 →Countries where authors are citing Deep learning with Python
This map shows the geographic impact of Deep learning with Python. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Deep learning with Python with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deep learning with Python more than expected).
Fields of papers citing Deep learning with Python
This network shows the impact of Deep learning with Python. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Deep learning with Python.
About Deep learning with Python
This paper, published in 2018, received 518 indexed citations . Written by François Chollet. It is primarily cited by scholars working on Artificial Intelligence (120 citations), Computer Vision and Pattern Recognition (78 citations), Electrical and Electronic Engineering (56 citations), Mechanical Engineering (40 citations) and Radiology, Nuclear Medicine and Imaging (37 citations). Published in CERN Document Server (European Organization for Nuclear Research).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/w1086873.