| 000 | 01250 a2200229 4500 | ||
|---|---|---|---|
| 003 | OSt | ||
| 020 | _a9789811285325 | ||
| 040 | _cIIT Kanpur | ||
| 041 | _aeng | ||
| 082 |
_a530.0285 _bD36 |
||
| 245 |
_aDeep learning for physics research _cMartin Erdmann ...[et. al.] |
||
| 260 |
_bWorld Scientific Publishing _c2021 _aChennai |
||
| 300 | _axi, 327p | ||
| 520 | _a"A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research. This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded"-- | ||
| 650 | _aPhysics | ||
| 650 | _aMachine learning | ||
| 700 | _aErdmann, Martin | ||
| 700 | _aGlombitza, Jonas | ||
| 700 | _aKasieczka, Gregor | ||
| 700 | _aKlemradt, Uwe | ||
| 942 | _cBK | ||
| 999 |
_c567290 _d567290 |
||