. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1129 3.2.3 Top-level conversions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1135 3.2.4 Top-level in November, the monthly period of December 2011 is actually in the 2012 A-NOV period. Period conversions with anchored frequencies are particularly useful for working with various quarterly data common In [452]: ts.tz Out[452]: Warning: Be wary of conversions between libraries. For some time zones, pytz and dateutil have different definitions of the zone

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1129 3.2.3 Top-level conversions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1135 3.2.4 Top-level in November, the monthly period of December 2011 is actually in the 2012 A-NOV period. Period conversions with anchored frequencies are particularly useful for working with various quarterly data common In [452]: ts.tz Out[452]: Warning: Be wary of conversions between libraries. For some time zones, pytz and dateutil have different definitions of the zone

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1085 3.2.3 Top-level conversions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1090 3.2.4 Top-level in November, the monthly period of December 2011 is actually in the 2012 A-NOV period. Period conversions with anchored frequencies are particularly useful for working with various quarterly data common In [452]: ts.tz Out[452]: Warning: Be wary of conversions between libraries. For some time zones, pytz and dateutil have different definitions of the zone

in November, the monthly period of December 2011 is actually in the 2012 A-NOV period. Period conversions with anchored frequencies are particularly useful for working with various quarterly data common In [455]: ts.tz Out[455]: Warning: Be wary of conversions between libraries. For some time zones, pytz and dateutil have different definitions of the zone to_list() Out[118]: [Timestamp('2012-12-31 00:00:00'), NaT, Timestamp('2013-01-01 00:00:00')] Conversions Similarly to frequency conversion on a Series above, you can convert these indices to yield another

in November, the monthly period of December 2011 is actually in the 2012 A-NOV period. Period conversions with anchored frequencies are particularly useful for working with various quarterly data common In [455]: ts.tz Out[455]: Warning: Be wary of conversions between libraries. For some time zones, pytz and dateutil have different definitions of the zone to_list() Out[118]: [Timestamp('2012-12-31 00:00:00'), NaT, Timestamp('2013-01-01 00:00:00')] Conversions Similarly to frequency conversion on a Series above, you can convert these indices to yield another

in November, the monthly period of December 2011 is actually in the 2012 A-NOV period. Period conversions with anchored frequencies are particularly useful for working with various quarterly data common In [455]: ts.tz Out[455]: Warning: Be wary of conversions between libraries. For some time zones, pytz and dateutil have different definitions of the zone to_list() Out[118]: [Timestamp('2012-12-31 00:00:00'), NaT, Timestamp('2013-01-01 00:00:00')] Conversions Similarly to frequency conversion on a Series above, you can convert these indices to yield another

techniques like momentum to help the optimizer escape the local minimas. Sharpness-Aware Minimization (SAM)22 is one such technique. It suggests that steep valleys in the objective function might just be the local minima (right). Source: Forret et al. SAM encourages the optimizer to find a minima where the neighborhood of that minima has low loss too, by using the SAM loss. If we denote the weights of a model model by , and its loss function on a training dataset as . Then the SAM objective function is defined as: 23 https://en.wikipedia.org/wiki/Occam%27s_razor 22 Foret, Pierre, et al. "Sharpness-Aware Minimization

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1000 3.2.3 Top-level conversions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1006 3.2.4 Top-level in November, the monthly period of December 2011 is actually in the 2012 A-NOV period. Period conversions with anchored frequencies are particularly useful for working with various quarterly data common In [446]: ts.tz Out[446]: Warning: Be wary of conversions between libraries. For some time zones, pytz and dateutil have different definitions of the zone

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1000 3.2.3 Top-level conversions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1006 3.2.4 Top-level in November, the monthly period of December 2011 is actually in the 2012 A-NOV period. Period conversions with anchored frequencies are particularly useful for working with various quarterly data common In [446]: ts.tz Out[446]: Warning: Be wary of conversions between libraries. For some time zones, pytz and dateutil have different definitions of the zone

Improved error handling during item assignment in pd.eval . . . . . . . . . . . . . 21 1.2.2.9 Dtype Conversions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 1.2.2.10 MultiIndex Constructor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 912 20.6.3 Conversions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 913 20.7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1286 34.2.3 Top-level conversions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1286 xxvi 34.2.3