|
2 | 2 | "cells": [ |
3 | 3 | { |
4 | 4 | "cell_type": "markdown", |
5 | | - "id": "299b83d2", |
| 5 | + "id": "e627cfbd", |
6 | 6 | "metadata": {}, |
7 | 7 | "source": [ |
8 | 8 | "\n", |
|
19 | 19 | }, |
20 | 20 | { |
21 | 21 | "cell_type": "markdown", |
22 | | - "id": "d9b29395", |
| 22 | + "id": "3aee5e42", |
23 | 23 | "metadata": {}, |
24 | 24 | "source": [ |
25 | 25 | "# About These Lectures\n", |
|
31 | 31 | }, |
32 | 32 | { |
33 | 33 | "cell_type": "markdown", |
34 | | - "id": "ee7984a9", |
| 34 | + "id": "fbc33cd8", |
35 | 35 | "metadata": {}, |
36 | 36 | "source": [ |
37 | 37 | "## Overview\n", |
|
55 | 55 | }, |
56 | 56 | { |
57 | 57 | "cell_type": "markdown", |
58 | | - "id": "204c3495", |
| 58 | + "id": "b0a03c77", |
59 | 59 | "metadata": {}, |
60 | 60 | "source": [ |
61 | 61 | "### Can’t I Just Use LLMs?\n", |
|
79 | 79 | }, |
80 | 80 | { |
81 | 81 | "cell_type": "markdown", |
82 | | - "id": "0f1a1cf2", |
| 82 | + "id": "b976772f", |
83 | 83 | "metadata": {}, |
84 | 84 | "source": [ |
85 | 85 | "### Isn’t MATLAB Better?\n", |
|
102 | 102 | }, |
103 | 103 | { |
104 | 104 | "cell_type": "markdown", |
105 | | - "id": "1866c1c3", |
| 105 | + "id": "64446acd", |
106 | 106 | "metadata": {}, |
107 | 107 | "source": [ |
108 | 108 | "## Introducing Python\n", |
|
120 | 120 | }, |
121 | 121 | { |
122 | 122 | "cell_type": "markdown", |
123 | | - "id": "b19209f5", |
| 123 | + "id": "7e9549c4", |
124 | 124 | "metadata": {}, |
125 | 125 | "source": [ |
126 | 126 | "### Common Uses\n", |
|
152 | 152 | }, |
153 | 153 | { |
154 | 154 | "cell_type": "markdown", |
155 | | - "id": "d33d0109", |
| 155 | + "id": "18829d1c", |
156 | 156 | "metadata": {}, |
157 | 157 | "source": [ |
158 | 158 | "### Relative Popularity\n", |
|
175 | 175 | }, |
176 | 176 | { |
177 | 177 | "cell_type": "markdown", |
178 | | - "id": "e0dc0c80", |
| 178 | + "id": "5ccaa579", |
179 | 179 | "metadata": {}, |
180 | 180 | "source": [ |
181 | 181 | "### Features\n", |
|
193 | 193 | }, |
194 | 194 | { |
195 | 195 | "cell_type": "markdown", |
196 | | - "id": "0ddfe742", |
| 196 | + "id": "2c309971", |
197 | 197 | "metadata": {}, |
198 | 198 | "source": [ |
199 | 199 | "### Syntax and Design\n", |
|
211 | 211 | }, |
212 | 212 | { |
213 | 213 | "cell_type": "markdown", |
214 | | - "id": "76f9d4b7", |
| 214 | + "id": "aaf8bb3d", |
215 | 215 | "metadata": { |
216 | 216 | "hide-output": false |
217 | 217 | }, |
|
269 | 269 | }, |
270 | 270 | { |
271 | 271 | "cell_type": "markdown", |
272 | | - "id": "cd07e078", |
| 272 | + "id": "5cc142db", |
273 | 273 | "metadata": {}, |
274 | 274 | "source": [ |
275 | 275 | "This Java code opens an imaginary file called `data.csv` and computes the mean\n", |
|
283 | 283 | { |
284 | 284 | "cell_type": "code", |
285 | 285 | "execution_count": null, |
286 | | - "id": "e54e7f5a", |
| 286 | + "id": "1f4c6f0e", |
287 | 287 | "metadata": { |
288 | 288 | "hide-output": false |
289 | 289 | }, |
|
305 | 305 | }, |
306 | 306 | { |
307 | 307 | "cell_type": "markdown", |
308 | | - "id": "c570d66c", |
| 308 | + "id": "a5818c4f", |
309 | 309 | "metadata": {}, |
310 | 310 | "source": [ |
311 | 311 | "### The AI Connection\n", |
|
327 | 327 | }, |
328 | 328 | { |
329 | 329 | "cell_type": "markdown", |
330 | | - "id": "810b9edc", |
| 330 | + "id": "cbfdfc35", |
331 | 331 | "metadata": {}, |
332 | 332 | "source": [ |
333 | 333 | "## Scientific Programming with Python\n", |
|
354 | 354 | }, |
355 | 355 | { |
356 | 356 | "cell_type": "markdown", |
357 | | - "id": "7ccf0794", |
| 357 | + "id": "882534ac", |
358 | 358 | "metadata": {}, |
359 | 359 | "source": [ |
360 | 360 | "### NumPy\n", |
|
371 | 371 | { |
372 | 372 | "cell_type": "code", |
373 | 373 | "execution_count": null, |
374 | | - "id": "b01ab099", |
| 374 | + "id": "ef736bd4", |
375 | 375 | "metadata": { |
376 | 376 | "hide-output": false |
377 | 377 | }, |
|
383 | 383 | }, |
384 | 384 | { |
385 | 385 | "cell_type": "markdown", |
386 | | - "id": "09c5c59a", |
| 386 | + "id": "5f5a9723", |
387 | 387 | "metadata": {}, |
388 | 388 | "source": [ |
389 | 389 | "This array is very small so it’s fine to work with pure Python.\n", |
|
401 | 401 | { |
402 | 402 | "cell_type": "code", |
403 | 403 | "execution_count": null, |
404 | | - "id": "95ca5acf", |
| 404 | + "id": "a51666d2", |
405 | 405 | "metadata": { |
406 | 406 | "hide-output": false |
407 | 407 | }, |
|
415 | 415 | }, |
416 | 416 | { |
417 | 417 | "cell_type": "markdown", |
418 | | - "id": "740da7df", |
| 418 | + "id": "5a82aec9", |
419 | 419 | "metadata": {}, |
420 | 420 | "source": [ |
421 | 421 | "Now let’s transform this array by applying functions to it." |
|
424 | 424 | { |
425 | 425 | "cell_type": "code", |
426 | 426 | "execution_count": null, |
427 | | - "id": "0222da6a", |
| 427 | + "id": "6be857e7", |
428 | 428 | "metadata": { |
429 | 429 | "hide-output": false |
430 | 430 | }, |
|
436 | 436 | }, |
437 | 437 | { |
438 | 438 | "cell_type": "markdown", |
439 | | - "id": "348f61e9", |
| 439 | + "id": "f72c59ef", |
440 | 440 | "metadata": {}, |
441 | 441 | "source": [ |
442 | 442 | "Now we can easily take the inner product of `b` and `c`." |
|
445 | 445 | { |
446 | 446 | "cell_type": "code", |
447 | 447 | "execution_count": null, |
448 | | - "id": "af183b73", |
| 448 | + "id": "ef708946", |
449 | 449 | "metadata": { |
450 | 450 | "hide-output": false |
451 | 451 | }, |
|
456 | 456 | }, |
457 | 457 | { |
458 | 458 | "cell_type": "markdown", |
459 | | - "id": "6524a9f4", |
| 459 | + "id": "ae80a8a0", |
460 | 460 | "metadata": {}, |
461 | 461 | "source": [ |
462 | 462 | "We can also do many other tasks, like\n", |
|
471 | 471 | }, |
472 | 472 | { |
473 | 473 | "cell_type": "markdown", |
474 | | - "id": "4ae23efe", |
| 474 | + "id": "00600caf", |
475 | 475 | "metadata": {}, |
476 | 476 | "source": [ |
477 | 477 | "### NumPy Alternatives\n", |
|
497 | 497 | }, |
498 | 498 | { |
499 | 499 | "cell_type": "markdown", |
500 | | - "id": "3d4ff868", |
| 500 | + "id": "1606acfd", |
501 | 501 | "metadata": {}, |
502 | 502 | "source": [ |
503 | 503 | "### SciPy\n", |
|
512 | 512 | { |
513 | 513 | "cell_type": "code", |
514 | 514 | "execution_count": null, |
515 | | - "id": "91c9ecf7", |
| 515 | + "id": "0cdcfd34", |
516 | 516 | "metadata": { |
517 | 517 | "hide-output": false |
518 | 518 | }, |
|
528 | 528 | }, |
529 | 529 | { |
530 | 530 | "cell_type": "markdown", |
531 | | - "id": "44598e2f", |
| 531 | + "id": "688df24e", |
532 | 532 | "metadata": {}, |
533 | 533 | "source": [ |
534 | 534 | "SciPy includes many of the standard routines used in\n", |
|
548 | 548 | }, |
549 | 549 | { |
550 | 550 | "cell_type": "markdown", |
551 | | - "id": "c5bc9c87", |
| 551 | + "id": "88fec8b0", |
552 | 552 | "metadata": {}, |
553 | 553 | "source": [ |
554 | 554 | "### Graphics\n", |
|
594 | 594 | }, |
595 | 595 | { |
596 | 596 | "cell_type": "markdown", |
597 | | - "id": "5933259d", |
| 597 | + "id": "83a413d3", |
598 | 598 | "metadata": {}, |
599 | 599 | "source": [ |
600 | 600 | "### Networks and Graphs\n", |
|
628 | 628 | { |
629 | 629 | "cell_type": "code", |
630 | 630 | "execution_count": null, |
631 | | - "id": "702b36ba", |
| 631 | + "id": "1c089ad9", |
632 | 632 | "metadata": { |
633 | 633 | "hide-output": false |
634 | 634 | }, |
|
663 | 663 | }, |
664 | 664 | { |
665 | 665 | "cell_type": "markdown", |
666 | | - "id": "2840d363", |
| 666 | + "id": "382e6e2c", |
667 | 667 | "metadata": {}, |
668 | 668 | "source": [ |
669 | 669 | "### Other Scientific Libraries\n", |
|
698 | 698 | } |
699 | 699 | ], |
700 | 700 | "metadata": { |
701 | | - "date": 1763777710.9840705, |
| 701 | + "date": 1763884022.4286826, |
702 | 702 | "filename": "about_py.md", |
703 | 703 | "kernelspec": { |
704 | 704 | "display_name": "Python", |
|
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