15:[[17853248,{"by":"smaili","descendants":214,"id":17853248,"kids":[17853393,17853390,17853724,17854788,17857459,17853386,17856179,17854044,17853433,17856014,17857486,17854650,17854615,17856030,17854266,17853914,17854081,17853375,17853833,17853563,17853896,17854425,17854043,17856446,17853890],"score":310,"time":1535394500,"title":"Toyota Investing $500M in Uber in Driverless Car Pact","type":"story","url":"https://www.wsj.com/articles/toyota-investing-500-million-in-uber-in-driverless-car-pact-1535393774"}],[17464082,{"by":"devy","descendants":248,"id":17464082,"kids":[17464375,17466004,17465236,17464514,17465166,17464981,17464757,17464899,17464656,17464882,17465918,17465873,17506819,17478327,17473351,17468609,17469502,17465902,17467233,17465030,17466476,17464446,17467256,17465279,17464713],"score":108,"time":1530804992,"title":"Self-driving cars are headed toward an AI roadblock","type":"story","url":"https://www.theverge.com/2018/7/3/17530232/self-driving-ai-winter-full-autonomy-waymo-tesla-uber"}],[13909656,{"by":"craigcannon","descendants":219,"id":13909656,"kids":[13909996,13910751,13909984,13909738,13910506,13909784,13909727,13909817,13911243,13909884,13910415,13910145,13909711,13909765,13910493,13910180,13913807,13913904,13912710,13912182,13909938,13910866,13915413,13913155,13910484,13910872,13912373,13910666,13911920,13910058,13909852,13910836,13910932,13909734],"score":663,"time":1489957673,"title":"YC AI","type":"story","url":"https://blog.ycombinator.com/yc-ai"}],[12016568,{"by":"whoishiring","descendants":894,"id":12016568,"kids":[12016831,12017645,12017320,12017410,12017124,12019765,12016871,12018854,12017439,12018704,12016575,12017730,12017443,12020971,12017202,12016651,12017399,12017018,12017199,12016781,12017226,12016578,12017880,12016602,12017952,12018339,12017663,12016690,12016587,12018672,12017974,12018690,12017454,12019724,12021306,12016972,12016610,12020820,12017066,12016685,12017046,12020303,12019159,12017963,12016825,12018032,12017594,12016638,12017868,12016864,12031791,12016916,12017012,12020874,12017142,12021191,12020050,12016731,12021428,12019536,12018931,12020983,12018558,12017561,12019394,12017945,12016892,12016993,12018503,12017167,12019899,12017179,12016649,12018088,12022076,12022066,12016612,12022740,12018970,12021783,12016904,12017209,12019165,12019000,12022264,12074128,12018708,12018588,12018318,12021276,12019588,12019916,12017580,12016615,12019628,12017759,12020019,12016594,12018036,12019244,12019463,12019262,12017775,12018988,12017683,12017306,12018145,12017533,12024231,12018636,12016614,12019500,12017569,12016601,12025099,12018641,12017554,12018133,12017734,12017505,12018072,12016826,12018805,12019077,12020304,12024192,12019766,12016585,12016830,12018785,12037381,12017677,12019993,12017135,12018000,12020169,12018210,12017387,12017052,12019385,12018031,12059263,12017259,12019824,12021224,12019543,12024093,12023442,12017729,12016682,12017249,12016865,12017379,12018812,12017467,12017587,12018881,12024766,12018534,12018280,12019348,12017469,12019287,12017372,12018923,12020200,12019021,12017574,12017397,12019192,12018403,12018922,12019431,12018993,12018767,12018211,12017828,12019791,12018312,12019206,12017524,12020266,12019286,12019622,12018205,12016869,12021999,12017055,12016877,12020178,12018092,12018835,12019248,12018841,12017713,12016862,12020487,12016977,12017814,12017562,12017079,12019556,12020509,12051433,12018718,12023119,12017335,12017790,12022050,12018216,12042565,12017794,12019147,12017221,12036448,12030440,12053379,12018203,12036653,12019277,12016588,12023008,12019373,12017071,12017104,12020226,12017160,12019928,12044836,12018597,12018302,12018084,12025722,12016574,12023763,12080510,12028727,12019831,12025495,12018007,12017968,12019612,12018756,12018816,12027453,12017149,12018774,12038922,12036971,12020952,12019745,12017826,12017815,12021450,12018334,12016656,12017484,12017938,12016596,12020037,12018781,12018127,12017089,12022244,12017782,12036897,12019191,12018296,12016593,12071928,12018010,12048574,12019886,12019985,12019045,12018355,12073526,12019055,12029465,12020720,12017680,12036197,12022860,12016592,12018066,12016856,12017980,12016817,12017672,12028017,12021388,12017861,12019664,12058151,12073497,12019124,12017702,12018460,12022242,12018153,12083687,12017263,12021073,12017458,12021046,12023331,12035067,12032620,12016789,12031607,12017253,12020593,12017993,12019512,12021291,12017836,12049465,12017433,12017246,12016969,12086585,12028292,12017849,12018935,12017688,12017579,12018499,12017063,12016863,12017385,12017150,12020792,12016851,12019551,12018299,12017990,12018194,12017030,12023753,12017065,12018916,12018580,12018699,12017176,12021736,12019374,12017958,12053196,12029933,12017087,12016676,12040074,12020383,12018049,12017514,12022253,12031350,12018953,12071046,12018480,12017684,12020213,12018808,12018416,12020698,12040617,12018156,12019066,12017879,12018188,12071902,12037043,12018155,12024479,12058946,12021332,12017746,12019667,12016572,12024860,12023085,12042228,12018132,12016870,12016716,12038931,12018410,12022781,12020218,12017607,12020223,12045964,12016979,12059395,12019760,12026118,12019739,12019563,12028118,12019340,12017690,12063570,12019521,12071897,12020471,12018040,12017642,12018819,12017340,12071705,12018256,12020269,12017999,12028930,12043120,12036135,12018343,12017191,12039640,12021446,12045786,12020398,12018144,12016908,12018352,12018685,12018327,12022382,12067762,12017570,12020034,12019029,12017992,12019470,12017946,12017886,12018109,12020264,12020432,12018207,12045376,12019136,12018069,12020960,12018989,12038403,12020143,12019779,12021049,12019058,12018326,12019688,12018150,12019795,12017973,12055649,12019457,12018662,12020335,12050650,12053683,12017966,12018723,12017051,12018125,12028723,12018545,12019113,12017178,12016873,12036581,12029448,12046088,12024351,12088797,12031891,12017712,12044919,12033396,12017928,12017258,12017838,12020391,12039314,12026018,12019508,12019050,12017060,12018962,12019112,12021777,12049592,12020191,12018017,12017844,12020895,12018486,12050907,12017085,12040282,12017675,12030178,12017298,12093637,12016981,12016967,12073223,12017106,12017546,12021495,12017159,12046189,12056498,12018319,12017762,12019565,12018623,12018438,12045384,12071648,12071600,12019455,12045665,12020095,12017978,12082229,12039615,12017039,12017432,12017581,12018378,12048547,12022188,12098446,12054748,12074182,12022817,12017907,12016957,12017817,120185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lead with the location of the position and include the keywords\nREMOTE, INTERNS and/or VISA when the corresponding sort of candidate is welcome.\nWhen remote work is not an option, please include ONSITE. A one-sentence summary of\nyour interview process would also be helpful.

Submitters: please only post if you personally are part of the hiring company—no\nrecruiting firms or job boards.

Readers: please only email submitters if you personally are interested in the\njob—no recruiters or sales calls.

You can also use kristopolous' console script to search the thread:\nhttps://news.ycombinator.com/item?id=10313519.","time":1467385281,"title":"Ask HN: Who is hiring? (July 2016)","type":"story"}],[9432952,{"by":"mrborgen","descendants":35,"id":9432952,"kids":[9434031,9433316,9433804,9433981,9434774,9434063,9433685,9433060,9434344,9433732,9433715,9436978,9433979,9433843,9434729,9434921,9433797,9433850,9433814,9434944,9434765,9435746,9439015],"score":107,"text":"I'll spend all my time next week studying machine learning. However, I know that it's easy to get lost in overly theoretical material when learning ML.

So I'm interested in some advice on how to best spend my time, given that I'd like to get as much practical knowledge as possible.

Here is the level I'm at now:

I'm currently halfways though Andrew Ng's ML course on Coursera, and will probably finish this within the week. I love the mix of theory an practice this course is based around.

I've also done the Udacity - Intro to Machine Learning, but found it too theoretical.

I kind of understand the basic principles of linear & logistic regression, cost functions and gradient descent & the normal equation.

By the end of the week, I hope to be able to do linear regression using gradient descent on an actual dataset. If so, the week has been very well spent!

My preferred language is Python.

All tips and suggestions are highly appreciated :)","time":1429879824,"title":"Ask HN: How does a beginner best spend one week of learning machine learning?","type":"story"}],[4361711,{"by":"giladvdn","descendants":40,"id":4361711,"kids":[4362039,4362011,4362522,4361893,4362576,4362425,4361867,4361944,4362238,4362018,4364862,4365347,4361840,4361980,4364308,4363253,4364861,4363195,4364371,4362201,4362175,4363799,4365348,4362517],"score":158,"time":1344528530,"title":"Instant splash pages for iOS apps","type":"story","url":"http://www.smore.com/for-apps"}]]

1.	Ask HN: Where are companies using (Deep) Reinforcement Learning 3 points by samirparikh 9 years ago \| 0 comments
2.	Show HN: Using Reinforcement Learning to Perform Motion Planning on a YuMi Robot(robosamir.github.io) 2 points by samirparikh 9 years ago \| 0 comments

user:	samirparikh
created:	August 9, 2012
karma:	26