гҖҗиҜҫзЁӢд»Ӣз»ҚгҖ‘

ж•ҙдёӘи®ӯз»ғиҗҘеҺҶж—¶4дёӘжңҲзҡ„еӯҰд№ ж—¶й—ҙпјҢеҢ…жӢ¬еӨҡдёәдёҖзәҝжҠҖжңҜ专家дәІиҮӘжҺҲиҜҫпјҢеқҮжҜ•дёҡдәҺзҫҺеӣҪйЎ¶е°–й«ҳж ЎгҖӮиҜҫзЁӢиҰҶзӣ–дәҶ10еӨҡдёӘиҮӘ然иҜӯиЁҖе®һйҷ…йЎ№зӣ®гҖӮд»Һдј з»ҹNLPжҠҖжңҜеҲ°еҹәдәҺж·ұеәҰеӯҰд№ зҡ„NLPжҠҖжңҜпјҢеё®еҠ©еӯҰе‘ҳзҗҶи§ЈжҠҖжңҜж·ұеұӮж¬Ўзҡ„жң¬иҙЁе…ізі»гҖӮеӯҰе‘ҳжңүжңәдјҡйҖүжӢ©е№¶е®ҢжҲҗиҮӘе·ұж„ҹе…ҙи¶Јзҡ„иҜҫйўҳпјҢж— и®әеә”з”ЁжҖ§зҡ„иҝҳжҳҜеӯҰжңҜжҖ§зҡ„иҜҫйўҳпјҢеҜјеёҲеҠ©ж•ҷеӣўйҳҹйғҪеҸҜд»ҘеҫҲеҘҪең°жҢҮеҜјдҪ гҖӮжҲ–и®ёиҝҷж ·зҡ„дёҖдёӘйЎ№зӣ®еҸҜд»Ҙи®©дҪ е®һзҺ°жӢҘжңүйЎ¶дјҡи®әж–Үзҡ„зӣ®ж ҮгҖӮ

жңҖдё“дёҡгҖҒжңҖдҪ“зі»еҢ–зҡ„иҜҫзЁӢ

иҜҫзЁӢ收иҺ·

жҺҢжҸЎеӨ„зҗҶйқһз»“жһ„еҢ–ж–Үжң¬ж•°жҚ®пјҢе®ҢжҲҗзҹҘиҜҶжҠҪеҸ–гҖҒNLUгҖҒеӨҡиҪ®еҜ№иҜқеҶізӯ–гҖҒйҖ»иҫ‘жҺЁзҗҶзӯүNLPж ёеҝғжҠҖжңҜпјӣ

ж·ұе…ҘзҗҶи§ЈеҲҶиҜҚгҖҒиҜӯд№үеҲҶжһҗгҖҒиҜӯд№үиЎЁзӨәгҖҒж–Үжң¬еҢ№й…ҚгҖҒж–Үжң¬еҲҶзұ»гҖҒдё»йўҳиҜҶеҲ«зӯүNLPдё»жөҒйўҶеҹҹпјӣ

жҺҢжҸЎиҜҚеҗ‘йҮҸеҰӮword2vecзӯүд»ҘеҸҠNLPзӣёе…ізҡ„ж·ұеәҰеӯҰд№ пјҲRNNгҖҒLSTMгҖҒBertзӯүпјүзӣёе…ізҹҘиҜҶпјӣ

жҺҢжҸЎNLPдј з»ҹгҖҒжңәеҷЁеӯҰд№ еҸҠж·ұеәҰеӯҰд№ з®—жі•пјҢе®ҢжҲҗQAзі»з»ҹгҖҒжңәеҷЁзҝ»иҜ‘гҖҒзҹҘиҜҶеӣҫи°ұгҖҒиҒҠеӨ©жңәеҷЁдәәзӯүе®һжҲҳйЎ№зӣ®гҖӮ

е®һжҲҳйЎ№зӣ®д»Ӣз»Қ

й—®зӯ”зі»з»ҹ

д»Һйӣ¶ејҖе§Ӣжҗӯе»әдёҖдёӘе®Ңж•ҙзҡ„й—®зӯ”зі»з»ҹгҖӮз»ҷе®ҡдёҖдёӘиҜӯж–ҷеә“пјҲй—®йўҳе’Ңзӯ”жЎҲеҜ№пјүпјҢеҜ№дәҺз”ЁжҲ·зҡ„иҫ“е…ҘйңҖиҰҒиҝ”еӣһжңҖйҖӮеҗҲзҡ„зӯ”жЎҲгҖӮж¶үеҸҠеҲ°зҡ„жЁЎеқ—пјҡ1. еҜ№дәҺз”ЁжҲ·зҡ„иҫ“е…ҘйңҖиҰҒеҒҡжӢјеҶҷзә й”ҷпјҢиҝҷйғЁеҲҶдјҡз”ЁеҲ°иҜӯиЁҖжЁЎеһӢ 2. д№ӢеҗҺеҜ№иҫ“е…ҘеҒҡж–Үжң¬зҡ„йў„еӨ„зҗҶпјҢиҝҮж»Өзӯүж“ҚдҪңгҖӮ 3. жҠҠж–Үжң¬иҪ¬жҚўжҲҗеҗ‘йҮҸеҪўејҸпјҢиҝҷйҮҢйңҖиҰҒз”ЁеҲ°tf-idf, word2vecзӯүзӣёе…ізҡ„жҠҖжңҜгҖӮ 4. й’ҲеҜ№дәҺиҜӯж–ҷеә“пјҢдёәдәҶжҸҗеҚҮж•ҲзҺҮйңҖиҰҒеҲӣе»әеҖ’жҺ’иЎЁгҖӮ 5. еҹәдәҺзӣёдјјеәҰзҡ„и®Ўз®—жқҘиҺ·еҫ—жңҖдјҳзҡ„зӯ”жЎҲгҖӮ

жғ…ж„ҹеҲҶжһҗзі»з»ҹ

еҹәдәҺз»ҷе®ҡж•°жҚ®пјҢжқҘжҗӯе»әдёҖдёӘе®Ңж•ҙзҡ„жғ…ж„ҹеҲҶжһҗзі»з»ҹгҖӮйЎ№зӣ®ж¶үеҸҠеҲ°зҡ„жЁЎеқ—пјҡ 1. ж•°жҚ®зҡ„йў„еӨ„зҗҶ 2. зү№еҫҒе·ҘзЁӢпјҢиҝҷйғЁеҲҶжҳҜжң¬йЎ№зӣ®зҡ„ж ёеҝғгҖӮ 3. зӣ‘зқЈеӯҰд№ жЁЎеһӢзҡ„йҖүжӢ©дёҺи°ғеҸӮгҖӮи°ғеҸӮзҡ„иҝҮзЁӢйңҖиҰҒе°қиҜ•дёҚеҗҢзҡ„дјҳеҢ–зӯ–з•ҘгҖӮ

зҹҘиҜҶеӣҫи°ұзі»з»ҹ

еҲ©з”Ёйқһз»“жһ„еҢ–ж•°жҚ®жқҘжҗӯе»әзҹҘиҜҶеӣҫи°ұгҖӮйЎ№зӣ®ж¶үеҸҠеҲ°зҡ„жЁЎеқ—пјҡ1. д»Һйқһз»“жһ„еҢ–ж•°жҚ®дёӯжҠҪеҸ–е®һдҪ“пјҢд»ҘеҸҠиҜҚе…ёеә“зҡ„жһ„е»ә 2. е…ізі»зҡ„жҠҪеҸ–пјҲжҢҮе®ҡзҡ„е…ізі»пјү 3. е®һдҪ“з»ҹдёҖд»ҘеҸҠе®һдҪ“ж¶Ҳжӯ§гҖӮ 4. зҹҘиҜҶеӣҫи°ұзҡ„жһ„е»әд»ҘеҸҠжҹҘиҜў

еҜ№иҜқзі»з»ҹдёӯзҡ„NLU

еҹәдәҺз»ҷе®ҡзҡ„еҜ№иҜқж•°жҚ®жқҘжһ„е»әNLUиҜҶеҲ«йғЁеҲҶпјҢ并结жһңз”ЁдәҺиҒҠеӨ©жңәеҷЁдәәдёӯгҖӮ йЎ№зӣ®ж¶үеҸҠеҲ°зҡ„жЁЎеқ—пјҡ 1. ж–Үжң¬зү№еҫҒзҡ„жҸҗеҸ– 2. жҗӯе»әCRFжЁЎеһӢжқҘиҜҶеҲ«е…ій”®иҜҚ 3. жҗӯе»әLSTM-CRFжЁЎеһӢжқҘиҜҶеҲ«е…ій”®иҜҚгҖӮ

жңәеҷЁзҝ»иҜ‘зі»з»ҹ

жҗӯе»әдёҖдёӘе®Ңж•ҙзҡ„жңәеҷЁзҝ»иҜ‘зі»з»ҹгҖӮиҝҷйҮҢдҪҝз”Ёзҡ„ж–№жі•и®әжҳҜз«ҜеҲ°з«Ҝзҡ„ж–№жі•гҖӮйЎ№зӣ®йңҖиҰҒдҪҝз”ЁдёӨз§ҚдёҚеҗҢзҡ„ж–№жі•пјҡ 1. еҹәдәҺеӨҡеұӮLSTM+Attentionзҡ„ж–№жі•гҖӮ2. еҹәдәҺеӨҡеұӮTransformer, Bertзҡ„ж–№жі•гҖӮ

д»»еҠЎеҜјеҗ‘еһӢиҒҠеӨ©жңәеҷЁдәә

жҗӯе»әдёҖдёӘе®Ңж•ҙзҡ„иҒҠеӨ©жңәеҷЁдәәпјҢз”ЁжқҘжңҚеҠЎжҗңзҙўйӨҗеҺ…гҖӮйЎ№зӣ®ж¶үеҸҠеҲ°зҡ„жЁЎеқ—пјҡ1. ж–Үжң¬йў„еӨ„зҗҶ 2. ж„ҸеӣҫиҜҶеҲ«е’Ңе…ій”®дҝЎжҒҜжҠҪеҸ– 3. еҜ№дәҺжҜҸдёҖдёӘж„Ҹеӣҫи®ҫи®ЎеҜ№иҜқз®ЎзҗҶзҠ¶жҖҒжңә 4. и®ҫи®ЎдёҠдёӢж–ҮеӨ„зҗҶзҡ„ж–№жі• 5. еҜ№иҜқз”ҹжҲҗжЁЎеқ— 6. еӨ„зҗҶдёҖдәӣеёёи§Ғзҡ„boundary caseгҖӮ

ејҖж”ҫеһӢCapstoneйЎ№зӣ®пјҲд»ҳиҙ№йЎ№зӣ®пјү

иҝҷжҳҜдёҖдёӘејҖж”ҫжҖ§зҡ„йЎ№зӣ®пјҢеӯҰе‘ҳж №жҚ®иҮӘе·ұзҡ„е…ҙи¶ЈеҸҜд»ҘйҖүжӢ©ж–№еҗ‘д»ҘеҸҠйўҳзӣ®пјҢеҜјеёҲд»ҘеҸҠеҠ©ж•ҷеӣўйҳҹдјҡе…ЁзЁӢжҢҮеҜјдҪ е®ҢжҲҗгҖӮйҖүжӢ©зҡ„йЎ№зӣ®еҸҜд»Ҙеӣҙз»•иҮӘе·ұжүҖзҶҹжӮүзҡ„д»»дҪ•NLPеңәжҷҜпјҢд№ҹеҸҜд»ҘжҳҜзәҜеӯҰжңҜжҖ§зҡ„пјҢеҪ“然й’ҲеҜ№еӯҰжңҜжҖ§зҡ„йЎ№зӣ®пјҢеҰӮжһңжңүдёҖе®ҡеҲӣж–°жҖ§пјҢеҜјеёҲеӣўйҳҹд№ҹеҸҜд»ҘеҚҸеҠ©дҪ еҸ‘иЎЁйЎ¶дјҡи®әж–ҮгҖӮйЎ№зӣ®зҡ„дёӯжңҹе’ҢжңҖеҗҺпјҢдјҡжңүз®Җзҹӯзҡ„еӯҰе‘ҳйЎ№зӣ®еұ•зӨәзҺҜиҠӮпјҢеҜјеёҲе’ҢеҠ©ж•ҷеӣўйҳҹдјҡжҸҗдҫӣе»әи®ҫжҖ§зҡ„ж„Ҹи§ҒгҖӮ

иҜҫзЁӢзӣ®еҪ•

• 001.иҮӘ然иҜӯиЁҖеӨ„зҗҶи®ӯз»ғиҗҘ
• 002.и®ӯз»ғиҗҘд»Ӣз»ҚиҜҫзЁӢдҪ“зі»д»Ӣз»Қ
• 003.NLPе®ҡд№үд»ҘеҸҠжӯ§д№үжҖ§
• 004.жЎҲдҫӢ.жңәеҷЁзҝ»иҜ‘01
• 005.жЎҲдҫӢ.жңәеҷЁзҝ»иҜ‘02
• 006.NLPзҡ„еә”з”ЁеңәжҷҜ
• 007.NLPзҡ„е…ій”®жҠҖжңҜ
• 008.з®—жі•еӨҚжқӮеәҰд»Ӣз»Қ
• 009.иҜҫеҗҺзӯ”з–‘
• 010.з®ҖеҚ•зҡ„еӨҚжқӮеәҰзҡ„еӣһйЎҫ
• 011.еҪ’并жҺ’еәҸ
• 012.MasterTheorem
• 013.ж–җжіўйӮЈеҘ‘ж•°зҡ„ж—¶й—ҙеӨҚжқӮеәҰ
• 014.ж–җжіўйӮЈеҘ‘ж•°зҡ„з©әй—ҙеӨҚжқӮеәҰ
• 015.ж–җжіўйӮЈеҘ‘ж•°зҡ„еҫӘзҺҜе®һзҺ°
• 016.PvsNPvsNPHardvsNPComplete
• 017.й—®зӯ”зі»з»ҹд»Ӣз»Қ
• 018.ReviewдёҖеҸӘзӢ—е’ҢдёӨеҸӘзҢ«зҡ„ж•…дәӢвҖ”вҖ”еҝғзҗҶеӯҰдёҺDL,RL-01
• 019.ReviewдёҖеҸӘзӢ—е’ҢдёӨеҸӘзҢ«зҡ„ж•…дәӢвҖ”вҖ”еҝғзҗҶеӯҰдёҺDL,RL-02
• 020.ж–Үжң¬еӨ„зҗҶзҡ„жөҒзЁӢ
• 021.еҲҶиҜҚ-еүҚеҗ‘жңҖеӨ§еҢ№й…Қ
• 022.еҲҶиҜҚ-еҗҺеҗ‘жңҖеӨ§еҢ№й…Қ
• 023.еҲҶиҜҚ-иҖғиҷ‘иҜӯиЁҖжЁЎеһӢ
• 024.еҲҶиҜҚ-з»ҙзү№жҜ”з®—жі•
• 025.жӢјеҶҷй”ҷиҜҜзә жӯЈ
• 026.жӢјеҶҷзә й”ҷпјҲ2пјү
• 027.жӢјеҶҷзә й”ҷпјҲ3пјү
• 028.еҒңз”ЁиҜҚиҝҮж»ӨпјҢStemmingж“ҚдҪң
• 029.ж–Үжң¬зҡ„иЎЁзӨә
• 030.ж–Үжң¬зҡ„зӣёдјјеәҰ
• 031.tf-idfж–Үжң¬иЎЁзӨә
• 032.иҜҚеҗ‘йҮҸд»Ӣз»Қ
• 033.еӯҰд№ иҜҚеҗ‘йҮҸ
• 034.еҖ’жҺ’иЎЁ
• 035.NoisyChannelModel
• 036.иҜӯиЁҖжЁЎеһӢд»Ӣз»Қ
• 037.ChainRuleе’ҢMarkovAssumption
• 038.Unigram,Bigram,N-gram
• 039.дј°и®ЎиҜӯиЁҖжЁЎеһӢзҡ„жҰӮзҺҮ
• 040.иҜ„дј°иҜӯиЁҖжЁЎеһӢ.Perplexity
• 041.Add-oneSmoothing
• 042.Add-KSmoothing
• 043.Interpolation
• 044.2019.2.17ReviewеҹәдәҺеҮ дёӘдҫӢеӯҗпјҢд№ҰеҶҷеҠЁжҖҒ规еҲ’-01
• 045.2019.2.17ReviewеҹәдәҺеҮ дёӘдҫӢеӯҗпјҢд№ҰеҶҷеҠЁжҖҒ规еҲ’-02
• 046.2019.2.17ReviewеҹәдәҺеҮ дёӘдҫӢеӯҗпјҢд№ҰеҶҷеҠЁжҖҒ规еҲ’-03
• 047.Lesson6зӣҙж’ӯ
• 048.еңЁи®ӯз»ғж•°жҚ®йҮҢжІЎжңүи§ҒиҝҮзҡ„жҖҺд№ҲеӨ„зҗҶпјҹ
• 049.Good-TurningSmoothing
• 050.еҲ©з”ЁиҜӯиЁҖжЁЎеһӢз”ҹжҲҗеҸҘеӯҗ
• 051.专家系з»ҹдёҺеҹәдәҺжҰӮзҺҮз»ҹи®ЎеӯҰд№
• 052.专家系з»ҹд»Ӣз»Қ
• 053.йҖ»иҫ‘жҺЁзҗҶ
• 054.CaseStudyйЈҺжҺ§
• 055.дёҖдәӣйҡҫйўҳ
• 056.жңәеҷЁеӯҰд№ д»Ӣз»Қ01
• 057.жңәеҷЁеӯҰд№ д»Ӣз»Қ02
• 058.жңҙзҙ иҙқеҸ¶ж–Ҝд»Ӣз»Қ
• 059.CaseStudyеһғеңҫйӮ®д»¶иҝҮж»Ө
• 060.lambdaиЎЁиҫҫејҸ
• 061.mapеҮҪж•°зҡ„еә”з”Ё
• 062.filterиҝҮж»ӨеҷЁ
• 063.reduceеҮҪж•°
• 064.pythonдёүеӨ§жҺЁеҜјејҸ
• 065.й—ӯеҢ…
• 066.иЈ…йҘ°еҷЁдёҖ
• 067.иЈ…йҘ°еҷЁдәҢ
• 068.еҲқиҜҶnumpy
• 069.numpyж•°з»„зҡ„еҲӣе»ә
• 070.numpyзҡ„зҹўйҮҸеҢ–иҝҗз®—
• 071.numpyзҡ„иҠұејҸзҙўеј•
• 072.numpyж•°з»„иҪ¬зҪ®е’ҢиҪҙеҜ№жҚў
• 073.жқЎд»¶йҖ»иҫ‘иҪ¬ж•°з»„
• 074.ж•°еӯҰиҝҗз®—дёҺжҺ’еәҸ
• 075.numpyж–Ү件еӨ„зҗҶ
• 076.зәҝжҖ§д»Јж•°еҮҪж•°е’ҢйҡҸжңәжј«жӯҘдҫӢеӯҗ
• 077.иҜҚжҖ§ж ҮжіЁ-е®һжҲҳпјҲ1пјү
• 078.иҜҚжҖ§ж ҮжіЁ--е®һжҲҳпјҲ2пјү
• 079.иҜҚжҖ§ж ҮжіЁ-е®һжҲҳпјҲ3пјү
• 080.иҜҚжҖ§ж ҮжіЁ-е®һжҲҳпјҲ4пјү
• 081.иҜҚжҖ§ж ҮжіЁ-е®һжҲҳпјҲ5пјү
• 082.еҲқиҜҶseriesзұ»еһӢ
• 083.еҲқиҜҶdataframe
• 084.йҮҚж–°зҙўеј•гҖҒж•°еӯҰиҝҗз®—е’Ңж•°жҚ®еҜ№йҪҗ
• 085.dataframeе’Ңseriesд№Ӣй—ҙзҡ„иҝҗз®—е’ҢжҺ’еәҸ
• 086.еұӮж¬ЎеҢ–зҙўеј•
• 087.dataframeзҡ„еұӮж¬ЎеҢ–зҙўеј•зҡ„и®ҝй—®е’ҢжұҮжҖ»иҝҗз®—
• 088.pandasиҜ»еҶҷcsvж–Ү件
• 089.pandasиҜ»еҸ–excelж–Ү件并画еӣҫ
• 090.matplotlibеҸҜи§ҶеҢ–еҸҠеӯҰд№ ж–№жі•е»әи®®
• 091.иҷҡжӢҹзҺҜеўғзҡ„жҗӯе»ә
• 092.еҲӣе»ә第дёҖдёӘзҲ¬иҷ«йЎ№зӣ®
• 093.и°ғиҜ•иҝҗиЎҢзҲ¬иҷ«зЁӢеәҸ
• 094.13-scrapyshellи°ғиҜ•ж–№жі•иҝӣиЎҢе…ғзҙ е®ҡдҪҚ
• 095.и®ҝй—®йҰ–йЎөеҲ—иЎЁдёӯзҡ„url
• 096.иҺ·еҸ–её–еӯҗж Үйўҳе’ҢеҶ…е®№
• 097.еӨ„зҗҶеё–еӯҗеҶ…е®№дёӯзҡ„зү№ж®Ҡж Үзӯҫ
• 098.иҺ·еҸ–её–еӯҗеҸ‘йҖҒж—¶й—ҙеҸҠдҪҚдәҺзҡ„жҘјж•°
• 099.зҲ¬иҷ«зҡ„bugи°ғиҜ•дёҺдҝ®еӨҚ
• 100.ж•°жҚ®жҢҒд№…еҢ–д»Јз ҒејҖеҸ‘
• 101.ж•°жҚ®е…Ҙеә“
• 102.importancesamplenegtivesamplence-01
• 103.importancesamplenegtivesamplence-02
• 104.importancesamplenegtivesamplence-03
• 105.зІҫзЎ®зҺҮе’ҢеҸ¬еӣһзҺҮ
• 106.йҖ»иҫ‘еӣһеҪ’д»Ӣз»Қ
• 107.йҖ»иҫ‘еӣһеҪ’жҳҜзәҝжҖ§еҲҶзұ»еҷЁ
• 108.йҖ»иҫ‘еӣһеҪ’зҡ„зӣ®ж ҮеҮҪж•°
• 109.жўҜеәҰдёӢйҷҚжі•
• 110.йҖ»иҫ‘еӣһеҪ’зҡ„жўҜеәҰдёӢйҷҚжі•
• 111.еҪ“зәҝжҖ§еҸҜеҲҶзҡ„ж—¶еҖҷ
• 112.е…ідәҺйқўиҜ•зҡ„иҜқйўҳ-01
• 113.е…ідәҺйқўиҜ•зҡ„иҜқйўҳ-02
• 114.е…ідәҺйқўиҜ•зҡ„иҜқйўҳ-03
• 115.зӣҙж’ӯпјҲдјҳеҢ–з®—жі•пјү-01
• 116.зӣҙж’ӯ-02
• 117.зӣҙж’ӯ-03
• 118.зӣҙж’ӯ-04
• 119.зӣҙж’ӯ-05
• 120.зӣҙж’ӯ-06
• 121.зӣҙж’ӯ-07
• 122.зӣҙж’ӯ-08
• 123.зӣҙж’ӯ-09
• 124.зӣҙж’ӯ-10
• 125.зӣҙж’ӯ-11
• 126.еҪ“ж•°жҚ®зәҝжҖ§еҸҜеҲҶеүІзҡ„ж—¶еҖҷ
• 127.йҷҗеҲ¶еҸӮж•°еҸҳеҫ—еӨӘеӨ§
• 128.жЁЎеһӢеӨҚжқӮеәҰдёҺиҝҮжӢҹеҗҲ
• 129.жҖҺд№ҲйҒҝе…ҚиҝҮжӢҹеҗҲ
• 130.жӯЈеҲҷд»Ӣз»Қ
• 131.L1VSL2
• 132.reviewж•°жҚ®з»“жһ„дёІи®І-01
• 133.reviewж•°жҚ®з»“жһ„дёІи®І-02
• 134.AffectiveComputing&жғ…з»ӘиҜҶеҲ«е®һжҲҳ
• 135.дәӨеҸүйӘҢиҜҒпјҲ1пјү
• 136.дәӨеҸүйӘҢиҜҒпјҲ2пјү
• 137.жӯЈеҲҷзҡ„дҪңз”Ё
• 138.MLEVSMAPд»Ӣз»Қ
• 139.жӯЈеҲҷзҡ„дҪҝз”Ё
• 140.дәӨеҸүйӘҢиҜҒ
• 141.еҸӮж•°жҗңзҙўзӯ–з•Ҙ
• 142.й«ҳзә§.жӯЈеҲҷзҡ„зҒөжҙ»еә”з”Ё
• 143.жҖ»з»“
• 144.MLEдёҺMAP
• 145.LassoRegressionд»Ӣз»Қ
• 146.зү№еҫҒйҖүжӢ©жҠҖжңҜ
• 147.LASSOд»Ӣз»Қ
• 148.CoordinateDescent
• 149.CoordinateDescentforLASSO
• 150.е…¶д»–LASSOSolver
• 151.еҸҳеҲҶжҺЁж–ӯжҢҮж•°ж—Ҹ家ж—Ҹlda
• 152.Optimization
• 153.OptimizationisEverywhere
• 154.Optimization-Categories
• 155.ConvexOptimization-GlobalvsLocalOptimal
• 156.еҲӨж–ӯдёҖдёӘеҮҪж•°жҳҜеҮёеҮҪж•°
• 157.и§ЈеҶідёҖдёӘе…·дҪ“й—®йўҳ1
• 158.и§ЈеҶідёҖдёӘе…·дҪ“й—®йўҳ2
• 159.еӣһйЎҫеҮёеҮҪж•°
• 160.д»Ӣз»ҚSetCoverProblem
• 161.Approach1-ExhaustiveSearch
• 162.Approach2-иҙӘеҝғз®—жі•
• 163.Approach3-Optimization
• 164.жҖ»з»“
• 165.еӣһйЎҫ-йҖ»иҫ‘еӣһеҪ’зҡ„жўҜеәҰдёӢйҷҚжі•
• 166.жўҜеәҰдёӢйҷҚжі•зҡ„еӨҚжқӮеәҰ
• 167.жўҜеәҰдёӢйҷҚжі•зҡ„收ж•ӣеҲҶжһҗ
• 168.еҮёеҮҪж•°жҖ§иҙЁд»ҘеҸҠL-LipschitzжқЎд»¶
• 169.收ж•ӣжҖ§жҺЁеҜј
• 170.LinearClassifier
• 171.Marginзҡ„и®Ўз®—
• 172.SVMзҡ„зӣ®ж ҮеҮҪж•°.Hardconstraint
• 173.SVMзҡ„зӣ®ж ҮеҮҪж•°.Softconstraint
• 174.HingeLoss
• 175.Primal-Dualд»Ӣз»Қ
• 176.attentiontransformerbert-01
• 177.attentiontransformerbert-02
• 178.CapstoneйЎ№зӣ®д»Ӣз»Қ
• 179.LinearSVMзҡ„зјәзӮ№
• 180.ж•°жҚ®жҳ е°„еҲ°й«ҳз»ҙ
• 181.жӢүж јжң—ж—Ҙ-зӯүеҸ·жқЎд»¶еӨ„зҗҶ
• 182.жӢүж јжң—ж—Ҙ-дёҚзӯүеҸ·жқЎд»¶еӨ„зҗҶ
• 183.KKTжқЎд»¶
• 184.SVMзҡ„KKTжқЎд»¶
• 185.Primal-Dualд»Ӣз»Қ
• 186.SVMзҡ„DualжҺЁеҜј
• 187.KernelTrick
• 188.дҝЎжҒҜжҠҪеҸ–д»Ӣз»Қзӣҙж’ӯ
• 189.е‘ҪеҗҚе®һдҪ“иҜҶеҲ«д»Ӣз»Қ
• 190.з®ҖеҺҶеҲҶжһҗеңәжҷҜ
• 191.жҗӯе»әNERеҲҶзұ»еҷЁ
• 192.ж–№жі•д»Ӣз»Қ
• 193.еҹәдәҺ规еҲҷзҡ„ж–№жі•
• 194.жҠ•зҘЁеҶізӯ–ж–№жі•
• 195.зү№еҫҒе·ҘзЁӢдёҺзү№еҫҒиЎЁзӨә01
• 196.зү№еҫҒе·ҘзЁӢдёҺзү№еҫҒиЎЁзӨә02
• 197.й—®зӯ”
• 198.дҝЎжҒҜжҠҪеҸ–д»Ӣз»Қ
• 199.OntologicalRelation
• 200.е…ізі»жҠҪеҸ–ж–№жі•д»Ӣз»Қ
• 201.еҹәдәҺ规еҲҷзҡ„ж–№жі•
• 202.еҹәдәҺзӣ‘зқЈеӯҰд№ зҡ„ж–№жі•
• 203.cnnrnntransformerеҜ№жҜ”-01
• 204.cnnrnntransformerеҜ№жҜ”-02
• 205.е…ізі»жҠҪеҸ–
• 206.bootstrapз®—жі•зҡ„зјәзӮ№
• 207.SnowBallз®—жі•
• 208.з”ҹжҲҗжЁЎжқҝ
• 209.з”ҹжҲҗtupleдёҺжЁЎжқҝиҜ„дј°
• 210.иҜ„дј°и®°еҪ•+иҝҮж»Ө
• 211.SnowBallжҖ»з»“
• 212.EntityDisambiguation(е®һдҪ“ж¶Ҳжӯ§)д»Ӣз»Қ
• 213.е®һдҪ“ж¶Ҳжӯ§з®—жі•
• 214.EntityResolution(е®һдҪ“з»ҹдёҖ)
• 215.е®һдҪ“з»ҹдёҖз®—жі•
• 216.Co-referenceResolution(жҢҮд»Јж¶Ҳи§Ј)д»Ӣз»Қ
• 217.д»Җд№ҲжҳҜеҸҘжі•еҲҶжһҗ
• 218.еҸҘжі•еҲҶжһҗзҡ„еә”з”Ё
• 219.иҜӯжі•
• 220.PCFG
• 221.иҜ„дј°иҜӯжі•ж ‘
• 222.еҜ»жүҫжңҖеҘҪзҡ„ж ‘
• 223.CNFForm
• 224.CKYз®—жі•
• 225.ж—¶еәҸжЁЎеһӢ
• 226.HMMзҡ„д»Ӣз»Қ
• 227.HMMзҡ„еә”з”ЁдҫӢеӯҗ
• 228.HMMзҡ„еҸӮж•°
• 229.HMMдёӯзҡ„Inferenceй—®йўҳ
• 230.HMMдёӯзҡ„FBз®—жі•1
• 231.HMMдёӯзҡ„FBз®—жі•2
• 232.HMMдёӯзҡ„FBз®—жі•3
• 233.DataRepresentation
• 234.LatentVariableModels
• 235.CompletevsIncompleteCase
• 236.MLEforCompleteandIncompleteCase
• 237.EMDerivation
• 238.RemarksonEM
• 239.K-means
• 240.K-meansCostFunction
• 241.MLEforGMM
• 242.жЁЎжӢҹйқўиҜ•(vep)-01
• 243.жЁЎжӢҹйқўиҜ•(vep)-02
• 244.HMMдёӯзҡ„еҸӮж•°
• 245.CompletevsIncompleteCase
• 246.CompleteCase
• 247.IncompleteCase
• 248.EMз®—жі•еӣһйЎҫ
• 249.FBз®—жі•еӣһйЎҫ
• 250.дј°и®ЎPI
• 251.дј°и®ЎB
• 252.дј°и®ЎA
• 253.е…¬еҸёе®һйҷ…йЎ№зӣ®дёІи®І-01
• 254.е…¬еҸёе®һйҷ…йЎ№зӣ®дёІи®І-02
• 255.е…¬еҸёе®һйҷ…йЎ№зӣ®дёІи®І-03
• 256.жңүеҗ‘еӣҫдёҺж— еҗ‘еӣҫжЁЎеһӢ
• 257.з”ҹжҲҗжЁЎеһӢдёҺеҲӨеҲ«жЁЎеһӢ
• 258.Log-LinearModel
• 259.Log-LinearModelдёҺеӨҡе…ғйҖ»иҫ‘еӣһеҪ’
• 260.CRFд»Ӣз»Қ
• 261.Inferenceй—®йўҳ
• 262.еҸӮж•°дј°и®Ў
• 263.wordvectorиҜҚеҗ‘йҮҸ
• 264.GlobalGenerationofDistributedRepresentation
• 265.HowtoLearnWord2Vec-Intuition
• 266.Skip-GramModel
• 267.иҜӯж–ҷеә“
• 268.Word2Vecд»Јз Ғ
• 269.и®ӯз»ғSkipGramй—®йўҳ
• 270.SkipGramеҸҰдёҖз§Қзӣ®ж ҮеҮҪж•°жһ„е»ә
• 271.SkipGramзҡ„negativesampling
• 272.иҜ„дј°иҜҚеҗ‘йҮҸ
• 273.иҜҚеҗ‘йҮҸеңЁжҺЁиҚҗзі»з»ҹдёӯзҡ„еә”з”Ё
• 274.жўҜеәҰжҸҗеҚҮж ‘
• 275.зӯ”з–‘
• 276.Word2vec
• 277.LearningwithSubword
• 278.Whensubwordisneeded
• 279.LearnEmbeddingfromLanguageModel
• 280.Whatarepotentialsolutions
• 281.ElmoatGlance
• 282.CategoryofWordRepresentation
• 283.зҘһз»ҸзҪ‘з»ңд»Ӣз»Қ
• 284.жҝҖжҙ»еҮҪж•°
• 285.MLP
• 286.еӨҡеұӮзҘһз»ҸзҪ‘з»ң
• 287.UniversalApproximationTheorem
• 288.BiologicalInspiration
• 289.еӣһйЎҫзҘһз»ҸзҪ‘з»ң
• 290.зҘһз»ҸзҪ‘з»ңзҡ„жҚҹеӨұеҮҪж•°
• 291.BPз®—жі•зҡ„ж ёеҝғжөҒзЁӢ
• 292.еҜ№иҫ“еҮәеұӮзҡ„жўҜеәҰи®Ўз®—
• 293.еҜ№йҡҗеҗ«еұӮзҡ„жўҜеәҰи®Ўз®—
• 294.еҜ№еҸӮж•°зҡ„жўҜеәҰи®Ўз®—
• 295.еҜ№BPз®—жі•зҡ„жҖ»з»“
• 296.gradientchecking
• 297.ж·ұеәҰеӯҰд№ дёҺйқһеҮёеҮҪж•°
• 298.ж·ұеәҰеӯҰд№ дёӯзҡ„Plateau
• 299.SGDзҡ„收ж•ӣжқЎд»¶
• 300.EarlyStopping
• 301.дёәд»Җд№ҲйңҖиҰҒйҖ’еҪ’зҘһз»ҸзҪ‘з»ңпјҹ
• 302.йҖ’еҪ’зҘһз»ҸзҪ‘з»ңд»Ӣз»Қ
• 303.иҜӯиЁҖжЁЎеһӢ
• 304.RNNзҡ„ж·ұеәҰ
• 305.жўҜеәҰзҲҶзӮёе’ҢжўҜеәҰж¶ҲеӨұ
• 306.GradientClipping
• 307.LSTMзҡ„д»Ӣз»Қ
• 308.LSTMзҡ„еә”з”Ё
• 309.Bi-DirectionalLSTM
• 310.GatedRecurrentUnit
• 311.й—®зӯ”зі»з»ҹи®Іи§Ј01
• 312.й—®зӯ”зі»з»ҹи®Іи§Ј02
• 313.RepresentationLearning
• 314.Whatmakesgoodrepresentation-01
• 315.Whatmakesgoodrepresentation-02
• 316.Whatmakesgoodrepresentation-03
• 317.WhyDeep
• 318.WhyDeepLearningHardtoTrain
• 319.WaystoSolveTraining
• 320.Dropoutд»Ӣз»Қ
• 321.дёәд»Җд№ҲDropoutйҳІжӯўиҝҮжӢҹеҗҲзҺ°иұЎ
• 322.жңәеҷЁзҝ»иҜ‘
• 323.MultimodalLearning
• 324.Seq2SeqжЁЎеһӢ
• 325.Seq2Seqи®ӯз»ғд»Ӣз»Қ
• 326.InferenceDecoding
• 327.ExhausticSearch
• 328.BeamSearch
• 329.еӣһйЎҫMultimodalLearning
• 330.AttentionжіЁж„ҸеҠӣжңәеҲ¶д»Ӣз»Қ
• 331.зңӢеӣҫиҜҙиҜқд»Ӣз»Қ
• 332.еӣҫеғҸиҜҶеҲ«зҡ„жіЁж„ҸеҠӣжңәеҲ¶
• 333.еҹәдәҺGANеҸҠејәеҢ–еӯҰд№ зҡ„ж–Үжң¬з”ҹжҲҗ-01
• 334.еҹәдәҺGANеҸҠејәеҢ–еӯҰд№ зҡ„ж–Үжң¬з”ҹжҲҗ-02
• 335.еӣһйЎҫSeq2SeqжЁЎеһӢ
• 336.Seq2Seqзҡ„Attention
• 337.Self-Attention1
• 338.Self-Attention2
• 339.ж·ұеәҰж–Үжң¬еҢ№й…Қ-01
• 340.ж·ұеәҰж–Үжң¬еҢ№й…Қ-02
• 341.еӣһйЎҫAttention
• 342.RNNLSTM-basedmodels
• 343.Transformerзҡ„з»“жһ„
• 344.EachEncoderBlock
• 345.Self-Attention
• 346.AddNormalize
• 347.BERTжҰӮеҝө
• 348.еӣһйЎҫLanguagemodel
• 349.maskedLanguagemodel
• 350.maskedLanguagemodelеӯҳеңЁзҡ„й—®йўҳ
• 351.LSTM
• 352.BERTи®ӯз»ғиҝҮзЁӢ
• 353.PGMйўҶеҹҹ
• 354.дё»йўҳжЁЎеһӢ
• 355.еӣһйЎҫдёҚеҗҢжЁЎеһӢзҡ„иҢғз•ҙModelEstimation
• 356.йў„жөӢзҡ„иҝҮзЁӢ
• 357.GDпјҢSGDпјҢAdagradз®—жі•
• 358.еӣһйЎҫLDA
• 359.дёҫдҫӢиҜҙжҳҺз”ҹжҲҗзҡ„иҝҮзЁӢ
• 360.д»Һе®ҳж–№зҡ„и§’еәҰи®Іи§Јз”ҹжҲҗзҡ„иҝҮзЁӢ
• 361.ОұеҲ°Оёiзҡ„з”ҹжҲҗ
• 362.дёҫдҫӢиҜҙжҳҺз”ҹжҲҗж–Үз«
• 363.gibbssampler
• 364.collapsedgibbssampling-01
• 365.collapsedgibbssampling-02
• 366.collapsedgibbssampling-03
• 367.collapsedgibbssampling-04
• 368.collapsedgibbssampling-05
• 369.жҺЁеҜјиҝҮзЁӢ01
• 370.жҺЁеҜјиҝҮзЁӢ02
• 371.жҺЁеҜјиҝҮзЁӢ03
• 372.GibbsйҮҮж ·01
• 373.GibbsйҮҮж ·02
• 374.Web-ScaleInformationExtractioninKnowItAll-01
• 375.Web-ScaleInformationExtractioninKnowItAll-02
• 376.ж ёеҮҪж•°
• 377.зӣҙж’ӯ-01
• 378.зӣҙж’ӯ-02
• 379.зӣҙж’ӯ-03
• 380.зӣҙж’ӯ-04
• 381.зӣҙж’ӯ-05
• 382.зӣҙж’ӯ-06
• 383.зӣҙж’ӯ-07
• 384.зӣҙж’ӯ-01
• 385.зӣҙж’ӯ-02
• 386.зӣҙж’ӯ-03
• 387.зӣҙж’ӯ-04
• 388.зӣҙж’ӯ-05
• 389.зӣҙж’ӯ-06
• 390.еҲ©з”ЁCRFжЁЎеһӢеҒҡе‘ҪеҗҚе®һдҪ“иҜҶеҲ«-01
• 391.еҲ©з”ЁCRFжЁЎеһӢеҒҡе‘ҪеҗҚе®һдҪ“иҜҶеҲ«-02
• 392.еҹәдәҺиҜӯж–ҷеә“и®ӯз»ғGloveиҜҚеҗ‘йҮҸжЁЎеһӢ-01
• 393.еҹәдәҺиҜӯж–ҷеә“и®ӯз»ғGloveиҜҚеҗ‘йҮҸжЁЎеһӢ-02
• 394.GMM-01
• 395.GMM-02
• 396.GMM-03
• 397.XLNet-BertAutoregressiveLM
• 398.ж”№иҝӣжҖқи·Ҝ
• 399.Bertзҡ„зӣ®ж ҮеҮҪж•°
• 400.permutation
• 401.pytorchе®һзҺ°skip-gram
• 402.AirbnbпјҲKDD2018bestPaperпјү-01
• 403.AirbnbпјҲKDD2018bestPaperпјү-02
• 404.зӣҙж’ӯ-01
• 405.зӣҙж’ӯ-02
• 406.зӣҙж’ӯ-03
• 407.зӣҙж’ӯ-04