Cette formation vous apprendra 脿 construire des mod猫les pour le langage naturel, l鈥檃udio et les autres donn茅es de s茅quence. Gr芒ce 脿 l鈥檃pprentissage profond, les algorithmes de s茅quence fonctionnent beaucoup mieux qu鈥檌l y a deux ans ; nous disposons donc de nombreuses applications tr猫s int茅ressantes en mati猫re de reconnaissance vocale, de synth猫se musicale, de chatbots, de traduction automatique, de compr茅hension naturelle du langage, etc.
Mod猫les de s茅quence
Gain insight into a topic and learn the fundamentals.
Intermediate level
Some related experience required
15 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace
Details to know
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Assessments
3 assignments
Taught in French
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There are 3 modules in this course
D茅couvrez les r茅seaux neuronaux r茅currents. Ce type de mod猫le s鈥檈st av茅r茅 extr锚mement performant sur les donn茅es temporelles. Il comporte plusieurs variantes, y compris les LSTM, les GRU et les RNN bidirectionnels, que vous allez d茅couvrir dans cette section.
What's included
12 videos2 readings1 assignment3 programming assignments3 ungraded labs
Le traitement du langage naturel avec l'apprentissage profond est une combinaison importante. En utilisant des repr茅sentations de vecteurs de mots et des couches de prolongements, vous pouvez former des r茅seaux neuronaux r茅currents avec des performances exceptionnelles, dans une grande vari茅t茅 de secteurs. Des exemples d鈥檃pplications sont l鈥檃nalyse de sentiments, la reconnaissance d鈥檈ntit茅s nomm茅es et la traduction automatique.
What's included
10 videos1 reading1 assignment2 programming assignments2 ungraded labs
Les mod猫les de s茅quence peuvent 锚tre am茅lior茅s 脿 l鈥檃ide d鈥檜n m茅canisme d鈥檃ttention. Cet algorithme aidera votre mod猫le 脿 comprendre o霉 celui-ci doit focaliser son attention, compte tenu d鈥檜ne s茅quence d鈥檈ntr茅es. Cette semaine, vous apprendrez 茅galement 脿 reconna卯tre la parole et 脿 g茅rer les donn茅es audio.
What's included
11 videos3 readings1 assignment2 programming assignments2 ungraded labs
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