This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others.
Ce cours fait partie de la Spécialisation Deep Learning
Sequence Models
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À propos de ce cours
678,417 consultations récentes
Résultats de carrière des étudiants
39%
ont commencé une nouvelle carrière après avoir terminé ce cours
40%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
13%
a obtenu une augmentation de salaire ou une promotion
Compétences que vous acquerrez
Recurrent Neural NetworkArtificial Neural NetworkDeep LearningLong Short-Term Memory (ISTM)
Résultats de carrière des étudiants
39%
ont commencé une nouvelle carrière après avoir terminé ce cours
40%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
13%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 5 sur 5 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 18 heures pour terminer
Recommandé : 12 hours/week
Anglais
Sous-titres : Chinois (traditionnel), Chinois (simplifié), Coréen, Anglais, Espagnol
Offert par
deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
Programme du cours : ce que vous apprendrez dans ce cours
6 heures pour terminer
Recurrent Neural Networks
Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section.
6 heures pour terminer
12 vidéos (Total 112 min), 2 lectures, 4 quiz
12 vidéos
Why sequence models2 min
Notation9 min
Recurrent Neural Network Model16 min
Backpropagation through time6 min
Different types of RNNs9 min
Language model and sequence generation12 min
Sampling novel sequences8 min
Vanishing gradients with RNNs6 min
Gated Recurrent Unit (GRU)17 min
Long Short Term Memory (LSTM)9 min
Bidirectional RNN8 min
Deep RNNs5 min
2 lectures
Gated Recurrent Unit (GRU) *CORRECTION*1 min
Long Short Term Memory (LSTM) *CORRECTION*1 min
1 exercices pour s'entraîner
Recurrent Neural Networks30 min
4 heures pour terminer
Natural Language Processing & Word Embeddings
Natural language processing with deep learning is an important combination. Using word vector representations and embedding layers you can train recurrent neural networks with outstanding performances in a wide variety of industries. Examples of applications are sentiment analysis, named entity recognition and machine translation.
4 heures pour terminer
10 vidéos (Total 102 min), 1 lecture, 3 quiz
10 vidéos
Word Representation10 min
Using word embeddings9 min
Properties of word embeddings11 min
Embedding matrix5 min
Learning word embeddings10 min
Word2Vec12 min
Negative Sampling11 min
GloVe word vectors11 min
Sentiment Classification7 min
Debiasing word embeddings11 min
1 lectures
GloVe word vectors *CORRECTION*1 min
1 exercices pour s'entraîner
Natural Language Processing & Word Embeddings30 min
5 heures pour terminer
Sequence models & Attention mechanism
Sequence models can be augmented using an attention mechanism. This algorithm will help your model understand where it should focus its attention given a sequence of inputs. This week, you will also learn about speech recognition and how to deal with audio data.
5 heures pour terminer
11 vidéos (Total 103 min), 3 lectures, 3 quiz
11 vidéos
Basic Models6 min
Picking the most likely sentence8 min
Beam Search11 min
Refinements to Beam Search11 min
Error analysis in beam search9 min
Bleu Score (optional)16 min
Attention Model Intuition9 min
Attention Model12 min
Speech recognition8 min
Trigger Word Detection5 min
Conclusion and thank you2 min
3 lectures
Bleu Score *CORRECTION*1 min
Corrections10 min
Instructions if you are unable to open your notebook10 min
1 exercices pour s'entraîner
Sequence models & Attention mechanism30 min
Avis
4.8
Meilleurs avis pour SEQUENCE MODELS
par PJApr 3, 2019
The previous courses raised the bar and expectations. The assignments for Week 1 and Week 2 were a bit unclear. Lectures for Week 1 and Week 2 can be improved as well. Besides, this is a great course!
par AMJun 30, 2019
The course is very good and has taught me the all the important concepts required to build a sequence model. The assignments are also very neatly and precisely designed for the real world application.
par ACJun 23, 2019
Very well produced and explained. In my case, the nature of the Sequence Model makes understanding the concepts and finishing the assignment more challenging than other segments of the specialization.
par SDSep 27, 2018
Great hands on instruction on how RNNs work and how they are used to solve real problems. It was particularly useful to use Conv1D, Bidirectional and Attention layers into RNNs and see how they work.
par NMFeb 20, 2018
Hope can elaborate the backpropagation of RNN much more. BP through time is a bit tricky though we do not need to think about it during implementation using most of existing deep learning frameworks.
par SBFeb 18, 2018
Loved the course - it was very interesting. It is also pretty complex, so will probably go through it again to review the concepts and how the models work. Thank you for this wonderful course series!
par SSJan 1, 2020
Learnt a lot about new concepts in RNN and LSTM. Really wanted to learn about these models. This course helped a lot. Everything was new and so fascinating. Loved this course and our teach Andrew NG.
par JRMay 25, 2019
I am so grateful that Andrew and the team provided such good course, I learn so much from this course, I am so excited that see the wake word detection model actually work in the programming exercise
À propos du Spécialisation Deep Learning
If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.
You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.
AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.
We will help you master Deep Learning, understand how to apply it, and build a career in AI.
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
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