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.
You will:
- Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
- Be able to apply sequence models to natural language problems, including text synthesis.
- Be able to apply sequence models to audio applications, including speech recognition and music synthesis.
This is the fifth and final course of the Deep Learning Specialization.
deeplearning.ai is also partnering with the NVIDIA Deep Learning Institute (DLI) in Course 5, Sequence Models, to provide a programming assignment on Machine Translation with deep learning. You will have the opportunity to build a deep learning project with cutting-edge, industry-relevant content.
Ce cours fait partie de la Spécialisation Deep Learning
Sequence Models
proposé par
122,638 inscrit(s)
À propos de ce cours
605,517 consultations récentes
Compétences que vous acquerrez
Recurrent Neural NetworkArtificial Neural NetworkDeep LearningLong Short-Term Memory (ISTM)
Programme du cours : ce que vous apprendrez dans ce cours
Semaine
16 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.
12 vidéos (Total 112 min), 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
1 exercice pour s'entraîner
Recurrent Neural Networks20 min
Semaine
24 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.
10 vidéos (Total 102 min), 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 exercice pour s'entraîner
Natural Language Processing & Word Embeddings20 min
Semaine
35 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.
11 vidéos (Total 103 min), 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
1 exercice pour s'entraîner
Sequence models & Attention mechanism20 min
4.8
1,548 avis37%
a commencé une nouvelle carrière après avoir terminé ces cours
38%
a bénéficié d'un avantage concret dans sa carrière grâce à ce cours
11%
a obtenu une augmentation de salaire ou une promotion
Meilleurs avis
The lectures covers lots of SOTA deep learning algorithms and the lectures are well-designed and easy to understand. The programming assignment is really good to enhance the understanding of lectures.
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.
Enseignants
Andrew Ng
CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
Head Teaching Assistant - Kian Katanforoosh
Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
Teaching Assistant - Younes Bensouda Mourri
Mathematical & Computational Sciences, Stanford University, deeplearning.aiComputer Science
À propos de deeplearning.ai
deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders.
À propos de la 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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