About this Course
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Intermediate Level

Approx. 17 hours to complete

Suggested: 11 hours/week...

English

Subtitles: English, Korean, Chinese (Simplified)

Skills you will gain

Recurrent Neural NetworkArtificial Neural NetworkDeep LearningLong Short-Term Memory (ISTM)

Course 1 of 1 in the

100% online

Start instantly and learn at your own schedule.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Intermediate Level

Approx. 17 hours to complete

Suggested: 11 hours/week...

English

Subtitles: English, Korean, Chinese (Simplified)

Syllabus - What you will learn from this course

Week
1
6 hours to complete

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 videos (Total 112 min), 4 quizzes
12 videos
Notation9m
Recurrent Neural Network Model16m
Backpropagation through time6m
Different types of RNNs9m
Language model and sequence generation12m
Sampling novel sequences8m
Vanishing gradients with RNNs6m
Gated Recurrent Unit (GRU)17m
Long Short Term Memory (LSTM)9m
Bidirectional RNN8m
Deep RNNs5m
1 practice exercise
Recurrent Neural Networks20m
Week
2
4 hours to complete

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 videos (Total 102 min), 3 quizzes
10 videos
Using word embeddings9m
Properties of word embeddings11m
Embedding matrix5m
Learning word embeddings10m
Word2Vec12m
Negative Sampling11m
GloVe word vectors11m
Sentiment Classification7m
Debiasing word embeddings11m
1 practice exercise
Natural Language Processing & Word Embeddings20m
Week
3
5 hours to complete

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 videos (Total 103 min), 3 quizzes
11 videos
Picking the most likely sentence8m
Beam Search11m
Refinements to Beam Search11m
Error analysis in beam search9m
Bleu Score (optional)16m
Attention Model Intuition9m
Attention Model12m
Speech recognition8m
Trigger Word Detection5m
Conclusion and thank you2m
1 practice exercise
Sequence models & Attention mechanism20m
4.8
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Top Reviews

By JYOct 30th 2018

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.

By JRMay 26th 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

Instructors

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Andrew Ng

CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
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Head Teaching Assistant - Kian Katanforoosh

Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
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Teaching Assistant - Younes Bensouda Mourri

Mathematical & Computational Sciences, Stanford University, deeplearning.ai
Computer Science

About deeplearning.ai

deeplearning.ai is Andrew Ng's new venture which amongst others, strives for providing comprehensive AI education beyond borders....

About the Deep Learning Specialization

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....
Deep Learning

Frequently Asked Questions

  • Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.

  • When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.

More questions? Visit the Learner Help Center.