What Is an Edge Case?
January 31, 2024
Article
This course is part of Deep Learning Specialization
Instructors: Andrew Ng
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430,245 already enrolled
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Intermediate level
Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML
(30,552 reviews)
Recommended experience
Intermediate level
Intermediate Python skills: basic programming, understanding of for loops, if/else statements, data structures
A basic grasp of linear algebra & ML
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In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more.
By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering. The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career.
Discover recurrent neural networks, a type of model that performs extremely well on temporal data, and several of its variants, including LSTMs, GRUs and Bidirectional RNNs,
12 videos5 readings1 assignment3 programming assignments
Natural language processing with deep learning is a powerful combination. Using word vector representations and embedding layers, train recurrent neural networks with outstanding performance across a wide variety of applications, including sentiment analysis, named entity recognition and neural machine translation.
10 videos2 readings1 assignment2 programming assignments
Augment your sequence models using an attention mechanism, an algorithm that helps your model decide where to focus its attention given a sequence of inputs. Then, explore speech recognition and how to deal with audio data.
10 videos2 readings1 assignment2 programming assignments
5 videos5 readings1 assignment1 programming assignment3 ungraded labs
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
DeepLearning.AI is an education technology company that develops a global community of AI talent. DeepLearning.AI's expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future.
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Reviewed on Feb 14, 2020
One of the best thing from this class is not only we can understand the concept of RNN, LSTM, etc, but also I also get the idea about how these technique can be used in many daily life applications
Reviewed on Mar 3, 2018
Dr. Ng and team did a great job! Dr. Ng delivered even the most complicated concepts in the most lucid way possible. Assignments created by the team are awesome and very good to work on! 5/5 course!
Reviewed on Jul 12, 2020
brilliant course, great quality instruction from Andrew Ng. The only faults are that some of the labs have not been supervised properly being a but buggy and a couple of later lectures were very dry.
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