Sequence Models for Time Series and Natural Language Processing
- 4.4
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Approx. 15 hours to complete
Course Summary
This course is designed to teach you how to build and train sequence models using TensorFlow and Google Cloud Platform.Key Learning Points
- Learn how to build and train sequence models using TensorFlow and Google Cloud Platform
- Understand how to use Recurrent Neural Networks (RNNs) to solve problems in natural language processing, speech recognition, and more
- Gain hands-on experience by working on real-world projects and building your own sequence models
Job Positions & Salaries of people who have taken this course might have
- USA: $120,000 - $200,000
- India: ₹800,000 - ₹2,000,000
- Spain: €35,000 - €60,000
- USA: $120,000 - $200,000
- India: ₹800,000 - ₹2,000,000
- Spain: €35,000 - €60,000
- USA: $100,000 - $160,000
- India: ₹600,000 - ₹1,500,000
- Spain: €30,000 - €50,000
- USA: $120,000 - $200,000
- India: ₹800,000 - ₹2,000,000
- Spain: €35,000 - €60,000
- USA: $100,000 - $160,000
- India: ₹600,000 - ₹1,500,000
- Spain: €30,000 - €50,000
- USA: $120,000 - $180,000
- India: ₹1,000,000 - ₹2,500,000
- Spain: €45,000 - €70,000
Related Topics for further study
Learning Outcomes
- Build and train your own sequence models using TensorFlow and Google Cloud Platform
- Understand how to use Recurrent Neural Networks to solve problems in natural language processing, speech recognition, and more
- Gain practical experience by working on real-world projects
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming
- Familiarity with machine learning concepts and terminology
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures
- Real-world projects
Similar Courses
- Deep Learning Specialization
- Natural Language Processing with Classification and Vector Spaces
- Applied Data Science with Python Specialization
Related Education Paths
Related Books
Description
This course is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length.
Outline
- Working with Sequences
- Course Introduction
- Getting Started with Google Cloud Platform and Qwiklabs
- Sequence data and models
- From sequences to inputs
- Modeling sequences with linear models
- Lab intro: using linear models for sequences
- Lab solution: using linear models for sequences
- Modeling sequences with DNNs
- Lab intro: using DNNs for sequences
- Lab solution: using DNNs for sequences
- Modeling sequences with CNNs
- Lab intro: using CNNs for sequences
- Lab solution: using CNNs for sequences
- The variable-length problem
- How to send course feedback
- Recurrent Neural Networks
- Introducing Recurrent Neural Networks
- How RNNs represent the past
- The limits of what RNNs can represent
- The vanishing gradient problem
- Dealing with Longer Sequences
- Introduction
- LSTMs and GRUs
- RNNs in TensorFlow
- Lab Intro: Time series prediction: end-to-end (rnn)
- Lab Solution: Time series prediction: end-to-end (rnn)
- Deep RNNs
- Lab Intro: Time series prediction: end-to-end (rnn2)
- Lab Solution: Time series prediction: end-to-end (rnn2)
- Improving our Loss Function
- Demo: Time series prediction: end-to-end (rnnN)
- Working with Real Data
- Lab Intro: Time Series Prediction - Temperature from Weather Data
- Lab Solution: Time Series Prediction - Temperature from Weather Data
- Summary
- Text Classification
- Working with Text
- Text Classification
- Selecting a Model
- Lab Intro: Text Classification
- Lab Solution: Text Classification
- Python vs Native TensorFlow
- Demo: Text Classification with Native TensorFlow
- Summary
- Reusable Embeddings
- Historical methods of making word embeddings
- Modern methods of making word embeddings
- Introducing TensorFlow Hub
- Lab Intro: Evaluating a pre-trained embedding from TensorFlow Hub
- Lab Solution: TensorFlow Hub
- Using TensorFlow Hub within an estimator
- Encoder-Decoder Models
- Introducing Encoder-Decoder Networks
- Attention Networks
- Training Encoder-Decoder Models with TensorFlow
- Introducing Tensor2Tensor
- Lab Intro: Cloud poetry: Training custom text models on Cloud AI Platform
- Lab Solution: Cloud poetry: Training custom text models on Cloud AI Platform
- AutoML Translation
- Dialogflow
- Lab Intro: Introducing Dialogflow
- Lab Solution: Dialogflow
- Summary
- Summary
- Additional Reading
Summary of User Reviews
This course on sequence models in TensorFlow and GCP has received positive reviews from users, with many praising its comprehensive content and practical approach.Key Aspect Users Liked About This Course
The practical approach of the course has been noted as a key aspect that many users found good.Pros from User Reviews
- Comprehensive content that covers a range of topics in sequence models
- Hands-on exercises and assignments that allow for practical application of concepts
- Clear and engaging instruction from the course instructor
- Good balance between theory and practical application
- Well-structured course materials that are easy to follow
Cons from User Reviews
- Some users found the course challenging and may require additional resources or support
- A few users noted that the course may be too advanced for beginners
- Limited interaction with other students in the course
- Some users found the pace of the course to be too fast
- A few technical issues were reported by some users during the course
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