Intro to TensorFlow for Deep Learning
- Offered byUDACITY
Intro to TensorFlow for Deep Learning at UDACITY Overview
Intro to TensorFlow for Deep Learning
at UDACITY
Upskilling is a better roadmap to success. Enroll in this course to learn critical principles of Machine Learning through real-life case studies & examples
Duration | 2 months |
Total fee | Free |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Intro to TensorFlow for Deep Learning at UDACITY Highlights
Intro to TensorFlow for Deep Learning
at UDACITY
- Start instantly and learn at your own schedule.
- Earn a certificate of learning on course completion
- Taught by Industry Pros
- Rich Learning Content
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Intro to TensorFlow for Deep Learning at UDACITY Course details
Intro to TensorFlow for Deep Learning
at UDACITY
Skills you will learn
Who should do this course?
- This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses.
What are the course deliverables?
- Discuss common regression, classification, and multilabel classification metrics
- Explain the use of linear and logistic regression in supervised learning applications
- Describe common strategies for grid searching and cross-validation
- Employ evaluation metrics to select models for production use
- Explain the use of tree-based algorithms in supervised learning applications
- Explain the use of Neural Networks in supervised learning applications
- Discuss the major variants of neural networks and recent advances
- Create a neural net model in Tensorflow
- Create and test an instance of Watson Visual Recognition
- Create and test an instance of Watson NLU
More about this course
- Learn how to build deep learning applications with TensorFlow. This course was developed by the TensorFlow team and Udacity as a practical approach to deep learning for software developers. You'll get hands-on experience building your own state-of-the-art image classifiers and other deep learning models. You'll also use your TensorFlow models in the real world on mobile devices, in the cloud, and in browsers. Finally, you'll use advanced techniques and algorithms to work with large datasets. By the end of this course, you'll have all the skills necessary to start creating your own AI applications.
Intro to TensorFlow for Deep Learning at UDACITY Curriculum
Intro to TensorFlow for Deep Learning
at UDACITY
Model Evaluation and Performance Metrics
Building Machine Learning and Deep Learning Models
Intro to TensorFlow for Deep Learning at UDACITY Entry Requirements
Intro to TensorFlow for Deep Learning
at UDACITY
Eligibility criteria
- It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.
Conditional Offer
- Not mentioned
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Intro to TensorFlow for Deep Learning
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