https://github.com/vikas-ukani/udacity_intro_to_tensorflow_for_deep_learning
The Complete TensorFlow Course From Udacity For TensorFlow Deep Learning ...
https://github.com/vikas-ukani/udacity_intro_to_tensorflow_for_deep_learning
Last synced: 3 days ago
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The Complete TensorFlow Course From Udacity For TensorFlow Deep Learning ...
- Host: GitHub
- URL: https://github.com/vikas-ukani/udacity_intro_to_tensorflow_for_deep_learning
- Owner: vikas-ukani
- Created: 2020-10-06T13:07:47.000Z (about 5 years ago)
- Default Branch: main
- Last Pushed: 2020-11-06T10:16:25.000Z (almost 5 years ago)
- Last Synced: 2025-10-11T13:32:05.456Z (3 days ago)
- Language: Jupyter Notebook
- Size: 52.6 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Udacity Introduction to TensorFlow For Deep Learning.
## Intro to Machine Learning
- This class will teach you the end-to-end process of investigating data through a machine learning lens, and you'll apply what you've learned to a real-world data set.
### Lesson 1 - Welcome to Machine Learning
- Meet with Sebastian and Katie to discuss machine learning.
### Lesson 2 - Navie Bayes
- Learn about classification, training and testing, and run a naive Bayes classifier using Scikit Learn.
### Lesson 3 - SVM
- Build an intuition about how support vector machines (SVMs) work and implement one using scikit-learn.
### Lesson 4 - Decision trees
- Learn about how the decision tree algorithm works, including the concepts of entropy and information gain.
### Lesson 5 - Choose you own Model
- In this mini project, you will extend your toolbox of algorithms by choosing your own algorithm to classify terrain data, including k-nearest neighbors, AdaBoost, and random forests.