Skip to content

Latest commit

 

History

History
83 lines (62 loc) · 5.01 KB

README.md

File metadata and controls

83 lines (62 loc) · 5.01 KB
Raw

Meets Specifications Excellent work overall! The project meets all the specifications. Optimized implementations, pythonic code. Please do share your experience, difficulties, or solutions, to any specific cases so that other students could benefit too, on the AI Nanodegree forum. Feel free to reach out, if you need any help or have difficulty understanding anything, our dedicated mentors are there to help. Keep up the good work and stay udacious :udacious:

PART 1: Data Student provides correct alternate feature sets: delta, polar, normalized, and custom. Student passes unit tests. Student provides a reasonable explanation for what custom set was chosen and why (Q1). Well done! All the unit tests passed, correct alternative feature set.

PART 2: Model Selection Student correctly implements CV, BIC, and DIC model selection techniques in “my_model_selectors.py”. Student code runs error-free in notebook, passes unit tests and code review of the algorithms. Student provides a brief but thoughtful comparison of the selectors (Q2). Good job in BIC, DIC implementation and using KFold as the SelectorCV split method and utility "combine_sequences". Implementation passed all unit tests, comparison of pros and cons of various model selectors is quite detailed and impressive 😄

PART 3: Recognizer Student implements a recognizer in “my_recognizer.py” which runs error-free in the notebook and passes all unit tests Student provides three examples of feature/selector combinations in the submission cells of the notebook. Student code provides the correct words within <60% WER for at least one of the three examples student provided. Student provides a summary of results and speculates on how to improve the WER. Well summarized results. I would encourage you to implement optional part4 and share results in the classroom with other students. 👏

Artificial Intelligence Engineer Nanodegree

Probabilistic Models

Project: Sign Language Recognition System

Install

This project requires Python 3 and the following Python libraries installed:

Notes:

  1. It is highly recommended that you install the Anaconda distribution of Python and load the environment included in the "Your conda env for AI ND" lesson.
  2. The most recent development version of hmmlearn, 0.2.1, contains a bugfix related to the log function, which is used in this project. In order to install this version of hmmearn, install it directly from its repo with the following command from within your activated Anaconda environment:
pip install git+https://github.com/hmmlearn/hmmlearn.git

Code

A template notebook is provided as asl_recognizer.ipynb. The notebook is a combination tutorial and submission document. Some of the codebase and some of your implementation will be external to the notebook. For submission, complete the Submission sections of each part. This will include running your implementations in code notebook cells, answering analysis questions, and passing provided unit tests provided in the codebase and called out in the notebook.

Run

In a terminal or command window, navigate to the top-level project directory AIND_recognizer/ (that contains this README) and run one of the following command:

jupyter notebook asl_recognizer.ipynb

This will open the Jupyter Notebook software and notebook in your browser which is where you will directly edit and run your code. Follow the instructions in the notebook for completing the project.

Additional Information

Provided Raw Data

The data in the asl_recognizer/data/ directory was derived from the RWTH-BOSTON-104 Database. The handpositions (hand_condensed.csv) are pulled directly from the database boston104.handpositions.rybach-forster-dreuw-2009-09-25.full.xml. The three markers are:

  • 0 speaker's left hand
  • 1 speaker's right hand
  • 2 speaker's nose
  • X and Y values of the video frame increase left to right and top to bottom.

Take a look at the sample ASL recognizer video to see how the hand locations are tracked.

The videos are sentences with translations provided in the database.
For purposes of this project, the sentences have been pre-segmented into words based on slow motion examination of the files.
These segments are provided in the train_words.csv and test_words.csv files in the form of start and end frames (inclusive).

The videos in the corpus include recordings from three different ASL speakers. The mappings for the three speakers to video are included in the speaker.csv file.