DATA-X:
m500 - Natural Language Processing and NLTK
Author List (in no particular order): Niki Collette, Elias Castro Hernandez, Ikhlaq Sidhu, Debbie Yuen, and Alexander Fred-Ojala
About (TL/DR): Todo
Learning Goal(s): todo
Associated Materials: todo
Keywords (Tags): , data-x, uc-berkeley-engineering
Prerequisite Knowledge: (1) Python, (2) NumPy, (3) Pandas,
Target User: Data scientists, applied machine learning engineers, and developers
Copyright: Content curation has been used to expedite the creation of the following learning materials. Credit and copyright belong to the content creators used in facilitating this content. Please support the creators of the resources used by frequenting their sites, and social media.
CONTENT
- m410_shallow_neural_networks_introduction_to_tensorflow -- Overview of TensorFlow syntax, operations, and execution.
- assets/homeworks/ -- Contains several exercises to help you master the material.
I. TENSORS AND OPERATIONS
1) PART 1.1: TENSORFLOW SETUP
2) PART 1.2: TENSORBOARD SETUP
3) PART 1.3: TENSORFLOW TENSORS
4) PART 1.4: TENSORFLOW OPERATIONS
5) PART 1.5 (OPTIONAL): EAGER EXECUTION
II. TENSORFLOW GRAPHS AND EXECUTIONS
1) PART 2.1: TENSORFLOW COMPUTATION FUNCTION -- \@tf.function
III. TENSORFLOW LINEAR REGRESSION (OPTIONAL)
1) PART 3.1: PROBLEM DEFINITION AND SETUP
2) PART 3.2: MODEL
3) PART 3.3: GENERALIZATION AND PREDICTIONS
IV. WRAP UP AND NEXT STEPS
You've completed the introduction to TensorFlow V.2, and once can assume that you are ready to get things done with your new knowledge. Visit the Data-X website to learn how to use Tensorflow to tackle various deep learning problems, or use the following links to some topics of interest:
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