tensorflow tensor-flow tensorflow-tutorials deep-learning deep-learning-with-python neural-networks data-x uc-berkeley-engineering

DATA-X:
m410 - SHALLOW NEURAL NETWORKS; AN INTRODUCTION TO TENSORFLOW V.2

Author List (in no particular order): Elias Castro Hernandez, Rajarathnam Balakrishnan, Ikhlaq Sidhu, Debbie Yuen, and Alexander Fred-Ojala

About (TL/DR): Tensorflow (TF) is an open-source library used for dataflow, differentiable programming, symbolic math ,and machine learning applications such as deep learning neural networks. TF's flexible architecture allows for easy deployment across varied processing platforms.

Learning Goal(s): This notebook covers advanced topics in machine learning. However, it does not require any prior knowledge in machine learning. The goal of this notebook is to teach a user how to deploy a TF model, as well as to provide the user guidance on how to tackle the more nuanced topics.

Associated Materials: To ease the learning curve, we encourage the user of this notebook to view the resources section on the main JupyterLab, and/or review the Data-X Fundamentals repo.

Keywords (Tags): tensorflow, tensor-flow, tensorflow-tutorial, deep-learning, deep-learning-with-python, neural-networks, data-x, uc-berkeley-engineering

Prerequisite Knowledge: (1) Python, (2) NumPy, (3) Pandas, (4) Linear Algebra

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

II. TENSORFLOW GRAPHS AND EXECUTIONS

1) PART 2.1: TENSORFLOW COMPUTATION FUNCTION -- \@tf.function

III. WRAP UP AND NEXT STEPS

You've completed the introduction to TensorFlow V.2, and one 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:

TODO (m--): url needed TODO TODO TODO.

About

DATA-X: m410 - TensorFlow - Shallow Neural Networks; An Introduction to TensorFlow V.2. Tensorflow (TF) is an open-source library used for dataflow, differentiable programming, symbolic math, and machine learning applications such as deep learning neural networks. TF's flexible architecture allows for easy deployment across varied processing platforms. This notebook covers advanced topics in machine learning. However, it does not require any prior knowledge in machine learning. The goal of this notebook is to teach a user how to deploy a TF model, as well as to provide the user guidance on how to tackle the more nuanced topics.

https://datax.berkeley.edu/