Gain insights into industry-level machine learning by applying advanced TensorFlow and Python techniques. Explore efficient methods for creating scalable models, from data pipelines to deployment and inference.

AML-DLI.tar.gz

case-study

In this course, you'll level up your skills learned in the Industry Case Study and Machine Learning for Software Engineers. You'll take the modeling and data pipeline concepts and apply them to production-level classification and regression models for industry deployment, while continuing to practice the most efficient techniques for building scalable machine learning models. After this course, you will be able to complete industry-level machine learning projects, from data pipeline creation to model deployment and inference.

The code for this course is built around the TensorFlow framework, one of the premier frameworks for industry machine learning, and the Python pandas library for data analysis. Knowledge of Python and TensorFlow are prerequisites. 

This course was created by AdaptiLab, a company specializing in evaluating, sourcing, and upskilling enterprise machine learning talent. It is built in collaboration with industry machine learning experts from Google, Microsoft, Amazon, and Apple.

Applied Machine Learning: Deep Learning for Industry

## Chapter Goals:
- Learn about TensorBoard and how to track the progression of training values
- Specify training values to be shown in TensorBoard

## A. Training visualizations
When training a complex neural network, it is useful to have visualizations of the compuation graph and important values to make sure everything is correct. In TensorFlow, there is a tool known as [TensorBoard](https://www.tensorflow.org/guide/summaries_and_tensorboard), which lets us visualize all the important aspects of a model.

TensorBoard works by reading in an *events file*, which contains all the model data we want visualized. When training a model, the events file is stored in the same directory as the model *checkpoint* (which we'll discuss in the next chapter). The events file automatically contains the computation graph structure, as well as the loss and training speed (in iterations per second).

# Chapter Goals:
- Learn about TensorBoard and how to track the progression of training values
- Specify training values to be shown in TensorBoard

# A. Training visualizations
When training a complex neural network, it is useful to have visualizations of the compuation graph and important values to make sure everything is correct. In TensorFlow, there is a tool known as [TensorBoard](https://www.tensorflow.org/guide/summaries_and_tensorboard), which lets us visualize all the important aspects of a model.

Discover how TensorBoard can be used to visualize machine learning.

What you'll learn in this course

Data Pipeline

Model Execution

TensorBoard

Chapter Goals:

A. Training visualizations