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

## A. Training vs. evaluation
To measure how well our model has been trained, we evaluate it on datasets other than the training set. The datasets used for evaluation are known as the *validation* and *test* sets. Note that we don't shuffle or repeat the evaluation datasets, since those are techniques used specifically to improve training.

The validation set is used to evaluate a model in between training runs. We use the validation set to tweak certain hyperparameters for a model (such as learning rate or batch size) in order to make sure training continues smoothly. We also use the validation set to detect model [overfitting](https://en.wikipedia.org/wiki/Overfitting#Machine_learning), so we can stop training if overfitting is detected.

Overfitting occurs when we train a model (usually a relatively complex model) for too long on the training set, resulting in a decreased ability to generalize well to other datasets. In the above plot, overfitting occurs around the $32^{nd}$ epoch of training, when the loss on the validation set begins to increase.

The test set is used to evaluate the final version of a model, after it is completely done training. Evaluating on the test set lets us know how well our model performs on its given task.

# A. Training vs. evaluation
To measure how well our model has been trained, we evaluate it on datasets other than the training set. The datasets used for evaluation are known as the *validation* and *test* sets. Note that we don't shuffle or repeat the evaluation datasets, since those are techniques used specifically to improve training.

The validation set is used to evaluate a model in between training runs. We use the validation set to tweak certain hyperparameters for a model (such as learning rate or batch size) in order to make sure training continues smoothly. We also use the validation set to detect model [overfitting](https://en.wikipedia.org/wiki/Overfitting#Machine_learning), so we can stop training if overfitting is detected.

Learn how to evaluate a pre-trained model stored in a checkpoint.

What you'll learn in this course

Data Pipeline

Model Execution

Evaluation

A. Training vs. evaluation