In this video, Emmanuel Henri goes through an exploration of TensorFlow major concepts and terms.
Tensors are immutable, therefore, the values we assign to a tensor can't be changed. Then you have variables, which are a set of values that you can assign and can be changed. And if you need to set values that will be changed as you train your models, variables are best. Operations, or ops, are functions that allow you to manipulate data stored by tensors or variables. It provides all kinds of machine learning and linear algebra operations to explore your data.
As mentioned before, if applied to tensors, you can't change this data. Models allow you to use operations to create an output. In other words, this is where you manage to come up with your machine learning or deep learning results. You grab the data off your tensors, and through a function get an output, which is the result of your machine learning or deep learning model. In this case, we output 24 with an input of two. So to summarize all these terms, you feed data to your tensors or variables, and then build a model with operations to come up with a machine learning or deep learning result.
This is when it all comes together.
- Using TensorFlow
- Machine learning (ML) basics
- Creating a project with TensorFlow
- Working with tensors and variables
- TensorFlow ML operations
- Working with models and layers
- Importing a project
- Exploring datasets
- Training a model
- Using Python-based models in JS
- Converting SavedModel to web