Deep Learning Framework :
TensorFlow is a computational framework used to build Machine Learning models.
Mobile Deployments :
TensorFlow is good for mobile deployments as it provides easier understanding of data flow in the framework.
Google's Brainchild
Actively developed by all the awesome people at Google.
High Performance
TensorFlow serving is well known for being used for server-side deployments.
Linear Regression
TensorFlow is a computational framework used to build Machine Learning models.
Mobile Deployments :
TensorFlow is good for mobile deployments as it provides easier understanding of data flow in the framework.
Google's Brainchild
Actively developed by all the awesome people at Google.
High Performance
TensorFlow serving is well known for being used for server-side deployments.
Linear Regression
- Statistics :- Modelling the relationship between a scalar dependent variable and one or more explanatory variables
- Technique :- Linear Regression is a statistical used to measure the relationship between the variables
- Let's have an Example :- A simple Linear Regression can be:
y = w * x + b
Rock Or a mine prediction :
You have been hired by US navy to create a model, that can detect the difference between a mine and a rock.
A noval mine is a self-contained explosive device placed in water to damage or destroy surface ships or submarines.
How to create this model ?
Start → Read the Datasets → Define features → Encode the dependent variable → Divide the datasets into two parts for training and testing → TensorFlow data structure for holding features, labels etc. → Implement the model → Train the model → Reduce MSE (actual output - desired output) → Make prediction on the test data. → End.
Tensors are the standard way of representing data in TensorFlow (deep learning).
Tensors are multidimensional arrays, an extension of two-dimensional tables (matrices) to data with higher dimension.
No comments:
Post a Comment