Linear Regression Tutorial using Tensorflow and Keras

We introduce the topic of linear regression in the context of a simple neural network. we also show how Keras can be used to model and train the network, to learn the parameters of the linear model and how to visualize the model predictions.

Before studying deep neural networks, we will cover the fundamental components of a simple (linear) neural network. We’ll begin with the topic of linear regression. Since linear regression can be modeled as a neural network, it provides an excellent example to introduce the essential components of neural networks.

Regression is a form of supervised learning which aims to model the relationship between one or more input variables (features) and a continuous (target) variable. We assume that the relationship between the input variables x and the target variable y can be expressed as a weighted sum of the inputs (i.e., the model is linear in the parameters). In short, linear regression aims to learn a function that maps one or more input features to a single numerical target value.

Topics Covered
✅Dataset Exploration
✅Linear Regression Model
✅Neural Network Perspective and Terminology
✅Modeling a Neural Network in Keras

❓FAQ
What is Keras regression model?
Which Optimizer is best for linear regression?
Which deep learning model is best for regression?
How to do regression with Keras?
Can TensorFlow be used for linear regression?
Can neural networks do linear regression?
Which deep learning model is best for regression?
How to do regression with Tensorflow?

⭐️ Time Stamps:⭐️
0:00-00:16: Introduction
00:16-01:08: Neural Networks
01:08-02:19: Boston Housing Dataset
02:19-02:40: Printing out the features
02:40-03:27: Data Splitting
03:27-05:51: Explanation
05:40-08:51: Creating a Model using a Neural Network
08:51-12:40: Code implementation
12:40-14:14: Output from the Fit method
14:14-15:28: Predict Method
15:28-17:56: Plot data
17:56-18:21: Conclusion

Resources:

Processing, Image Classification, Object Detection, Face Detection, Face Recognition, YOLO, Segmentation, Pose Estimation, and many more using OpenCV(Python/C++), PyTorch, and TensorFlow.

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[andreshofmann]

HEy! nice tutorial but I get an error when running the train model cell

[ryankusekwa]

Hey man, my loss function is always higher than my losses through a 100 epochs .What might be the problem...Im using an Insurance dataset and trying to predict the insurance costs based on BMI index of an individual.