Implement Boston housing price prediction problem by Linear regression using Deep Neural network

Problem Statement:

 Linear regression by using Deep Neural network: Implement Boston housing price prediction problem by Linear regression using Deep Neural network. Use the Boston House price prediction dataset. 

Implementation:

1. Open https://colab.research.google.com/

2. click on the new notebook.

3. Import all the libraries

4. Read the dataset from the path

5. Do all basic operations on dataset.

6. Exploratory Data Analysis:

7. Training a Linear Regression Model

8. Train Test Split:

9. Creating and Training the Model:

10. Predictions from our Model:

11. Model Evaluation:

Hyperparameter

 coming soon....

Loss Function

 Coming soon ...

Activation Function

 coming soon...

What is a Perceptron?

 

Syllabus
of
DEEP NEURAL NETWORKS(DNN's)

Explain in detail the Biological Neuron.

Explain Perceptron in detail.

Applications of Deep Learning

 Q. Explain common architectural principles of deep networks.

 Q. List the Application of Deep Learning.

Working-Application of DL

Q. What is the Learning representation of Data? or Feature learning or Representation Learning?

Q. How does deep learning work in three figures explain with an example.

What is Deep Learning ?

Write a short note on the History of Deep Learning.

Define Deep Learning. Explain it's Pros and cons.

 What is Machine Learning?

 Why should we learn Machine Learning?

How does Machine Learning Work?

Explain types of Machine Learning?

Difference between supervised-unsupervised

What is Bias?

What is Variance?

What is underfitting?

What is Overfitting?

What is  Best fitting or idle Scenario?

What are Parameters?

What are Hyperparameters?

Write short note on: Full Training Pipeline.

Write short note on: under/over fitting Regularization.

Explain various Limitation of Machine Learning