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function createModel() {
  // Create a sequential model
  const model = tf.sequential(); 
  
  // Add a single input layer
  model.add(tf.layers.dense({inputShape: [1], units: 1, useBias: true}));
  
  // Add an output layer
  model.add(tf.layers.dense({units: 1, useBias: true}));

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const model = createModel();  
tfvis.show.modelSummary({name: 'Model Summary'}, model);

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/**
 * Convert the input data to tensors that we can use for machine 
 * learning. We will also do the important best practices of _shuffling_
 * the data and _normalizing_ the data
 * MPG on the y-axis.
 */
function convertToTensor(data) {
  // Wrapping these calculations in a tidy will dispose any 
  // intermediate tensors.
  

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async function trainModel(model, inputs, labels) {
  // Prepare the model for training.  
  model.compile({
    optimizer: tf.train.adam(),
    loss: tf.losses.meanSquaredError,
    metrics: ['mse'],
  });
  
  const batchSize = 32;
  const epochs = 50;

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function testModel(model, inputData, normalizationData) {
  const {inputMax, inputMin, labelMin, labelMax} = normalizationData;  
  
  // Generate predictions for a uniform range of numbers between 0 and 1;
  // We un-normalize the data by doing the inverse of the min-max scaling 
  // that we did earlier.
  const [xs, preds] = tf.tidy(() => {
    
    const xs = tf.linspace(0, 1, 100);      
    const preds = model.predict(xs.reshape([100, 1]));      

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Why are you applying for Financial Aid?

I’m a student from India keen to learn about Machine Learning, Deep Learning, and TensorFlow. Since the quality of education in our institution is not up to the mark, the only way to get a viable career option in the future for me is to take this course. Since I am a student and our college does not permit a part-time job, I would not be able to carry the expenses to pay for the certificate of this course. Financial Aid will help me take this course without any adverse impact on my monthly essential needs. I am really excited for this course since it presents me with a great opportunity to grow my skills and become a professional in this field as I graduate in the same or prospective fields with a great resume.

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# Python

import numpy as np
a = np.arange(15).reshape(3, 5)

print(a.shape == (3, 5))        # True
print(a.ndim == 2)              # True
print(a.dtype.name)             # 'int64'

b = (np.arange(15) ** 2).reshape(3, 5)

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// Kotlin 

import org.jetbrains.numkt.*

fun main() {
    val a = arange(15).reshape(3, 5)
    
    println(a.shape.contentEquals(intArrayOf(3, 5))) // true
    println(a.ndim == 2)                             // true
    println(a.dtype)                                 // class java.lang.Integer

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```
We can also save the model parameters in a JSON file and load them back. 
In the example below, we will save the model coefficients and intercept and load them back.
We will first import the json library, create a dictionary containing the coefficients and intercept.
Coefficients and intercept are an array object. 
We cannot dump an array into JSON strings so we convert the array to a list and store it in the dictionary
```

import json
model_param = {}

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