Madhurig at 14:38, 17 July 2025

2025-07-17T14:38:46Z

Madhurig at 09:49, 16 July 2025

2025-07-16T09:49:52Z

Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- || Show Slide: '''Welcome and Title Slide''' || Welcom..."

2025-07-03T12:18:34Z

Created page with " <div style="margin-left:1.27cm;margin-right:0cm;"></div> {| border="1" |- || '''Visual Cue''' || '''Narration''' |- || Show Slide: '''Welcome and Title Slide''' || Welcom..."

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|| '''Visual Cue'''
|| '''Narration'''
|-
|| Show Slide: '''Welcome and Title Slide'''
|| Welcome to the Spoken Tutorial on''' Artificial Neural Networks'''
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|| Show Slide:

'''Learning Objectives'''
|| In this tutorial, we will learn about
* <div style="margin-left:1.27cm;margin-right:0cm;">The fundamentals of '''Artificial Neural Networks (ANN)'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Implementing '''Multi- Layer Perceptron (MLP)''' classification </div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Evaluating the performance of a trained '''MLP''' model</div>

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|| Show Slide:
|| To record this tutorial, I am using
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Ubuntu Linux 24.04'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Jupyter Notebook IDE'''</div>

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|| Show Slide:
|| To follow this tutorial,

The learner must have basic knowledge of '''Python.'''

For pre-requisite Python tutorials, please visit this website.
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|| Show Slide:

Code-Files
||
* <div style="margin-left:1.27cm;margin-right:0cm;">The files used in this tutorial are provided in the '''Code files '''link.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Please download and extract the files.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Make a copy and use them while practicing.</div>

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|| Show Slide:

'''Artificial Neural Network'''
||
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Artificial Neural Networks''' are models inspired by the human brain’s processing.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Neurons''' receive '''inputs''', adjust '''weights''', and pass''' outputs''' to other neurons.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''ANNs '''have '''input''', '''hidden''', and '''output''' layers to process and learn patterns.</div>

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|| Show Slide:

'''Artificial Neural Network'''
||
* <div style="margin-left:1.27cm;margin-right:0cm;">Each connection in '''ANN''' has a '''weight''' that helps it to make better predictions.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''ANNs''' learn by adjusting '''weights '''based on''' errors''' to improve accuracy.</div>
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|| Show Slide:

'''Multi-Layer Perceptron'''
||
* <div style="margin-left:1.27cm;margin-right:0cm;">'''MLP''' is a type of '''ANN''' with '''multiple hidden layers''' that enhance feature learning.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">It processes data through '''interconnected neurons''' in a forward direction.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''MLP''' is widely used for '''classification''' and''' regression''' tasks in machine learning.</div>

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|| Show Slide:

'''ANN Architecture'''

'''arch.png'''
|| Let’s look at the architecture of an '''Artificial Neural Network'''.

'''The Input Layer''' receives the data from the dataset.The number of input neurons matches the number of input features.'''Hidden Layers''' process these inputs through weighted connections.These layers help the network learn complex patterns.The number of hidden '''neurons''' depends on task complexity.
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|| Show Slide:

'''ANN Architecture'''

'''arch.png'''
|| '''The Output Layer''' produces the final prediction or classification.The number of '''output neurons''' matches the number of output classes.'''Weights''' are updated during training to optimize performance.
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|| Show Slide:

'''Artificial Neuron Model'''

'''neuron.png'''
|| Now, let's break this down further by understanding a '''single neuron'''.

Each''' neuron '''receives inputs along with a''' bias '''value.

Each input has a '''weight''' that determines its importance.

The '''summation function''' adds the '''weighted inputs''' and '''bias'''.

The '''activation function''' decides the '''neuron’s''' output.

The output is then passed to the next layer in the network.
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|| Hover over the files
|| I have created Artificial neural networks. Ipynb file for the demonstration."
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|| Press '''Ctrl+Alt+T '''keys

Type '''conda activate ml'''

Press '''Enter'''
|| Let us open the Linux terminal by pressing '''Ctrl, Alt and T '''keys together.

Activate the machine learning environment as shown.
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|| To go to the '''Downloads '''folder,

Type '''cd Downloads'''

Type '''jupyter notebook'''
|| I have saved my code file in the '''Downloads '''folder.

Please navigate to the respective folder of your code file location.

Then type, '''jupyter space notebook '''and press '''Enter.'''
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|| Show Jupyter Notebook Home Page:Click on

'''Artificial Neural Networks.ipynb''' file
|| We can see the Jupyter Notebook Home page has opened in the web browser.

Click the''' Artificial neural networks dot ipynb''' file to open it.

<div style="color:#000000;">Note that each cell will have the output displayed in this file.</div>
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|| Highlight

'''import pandas as pd'''
'''import numpy as np'''
'''import matplotlib.pyplot as plt'''
'''import seaborn as sns'''
|| First, we import the necessary libraries for ANN.

Make sure to Press '''Shift '''and '''Enter '''to execute the code in each cell.

We will use the '''Breast Cancer Wisconsin''' dataset from '''sklearn''' library.

The dataset has '''30 features''' describing breast tumor characteristics.

The target variable is '''0''' for '''malignant tumors '''and '''1''' for '''benign tumors'''.

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|| Highlight

'''data = load_breast_cancer()'''
|| We load the dataset using the '''load underscore breast underscore cancer''' function.

Then, we create a dataframe using a '''pd dot dataframe''' for data handling.
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'''bcancer_df.tail() '''
|| To inspect the dataset, we display the last five rows using the '''tail''' function.
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|| Only narration

Highlight

'''bcancer_df.shape'''
|| The''' shape''' function returns the number of rows and columns in the dataframe.
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'''print("Feature names:", data.feature_names)'''
|| '''data dot feature underscore names''' displays the columns of different '''tumor '''characteristics.
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'''print("Target names:", data.target_names)'''

|| Next we display the class labels of the target variable.
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'''plt.figure(figsize=(12,6)) '''
'''plt.show()'''
|| We create a boxplot to compare '''mean radius''' across classes.

'''Malignant''' tumors are shown in red and '''benign''' tumors in green.
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|| Show output
|| Whiskers in a box plot represent the range of data within a certain boundary.

Outliers are values far from the rest of the data and appear as small dots.

In this plot, the '''malignant''' class has a higher mean radius with more variation.

The '''benign''' class has a lower mean radius with fewer outliers.
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|| Only Narration

Highlight

'''scaler = RobustScaler() '''
'''df_scaled = pd.DataFrame( '''
'''scaler.fit_transform(bcancer_df.iloc[:, :-1]), '''
|| Now, let’s preprocess the dataset.

To handle outliers, we normalize the features using '''Robust Scaler'''. It scales values based on the '''median''' and '''IQR''', making it resistant to '''outliers'''.

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|| Highlight

'''X = bcancer_df.drop(columns=["target"]) '''
'''y = bcancer_df['target']'''
|| We define '''X''' as the feature set by dropping the '''target''' column.

The '''y''' variable stores the '''target classes''' for classification.
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'''X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)'''
|| Next, we split the data into '''training '''and '''testing '''sets.
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'''mlp_relu = MLPClassifier(hidden_layer_sizes=(100, 50), activation='relu', max_iter=1000, random_state=42) '''

'''mlp_relu.fit(X_train, y_train)'''
|| We then initialize the '''MLP''' model for classification.

Using the''' MLPClassifier''' function, we define '''mlp underscore relu'''.

It has '''two hidden layers''', the first with '''100 neurons '''and second with '''50 neurons'''.

The model uses the '''Rectified Linear Unit '''i.e '''ReLU activation function''' for faster convergence.

'''ReLU '''helps the '''MLP''' to train effectively.

The model is trained for a maximum of '''1000 iterations''' to optimize performance.
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'''y_train_pred_relu = mlp_relu.predict(X_train)'''
|| Once trained, we predict class labels for''' X_train''' using our model.
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'''print("Training Accuracy (ReLU):", format(accuracy_score(y_train, y_train_pred_relu), ".3f")) '''
|| We then calculate the '''training accuracy''' using the '''accuracy underscore score'''.

The result is formatted to three decimal places and printed.
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|| Show output
|| The MLP model achieves a training accuracy of '''92.2%''' using ReLU activation.

This indicates that the model has learned well from the training data.
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|| Highlight

'''plt.plot(mlp_relu.loss_curve_, label="Training Loss", color="blue") '''

'''plt.xlabel("Iterations") '''

'''plt.ylabel("Loss") '''
|| We plot the '''training loss curve''' to track the learning progress.

The '''x-axis''' represents''' iterations,''' while the y-axis shows the '''loss value'''.

A decreasing '''loss curve''' indicates effective training.
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|| Show output
|| The''' training loss curve''' shows how the model's''' error''' decreases over iterations.

Initially, the '''loss''' is high but quickly drops, showing rapid learning.

After 20 iterations, the loss stabilizes, indicating model '''convergence'''.

Model '''convergence''' means training has optimized weights, with minimal further gain.
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|| Highlight

'''y_pred_relu = mlp_relu.predict(X_test) '''

'''accuracy_relu = accuracy_score(y_test, y_pred_relu) '''
|| Next, we predict the class labels on the test data.

We calculate and print the testing accuracy.
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|| Show output
|| The model achieves a testing accuracy of '''95.9%'''.

This indicates strong generalization to unseen data.
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|| Highlight

'''y_probs = mlp_relu.predict_proba(X_test)[:, 1] '''

'''fpr, tpr, _ = roc_curve(y_test, y_probs) '''

'''roc_auc = auc(fpr, tpr) '''

|| We further evaluate the performance using a ROC curve.

The curve shows the balance between '''true positive rate''' and '''false positive rate'''.
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|| Show output
|| The''' Area under the curve''' i.e, '''AUC value of 0.99''' confirms the model’s strong classification ability.
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|| Highlight

'''print("\nMLP with ReLU activation - Classification Report:") '''
|| Finally, we display the '''classification report''' of the model.

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|| Show output
|| The output indicates that the model performs well in both classes with above 95% precision.

The other metrics like recall and F1-score suggest that it correctly identifies most positive instances.

Thus, the model has learnt to classify whether the patient has breast cancer or not.
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|| Show Slide:'''Summary'''
|| This brings us to the end of the tutorial. Let us summarize.

In this tutorial, we have learnt about
* <div style="margin-left:1.27cm;margin-right:0cm;">The fundamentals of '''Artificial Neural Networks (ANN)'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Implementing '''Multi- Layer Perceptron (MLP)''' classification </div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Evaluating the performance of a trained '''MLP''' model</div>

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|| Show Slide:

'''Assignment '''
|| As an assignment, please do the following
* <div style="margin-left:1.27cm;margin-right:0cm;">Use''' activation='logistic' '''instead of '''activation='relu''''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Evaluate the performance using accuracy and classification report for test set</div>

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|| Show Slide:

'''Assignment Solution'''
|| After execution, we should get the accuracy and classification report as shown here.
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|| Show Slide:

'''FOSSEE Forum'''
|| <span style="background-color:#ffffff;">For any general or technical questions on </span>'''Python for'''

'''Machine Learning'''<span style="background-color:#ffffff;">, visit the</span><span style="background-color:#ffffff;">''' FOSSEE forum'''</span><span style="background-color:#ffffff;"> and post your question.</span>
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|| Show Slide:

'''Thank You'''
|| This is '''Anvita Thadavoose Manjummel''', a FOSSEE Summer Fellow 2025, IIT Bombay signing off

Thanks for joining.
|-
|}

@@ Line 109: / Line 109: @@
 |-
 || Hover over the files
-|| I have created Artificial neural networks. Ipynb file for the demonstration."
+|| I have created '''Artificial neural networks. Ipynb''' file for the demonstration.
 |-
 || Press '''Ctrl+Alt+T '''keys
@@ Line 134: / Line 134: @@
 '''Artificial Neural Networks.ipynb''' file
-|| We can see the Jupyter Notebook Home page has opened in the web browser.
+|| We can see the '''Jupyter Notebook Home''' page has opened in the web browser.
 Click the''' Artificial neural networks dot ipynb''' file to open it.

Python-for-Machine-Learning/C3/Artificial-Neural-Networks/English - Revision history

Madhurig at 14:38, 17 July 2025

Madhurig at 09:49, 16 July 2025

Nirmala Venkat: Created page with " {| border="1" |- || '''Visual Cue''' || '''Narration''' |- || Show Slide: '''Welcome and Title Slide''' || Welcom..."

Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- || Show Slide: '''Welcome and Title Slide''' || Welcom..."