Merge pull request #6 from rutvikjoshi63/Development

Development

_pages/projects.md

@@ -2,13 +2,13 @@
 layout: page
 title: Projects
 permalink: /projects/
-description: A growing collection of your cool projects.
+description: A collection of Computer Science projects.
 nav: true
 nav_order: 2
-display_categories: [work, fun]
+display_categories: [work, Personal]
 horizontal: false
 ---
-
+<!-- Artificial Intelligence and Data Science -->
 <!-- pages/projects.md -->
 <div class="projects">
 {%- if site.enable_project_categories and page.display_categories %}

_posts/AndrewNg/2023-09-31-NeuralNetworkEvaluation.md

@@ -0,0 +1,161 @@
+---
+layout: post
+title: Neural Network Evaluation
+date: 2023-09-31 06:56:00-0400
+description: Study topics
+tags: Machine Learning Masters
+categories: sample-posts
+giscus_comments: true
+related_posts: false
+# related_publications: einstein1950meaning, einstein1905movement
+---
+
+## Topics covered in Supervised Machine Learning
+It is usually a good idea to perform feature scaling to help your model converge faster. This is especially true if your input features have widely different ranges of values.
+For that, you will use the [`StandardScaler`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) class from scikit-learn. This computes the z-score of your inputs. As a refresher, the z-score is given by the equation:
+
+$$ z = \frac{x - \mu}{\sigma} $$
+
+where $\mu$ is the mean of the feature values and $\sigma$ is the standard deviation. 
+# Initialize the class
+scaler_linear = StandardScaler()
+
+# Compute the mean and standard deviation of the training set then transform it
+X_train_scaled = scaler_linear.fit_transform(x_train)
+
+print(f"Computed mean of the training set: {scaler_linear.mean_.squeeze():.2f}")
+print(f"Computed standard deviation of the training set: {scaler_linear.scale_.squeeze():.2f}")
+
+### Train the model
+
+Next, you will create and train a regression model. For this lab, you will use the [LinearRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html) class but take note that there are other [linear regressors](https://scikit-learn.org/stable/modules/classes.html#classical-linear-regressors) which you can also use.
+# Initialize the class
+linear_model = LinearRegression()
+
+# Train the model
+linear_model.fit(X_train_scaled, y_train )
+
+### Evaluate the Model
+
+To evaluate the performance of your model, you will measure the error for the training and cross validation sets. For the training error, recall the equation for calculating the mean squared error (MSE):
+
+$$J_{train}(\vec{w}, b) = \frac{1}{2m_{train}}\left[\sum_{i=1}^{m_{train}}(f_{\vec{w},b}(\vec{x}_{train}^{(i)}) - y_{train}^{(i)})^2\right]$$
+
+Scikit-learn also has a built-in [`mean_squared_error()`](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html) function that you can use. Take note though that [as per the documentation](https://scikit-learn.org/stable/modules/model_evaluation.html#mean-squared-error), scikit-learn's implementation only divides by `m` and not `2*m`, where `m` is the number of examples. 
+# Feed the scaled training set and get the predictions
+yhat = linear_model.predict(X_train_scaled)
+
+# Use scikit-learn's utility function and divide by 2
+print(f"training MSE (using sklearn function): {mean_squared_error(y_train, yhat) / 2}")
+
+You can then compute the MSE for the cross validation set with basically the same equation. 
+* Say that your training set has an input feature equal to `500` which is scaled down to `0.5` using the z-score.
+* After training, your model is able to accurately map this scaled input `x=0.5` to the target output `y=300`. 
+* Now let's say that you deployed this model and one of your users fed it a sample equal to `500`. 
+* If you get this input sample's z-score using any other values of the mean and standard deviation, then it might not be scaled to `0.5` and your model will most likely make a wrong prediction (i.e. not equal to `y=300`). 
+
+You will scale the cross validation set below by using the same `StandardScaler` you used earlier but only calling its [`transform()`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler.transform) method instead of [`fit_transform()`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler.fit_transform).
+
+# Scale the cross validation set using the mean and standard deviation of the training set
+X_cv_scaled = scaler_linear.transform(x_cv)
+
+print(f"Mean used to scale the CV set: {scaler_linear.mean_.squeeze():.2f}")
+print(f"Standard deviation used to scale the CV set: {scaler_linear.scale_.squeeze():.2f}")
+
+# Feed the scaled cross validation set
+yhat = linear_model.predict(X_cv_scaled)
+
+# Use scikit-learn's utility function and divide by 2
+print(f"Cross validation MSE: {mean_squared_error(y_cv, yhat) / 2}")
+
+## Adding Polynomial Features
+### Create the additional features
+
+First, you will generate the polynomial features from your training set. The code below demonstrates how to do this using the [`PolynomialFeatures`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html) class. It will create a new input feature which has the squared values of the input `x` (i.e. degree=2).
+# Instantiate the class to make polynomial features
+poly = PolynomialFeatures(degree=2, include_bias=False)
+
+# Compute the number of features and transform the training set
+X_train_mapped = poly.fit_transform(x_train)
+
+# Preview the first 5 elements of the new training set. Left column is `x` and right column is `x^2`
+# Note: The `e+<number>` in the output denotes how many places the decimal point should 
+# be moved. For example, `3.24e+03` is equal to `3240`
+print(X_train_mapped[:5])
+You will then scale the inputs as before to narrow down the range of values.
+# Instantiate the class
+scaler_poly = StandardScaler()
+
+# Compute the mean and standard deviation of the training set then transform it
+X_train_mapped_scaled = scaler_poly.fit_transform(X_train_mapped)
+
+# Preview the first 5 elements of the scaled training set.
+print(X_train_mapped_scaled[:5])
+You can then proceed to train the model. After that, you will measure the model's performance against the cross validation set. Like before, you should make sure to perform the same transformations as you did in the training set. You will add the same number of polynomial features then scale the range of values.
+# Initialize the class
+model = LinearRegression()
+
+# Train the model
+model.fit(X_train_mapped_scaled, y_train )
+
+# Compute the training MSE
+yhat = model.predict(X_train_mapped_scaled)
+print(f"Training MSE: {mean_squared_error(y_train, yhat) / 2}")
+
+# Add the polynomial features to the cross validation set
+X_cv_mapped = poly.transform(x_cv)
+
+# Scale the cross validation set using the mean and standard deviation of the training set
+X_cv_mapped_scaled = scaler_poly.transform(X_cv_mapped)
+
+# Compute the cross validation MSE
+yhat = model.predict(X_cv_mapped_scaled)
+print(f"Cross validation MSE: {mean_squared_error(y_cv, yhat) / 2}")
+
+**You can create a loop that contains all the steps in the previous code cells. Here is one implementation that adds polynomial features up to degree=10.**
+# Initialize lists containing the lists, models, and scalers
+train_mses = []
+cv_mses = []
+models = []
+scalers = []
+
+# Loop over 10 times. Each adding one more degree of polynomial higher than the last.
+for degree in range(1,11):
+
+    # Add polynomial features to the training set
+    poly = PolynomialFeatures(degree, include_bias=False)
+    X_train_mapped = poly.fit_transform(x_train)
+
+    # Scale the training set
+    scaler_poly = StandardScaler()
+    X_train_mapped_scaled = scaler_poly.fit_transform(X_train_mapped)
+    scalers.append(scaler_poly)
+
+    # Create and train the model
+    model = LinearRegression()
+    model.fit(X_train_mapped_scaled, y_train )
+    models.append(model)
+
+    # Compute the training MSE
+    yhat = model.predict(X_train_mapped_scaled)
+    train_mse = mean_squared_error(y_train, yhat) / 2
+    train_mses.append(train_mse)
+
+    # Add polynomial features and scale the cross validation set
+    poly = PolynomialFeatures(degree, include_bias=False)
+    X_cv_mapped = poly.fit_transform(x_cv)
+    X_cv_mapped_scaled = scaler_poly.transform(X_cv_mapped)
+
+    # Compute the cross validation MSE
+    yhat = model.predict(X_cv_mapped_scaled)
+    cv_mse = mean_squared_error(y_cv, yhat) / 2
+    cv_mses.append(cv_mse)
+
+# Plot the results
+degrees=range(1,11)
+utils.plot_train_cv_mses(degrees, train_mses, cv_mses, title="degree of polynomial vs. train and CV MSEs")
+
+# Get the model with the lowest CV MSE (add 1 because list indices start at 0)
+# This also corresponds to the degree of the polynomial added
+degree = np.argmin(cv_mses) + 1
+print(f"Lowest CV MSE is found in the model with degree={degree}")

_posts/IIT/2023-10-01-redirect.md

@@ -0,0 +1,9 @@
+---
+layout: post
+title: Flexure Member Output
+date: 2023-10-01 17:39:00
+description: Design Output generated using PyLaTeX
+redirect: /assets/pdf/FlexureModuleExample.pdf
+---
+
+Redirecting to another page.

_projects/1_project.md

@@ -1,81 +1,52 @@
 ---
 layout: page
-title: project 1
-description: a project with a background image
-img: assets/img/12.jpg
+title: Compression member 
+description: A Design module in Osdag software application for Steel Design
+img: assets/img/IIT/Indian_Institute_of_Technology_Bombay_Logo.svg.png
 importance: 1
 category: work
-related_publications: einstein1956investigations, einstein1950meaning
+# related_publications: einstein1956investigations, einstein1950meaning
 ---
 
-Every project has a beautiful feature showcase page.
-It's easy to include images in a flexible 3-column grid format.
-Make your photos 1/3, 2/3, or full width.
+The [FOSSEE](https://fossee.in/) project is part of the National Mission on Education through Information and Communication Technology (ICT), Ministry of Education (MoE), Government of India.\\
+[Osdag](https://osdag.fossee.in/) is a cross-platform free/libre and open-source software for the design (and detailing) of steel structures, following the Indian Standard IS 800:2007. 
 
-To give your project a background in the portfolio page, just add the img tag to the front matter like so:
+Below I have added the images of the module - `Compression member` and it's sub-modules: `Columns in Frames` and `Struts in Trusses`
+<!-- Make your photos 1/3, 2/3, or full width. -->
+
+<!-- To give your project a background in the portfolio page, just add the img tag to the front matter like so:
 
     ---
     layout: page
     title: project
     description: a project with a background image
-    img: /assets/img/12.jpg
-    ---
+    img: /assets/img/IIT/Indian_Institute_of_Technology_Bombay_Logo.svg.png
+    --- -->
 
 <div class="row">
     <div class="col-sm mt-3 mt-md-0">
-        {% include figure.html path="assets/img/1.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
+        {% include figure.html path="assets/img/IIT/OsdagColumnlayout1.png" title="example image" class="img-fluid rounded z-depth-1" %}
     </div>
     <div class="col-sm mt-3 mt-md-0">
-        {% include figure.html path="assets/img/3.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
+        {% include figure.html path="assets/img/IIT/OsdagColumnlayout2.1.png" title="example image" class="img-fluid rounded z-depth-1" %}
     </div>
     <div class="col-sm mt-3 mt-md-0">
-        {% include figure.html path="assets/img/5.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
+        {% include figure.html path="assets/img/IIT/OsdagColumnlayout2.2.png" title="example image" class="img-fluid rounded z-depth-1" %}
     </div>
 </div>
-<div class="caption">
-    Caption photos easily. On the left, a road goes through a tunnel. Middle, leaves artistically fall in a hipster photoshoot. Right, in another hipster photoshoot, a lumberjack grasps a handful of pine needles.
-</div>
+Images for the Columns in Frames sub-module. On the left, you can see the main page. Middle, is the Design Preference pop-up with the description of Column Section. Right, is another pop-up for the Design Preference with the description of Optimization tab. This is done so that user gets better control over the design based on their input from the input dock.
 <div class="row">
     <div class="col-sm mt-3 mt-md-0">
-        {% include figure.html path="assets/img/5.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
+        {% include figure.html path="assets/img/IIT/OsdagStrutlayout1.png" title="example image" class="img-fluid rounded z-depth-1" %}
     </div>
-</div>
-<div class="caption">
-    This image can also have a caption. It's like magic.
-</div>
-
-You can also put regular text between your rows of images.
-Say you wanted to write a little bit about your project before you posted the rest of the images.
-You describe how you toiled, sweated, *bled* for your project, and then... you reveal its glory in the next row of images.
-
-
-<div class="row justify-content-sm-center">
-    <div class="col-sm-8 mt-3 mt-md-0">
-        {% include figure.html path="assets/img/6.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
+    <div class="col-sm mt-3 mt-md-0">
+        {% include figure.html path="assets/img/IIT/OsdagStrutlayout2.png" title="example image" class="img-fluid rounded z-depth-1" %}
     </div>
-    <div class="col-sm-4 mt-3 mt-md-0">
-        {% include figure.html path="assets/img/11.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
+    <div class="col-sm mt-3 mt-md-0">
+        {% include figure.html path="assets/img/IIT/OsdagStrutlayout2.2.png" title="example image" class="img-fluid rounded z-depth-1" %}
     </div>
 </div>
-<div class="caption">
-    You can also have artistically styled 2/3 + 1/3 images, like these.
-</div>
-
+Images for the Struts in Trusses sub-module. On the left, you can see the main page. Middle, is the Design Preference pop-up with the description of Angle Section. Right, is another pop-up for the Design Preference with the description of Optimization tab. This is done so that user gets better control over the design based on their input from the input dock.
 
-The code is simple.
-Just wrap your images with `<div class="col-sm">` and place them inside `<div class="row">` (read more about the <a href="https://getbootstrap.com/docs/4.4/layout/grid/">Bootstrap Grid</a> system).
-To make images responsive, add `img-fluid` class to each; for rounded corners and shadows use `rounded` and `z-depth-1` classes.
-Here's the code for the last row of images above:
-
-{% raw %}
-```html
-<div class="row justify-content-sm-center">
-    <div class="col-sm-8 mt-3 mt-md-0">
-        {% include figure.html path="assets/img/6.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-    </div>
-    <div class="col-sm-4 mt-3 mt-md-0">
-        {% include figure.html path="assets/img/11.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
-    </div>
-</div>
-```
-{% endraw %}
+The code is simple. Checkout `Osdag` and dowload it, don't worry it's free and open-source, checkout the Github pages(read more about the <a href="https://github.com/osdag-admin/Osdag">`Osdag - Github`</a> system).
+The code for the above images is available at my [Github page](https://github.com/rutvikjoshi63/Osdag/tree/Column-module-dev)