Add new research and academic projects: Dropout Reduces Underfitting, GLM Bikes, ML Loan Prediction, and Breast Cancer Detection

- Implemented a new research project on Dropout strategies in deep learning, including detailed objectives, methodology, and usage instructions.
- Created a project for predicting bike rentals using Generalized Linear Models, outlining objectives, methodology, and key findings.
- Developed a machine learning project for loan prediction, detailing objectives, methodology, and a report on model performance.
- Added a project focused on breast cancer detection using various classification models, including objectives, methodology, and resources.
- Updated package.json with author information and upgraded dependencies.

content/projects/dl-dropout-reduces-underfitting.md

@@ -1,5 +1,5 @@
 ---
-slug: dropout-reduces-underfitting
+slug: dl-dropout-reduces-underfitting
 title: Dropout Reduces Underfitting
 type: Research Project
 description: TensorFlow/Keras implementation and reproduction of "Dropout Reduces Underfitting" (Liu et al., 2023). A comparative study of Early and Late Dropout strategies to optimize model convergence.
@@ -154,4 +154,4 @@ According to the paper, you should observe:
 
 M.Sc. Statistical and Financial Engineering (ISF) - Data Science Track at Université Paris-Dauphine PSL
 
-Based on the work of Liu, Z., et al. (2023). Dropout Reduces Underfitting.
+Based on the work of Liu, Z., et al. (2023). Dropout Reduces Underfitting.

content/projects/glm-bikes.md

@@ -1,5 +1,5 @@
 ---
-slug: bikes-glm
+slug: glm-bikes
 title: Generalized Linear Models for Bikes Prediction
 type: Academic Project
 description: Predicting the number of bikes rented in a bike-sharing system using Generalized Linear Models and various statistical techniques.

content/projects/ml-loan.md

@@ -1,5 +1,5 @@
 ---
-slug: loan-ml
+slug: ml-loan
 title: Machine Learning for Loan Prediction
 type: Academic Project
 description: Predicting loan approval and default risk using machine learning classification techniques.
@@ -37,4 +37,4 @@ The study employs various machine learning approaches:
 ## 📄 Detailed Report
 
 <iframe src="/projects/loan-ml.pdf" width="100%" height="1000px">
-</iframe>
+</iframe>

content/projects/sl-breast-cancer.md

@@ -1,5 +1,5 @@
 ---
-slug: breast-cancer
+slug: sl-breast-cancer
 title: Breast Cancer Detection
 type: Academic Project
 description: Prediction of breast cancer presence by comparing several supervised classification models using machine learning techniques.