Deep Learning from Scratch with Numpy

Welcome to the Deep Learning from Scratch project! This open-source project aims to provide a comprehensive understanding of deep learning concepts by implementing various models entirely from scratch using Python's NumPy library.

Project Overview

In this project, we cover the following key topics:

1. Linear Regression
2. Logistic Regression
3. Neural Networks
4. Parameter Estimation
5. Back-Propagation
6. Statistical Inference

These topics are implemented including 4 Jupyter Notebook file within this repository.

Project Structure

The project is organized into the following files:

1. Simulation Study.ipynb: Implementation of linear regression model.
2. Logistic_Regression.ipynb: Implementation of logistic regression model.
3. Computation Graph.ipynb: Implementation of neural network model.
4. Fully connected neural network.ipynb: Implementation of statistical inference methods.

Getting Started

To get started with this project, simply clone this repository:

git clone https://github.com/rookie727/Deep-Learning-from-Scratch-Numpy-Implementation.git