🧠 Deep Learning – Concept Notebooks with TensorFlow & Keras

This repository provides a hands-on exploration of essential Deep Learning concepts using TensorFlow, Keras, and NumPy through a series of curated Jupyter notebooks. It’s designed to help students, practitioners, and AI enthusiasts understand how deep learning models are built, trained, and evaluated.

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📚 Topics Covered

Notebook	Description
ann_basics.ipynb	Introduction to Artificial Neural Networks (ANNs)
cnn_classification.ipynb	Image classification using Convolutional Neural Networks (CNNs)
activation_functions.ipynb	Exploration of ReLU, Sigmoid, Tanh, Softmax, etc.
loss_functions.ipynb	Comparison of loss functions (MSE, CrossEntropy, etc.)
optimizers.ipynb	Working with optimizers like SGD, Adam, RMSProp
regularization_dropout.ipynb	Preventing overfitting using dropout and L2 regularization
mnist_classification.ipynb	Building a model to classify handwritten digits (MNIST)

Note: The above file names are assumed; feel free to replace them with your actual notebook names.

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🔧 Technologies Used

• Python 3.8+
• TensorFlow / Keras
• NumPy
• Matplotlib / Seaborn
• Jupyter Notebook

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🚀 Getting Started

1. Clone the repository

git clone https://github.com/anjaliy11/Deep_Learning.git
cd Deep_Learning