TensorFlow: Basics to Advanced 🚀

This repository is a complete hands-on learning path for TensorFlow, starting from core basics and gradually moving to advanced deep learning concepts.

The project is designed to build strong foundations, understand internal working, and gain practical experience with real implementations.

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🎯 Project Goals

• Learn TensorFlow from scratch
• Understand tensors, graphs, and automatic differentiation
• Build ML & DL models using TensorFlow and Keras
• Implement training, evaluation, and optimization
• Move from simple models to advanced neural networks
• Prepare for internships, research, and real-world projects

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📚 Topics Covered (Step-by-Step)

🔹 1. TensorFlow Basics

• What is TensorFlow?
• Installation & setup
• Tensors (rank, shape, dtype)
• Tensor operations
• NumPy vs TensorFlow
• GPU/CPU usage

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🔹 2. TensorFlow Core Concepts

• Computational graphs
• Eager execution
• Automatic differentiation (GradientTape)
• Variables vs tensors
• Broadcasting

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🔹 3. Linear & Logistic Regression

• Linear regression using TensorFlow
• Logistic regression from scratch
• Loss functions
• Gradient descent optimization

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🔹 4. Neural Networks with TensorFlow

• Perceptron
• Fully connected neural networks
• Activation functions
• Weight initialization
• Forward & backward propagation

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🔹 5. Keras API

• Sequential API
• Functional API
• Custom models
• Custom loss functions
• Custom training loops

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🔹 6. Model Training & Optimization

• Optimizers (SGD, Adam, RMSProp)
• Learning rate scheduling
• Batch size & epochs
• Callbacks
• Early stopping

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🔹 7. Deep Learning Models

• Deep Neural Networks (DNN)
• Convolutional Neural Networks (CNN)
• Image classification
• Transfer learning

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🔹 8. Model Evaluation & Tuning

• Accuracy, precision, recall
• Confusion matrix
• Overfitting & underfitting
• Regularization (L1, L2, Dropout)
• Hyperparameter tuning

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🔹 9. Advanced TensorFlow

• Custom training loops
• TensorFlow datasets (tf.data)
• Model saving & loading
• TensorBoard
• Performance optimization

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🔹 10. Deployment Basics

• Model serialization
• Inference pipeline
• TensorFlow SavedModel
• Introduction to model deployment

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🛠 Tech Stack

• Python
• TensorFlow
• Keras
• NumPy
• Matplotlib
• Pandas

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📂 Project Structure

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├── 01_basics/
├── 02_tensor_operations/
├── 03_regression_models/
├── 04_neural_networks/
├── 05_keras_api/
├── 06_training_optimization/
├── 07_cnn_models/
├── 08_evaluation_tuning/
├── 09_advanced_tensorflow/
├── 10_deployment_basics/
├── notebooks/
└── README.md