README.md

This project contains notebooks that provides a comprehensive introduction to various deep learning techniques, from basic neural networks to advanced convolutional neural networks (CNNs), and their applications in real-world problems

These notebooks are created as assignments while I am taking a course on Coursera

• Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning

Skills -> Computer Vision, Image Classification, Deep Neural Network, Convolution, Pooling, Activation, Optimizer, Loss function, Tensorflow, Keras, Python

Included Notebooks:

• SLNN-House-Prices-Prediction
  • This model predicts house prices based on the number of bedrooms using linear regression
    • https://github.com/TechWithRamaa/Deep-Learning-in-Tensorflow/blob/main/SLNN-House-Prices-Prediction.ipynb
• Neural-Network-Image-Classification
  • This model trains a neural network to classify Fashion MNIST into 10 categories using a feedforward architecture with one hidden layer
    • https://github.com/TechWithRamaa/Deep-Learning-in-Tensorflow/blob/main/Neural-Network-Image-Classification.ipynb
• Speeding-Neural-Network-With-Convolution
  • This CNN model effectively leverages convolutional and pooling layers for building efficient, scalable, and robust neural networks for multi-classifaction of huge image dataset
    • https://github.com/TechWithRamaa/Deep-Learning-in-Tensorflow/blob/main/Speeding-Neural-Network-With-Convolution.ipynb
• CNN-Happy-Sad-Image-Classification
  • This model features a CNN for binary image classification (happy/sad) with three convolutional layers, ReLU activation, and max pooling, followed by a dense layer and sigmoid output. It uses the Adam optimizer and binary cross-entropy loss, with early stopping based on accuracy
    • https://github.com/TechWithRamaa/Deep-Learning-in-Tensorflow/blob/main/CNN-Happy-Sad-Image-Classification.ipynb