Artificial Intelligence Fundamental Concepts
Natural Neural Network and Artificial Neural Network Fundamental Concepts [3D Brain]
Image Processing with OpenCV [Colab notebook link]
TensorFlow installation guide
PyTorch installation guide
Google Colab
- TensorFlow 2 quickstart
- TensorFlow Datasets [Catalog]
- [Teachable Machine V1] [Teachable Machine V2]
- Convolutional Neural Network (CNN) [Paper] [Explainer]
- Image classification, [Data Augmentation], [Batch Normalization], [Overfitting], and [Dropout]
- Transfer learning and fine-tuning
- Autoencoders [Paper]
- Variational Autoencoder (VAE) [Paper]
- MusicVAE [Paper] [Reference]
- Generative Adversarial Network (GAN) [Paper1] [Paper2] [Paper3] [Scribble Diffusion] [ChatGPT] [GPTZero]
- Pix2Pix (Image-to-image translation with a [Conditional GAN]) [Paper1] [Paper2] [Reference] [Demo]
- Image Segmentation with U-Net [Paper]
- Recurrent Neural Networks (RNN) [Paper1] [Paper2] [Textbook]
To plot the model:
tf.keras.utils.plot_model(model, rankdir="TD", show_shapes=True)
Parameters:
FC: (the previous layer number of nodes * the next layer number of nodes) + the next layer number of biases
CNNs: (kernel size (w*h) * number of channels * number of filters) + number of biases
Conv Feature Map Size:
The size of the convoluted matrix is given by C=((I-F+2P)/S)+1, where C is the size of the Convoluted matrix, I is the size of the input matrix, F is the size of the filter matrix and P is the padding applied to the input matrix.