Here you can find some of my AI related projects and codes
Contains some of my efforts in order to understand Deep Learning concepts and master the coding part of it. Note that the Colab links may not contain the codes for all parts of the problem and some parts may have been saved as versions (names) in Colab.
This file contains:
- Keras_cheat_sheet: A collection of useful functions and code snippets in Keras. Colab link
- MNIST_high_level: Check the affect of different hyperparameters on the MNIST problem and compare them all together. Colab link
- MNIST_deep_level: Create custom parts needed to implement a deep model (e.g. layers, model, optimizer) using Keras. Colab link
- MNIST_random_classification: Check if deep models really learn or they just memorize (related to this paper). Colab link
- MNIST_sequential_learning: Check if a model which learned some labels can learn new labels better.: Colab link
- CIFAR10_from_scratch: Seeing what you know in action is always enjoyable. Here we've written a deep models code from scratch (just numpy). Colab link
- CIFAR_10_one_layer_visualization: We want to know what do weights of a one layer network search for in CIFAR10 problem. Colab link
- Keras_char_RNN: Implementing different types of RNN in Keras including custom layer RNN. Colab link
- Mini_char_RNN: Implementing a vanilla RNN from scratch (just numpy). Inspired by this gist from Andrej Karpathy. Colab link
- Blob_AEs: Implementing AE, VAE and Disentangled VAE to learn useful features of blob images. Inspired by this paper. Colab link
- MNIST_AEs: Implementing AE, VAE and Disentangled VAE to learn useful features of MNIST images. Colab link
- Deep_JSCC_for_wireless_image_transmission-papaer: Implement the model proposed in this paper. Colab link
- And more stuff ...