A Repo to store the Google Colaboratory Notebooks that I have created and shared
Generative Adversarial Networks with TensorFlow2, Keras and Python (Jupyter Notebooks Implementations)
A TF 2.0 implementation of Progressive growing of GANs
Colab notebooks for GAN tutorials.
A curated list of extensions, python packages, machine learning and collaborative notebooks ready to run in Deepnote.
A PyTorch notebook and implementation of a normal linear GAN
Repository of all notebooks used in the GANs and VAEs event.
Notebooks of pre trained models using the HAM10000 dataset
This Repository contain an IPython notebook of an example implementation of conditional Deep Convolutional Generative Adversarial Networks or cDCGAN or DC cGAN using Tensorflow.Keras Funtional API.
Scientific Guide AI notebooks is a collection of machine learning and deep learning notebooks prepared by Salem Messoud.
Exploring the fundamentals of neural networks
A google collab notebooks to generate audio reactive videos based on Stylegan2 models
Python codes and notebooks for the summer school on computer vision and machine learning in CVIT, IIIT Hyderabad, 2019
ou'll find a collection of Jupyter Notebook files showcasing various Generative Adversarial Networks (GANs) and their applications. Each notebook provides an interactive and informative environment to explore and experiment with different GAN architectures and techniques.
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
“Deep Learning with PyTorch: Zero to GANs” is an online course intended to provide a codingfirst introduction to deep learning using the PyTorch framework. The course takes a hands-on coding-focused approach and will be taught using live interactive Jupyter notebooks, allowing students to follow along and experiment. Theoretical concepts will be…
This repository contains all tasks, notebooks, and projects from my AI L3 internship at Orange Digital Center, including NLP, Computer Vision, GANs, VAEs, and MLOps using tools like BERT, YOLOv8, and Docker.
This repository focuses on implementing various machine learning techniques for Intrusion Detection Systems (IDS) in cybersecurity. It includes Jupyter Notebooks demonstrating methods like Isolation Forest, K-Means clustering, and Principal Component Analysis (PCA). Written in Python, it provides practical insights into anomaly detection.
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