We will visualize the style transfer output produced by monet_generator_model. We take 5 sample images that are photos of beautiful landscapes in the original dataset and feed them to the model.
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Updated
Mar 8, 2024 - Jupyter Notebook
We will visualize the style transfer output produced by monet_generator_model. We take 5 sample images that are photos of beautiful landscapes in the original dataset and feed them to the model.
Collection of Jupyter Notebooks on neural networks using PyTorch and Stochaster involving detailed mathematics.
Things that I use while building the models
MNIST code with different classifier using sklearn and tflearn library in Python
NN Forge: Design neural networks visually with our intuitive GUI, then generate clean Python code to jumpstart your deep learning projects.
A python program to visualize simple neural networks
Real-time Object Detection and Neural Network Training Visualization
Training and web visualization of digit recognition feedforward neural network using brain.js and three.js
Nerou - neural webs engine with a graphical interface
An Interactive web-app that allows people to train live Neural-Network with Custom Inputs and Live feedback.
An impementation of CNN Fixations for PyTorch.
A generic implementation of multi layer perceptron neural networks, including some visual tools. Contains example of using the neural network classes for function approximation.
A Neural Network Visualizer Web Application On The MNIST Dataset.
Easy neural network visualizer control for .Net
A collection of Latex (tikz) code to Visualize Neural Network
Visualising your neat algorithm networks with the manim library. Works specifically for the neat-python library
A web app made using Flask and Streamlit to visualise the workings of a Neural Network
This repository consists the code for the paper titled Introspective Learning : A Two-Stage Approach for Inference in Neural Networks
Self Driving Car Simulation
Part of the experiments in "A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations"
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