Live training loss plot in Jupyter Notebook for Keras, PyTorch and others

Layers Outputs and Gradients in Keras. Made easy.

Neural network visualization toolkit for tf.keras

Keras implementation of a ResNet-CAM model

K-CAI NEURAL API - Keras based neural network API that will allow you to create parameter-efficient, memory-efficient, flops-efficient multipath models with new layer types. There are plenty of examples and documentation.

Object classification with CIFAR-10 using transfer learning

ASCII summary for simple sequential models in Keras

Using various CNN techniques on the MNIST dataset

Utilities for Keras - Deep Learning library

A Keras Model Visualizer

https://pypi.org/project/kviz/ Visualization library for keras neural networks. Contributions welcome

Dynamic visualization training service in Jupyter Notebook for Keras tf.keras and others.

📺 A Python library for pruning and visualizing Keras Neural Networks' structure and weights

Recognition of the images includes train and tests based on Python.

An IPython notebook demonstrating the process of Transfer Learning using pre-trained Convolutional Neural Networks with Keras on the popular CIFAR-10 Image Classification dataset.

This is a model that has been trained on historical data obtained from Yahoo Finance. The data set comprises of all data records starting from the launch date of this stock in India (1996). This model aims to pick up key trends in the stock price fluctuations based on Time Series mapping. It is able to render predictions for the upcoming time pe…

Breast cancer is the most common form of cancer in women, and invasive ductal carcinoma (IDC) is the most common form of breast cancer. Accurately identifying and categorizing breast cancer subtypes is an important clinical task, and automated methods can be used to save time and reduce error. The goal of this script is to identify IDC when it i…

📉 Visualize your Deep Learning training in static graphics

Easy way to visualize convolutional neural networks, through two visualizations : Reason & MaxOut. Final version : web app.