Training using an alternative approach: forward-thinking
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Updated
May 9, 2023 - Jupyter Notebook
Training using an alternative approach: forward-thinking
This Project uses Convolutional Neural Networks (CNN) for the classification and prediction of handwritten Devanagari script. Leveraging transfer learning techniques, it adapts pre-trained models to recognize and forecast characters in Devanagari, enhancing accuracy and efficiency.
Journey to Learn Deep Learning with Pytorch from scratch i.e, from Tensor & Gradients to Advance topic like Generative Adversarial Networks
Pytorch Implementation and Performance Analysis of the Popular Vision Architectures from Scratch.
Simple implementation of a residually connected convolutional neural network in PyTorch
Optimize ResNet Learning Process
Initial Deep Learning Projects With Pytorch
A set of experiments inspired by the paper "Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs" by Jonathan Frankle, David J. Schwab, Ari S. Morcos
High Accuracy ResNet Model under 5 Million parameters.
An experiment in training a fully connected residual net to learn the argmax function.
A collection of small-scale projects that helped me learn the basics of the PyTorch framework
Simple Multi-GPU Implementation of ResNet in Tensorflow
Implemented Deep Residual Learning for Image Recognition Paper and achieved lower error rate by customizing different parts of the architecture.
Implemented the Deep Residual Learning for Image Recognition Paper and achieved better accuracy by customizing different parts of the architecture.
Robustness of Deep Neural Networks using Trainable Activation Functions
The aim is to build a Deep Convolutional Network using Residual Networks (ResNet). Here we build ResNet 50 using Keras.
Residual Embedding Similarity-based Network Selection (RESNets) for forecasting network dynamics.
Add a description, image, and links to the resnets topic page so that developers can more easily learn about it.
To associate your repository with the resnets topic, visit your repo's landing page and select "manage topics."