Investigating the Behaviour of Deep Neural Networks for Classification
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Updated
Oct 7, 2018 - Python
Investigating the Behaviour of Deep Neural Networks for Classification
Deep Learning Library
Use NLP techniques to improve baseline model performance of a Question Answering problem
cifar10 classification based on alexnet and vgg16 using TensorFlow
Deep learning models in Python
Why Batch Normalization Works so Well (best peer-reviewed project at MLDS, 2017 Spring)
PyTorch implementation of batch normalization from "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" by Sergey Ioffe, Christian Szegedy
Tune-Mode ConvBN Blocks For Efficient Transfer Learning
Implementation of Neural Network from scratch using Numpy. Contains implementation of foward & backward prop, Fully Connected Architectures, CNN, Batch Normalization, Activation functions, DataLoader, PatternProducingNetwork, multiple experiments with random parameter search, filter visualization, accuracy graph (on html), etc.
Using advanced deep learning techniques on the MNIST dataset. Over 98% validation set accuracy.
Code for ResNet-Fixup experiments as part of "Batch Norm is a Cause of Adversarial Vulnerability" presented at http://deep-phenomena.org/
Фреймворк глубоко обучения на Numpy, написанный с целью изучения того, как все работает под "капотом".
Fall 2021 Introduction to Deep Learning - Homework 1 Part 2 (Frame Level Classification of Speech)
Machine Learning Practical - Coursework 2: Analysing problems with the VGG deep neural network architectures (with 8 and 38 hidden layers) on the CIFAR100 dataset by monitoring gradient flow during training. And exploring solutions using batch normalization and residual connections.
Various concepts of neural networks applied in python (numpy) to help people get started with AI.
This is AI that generates Shakespeare alike text.
Batch Normalization is technique to improve training a Neural Network by reducing Covariant Shift and this repository contains experiments pertinent to the White Paper.
Test-Time Entropy Minimization with Prototype Learning for EEG Signals
CNN (pytorch ver.) (In progress)
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