Author: Deep Learner

Neural Network

A PyTorch library to analyze representation of neural networks

anatome Ἀνατομή is a PyTorch library to analyze internal representation of neural networks This project is under active development and the codebase is subject to change. Installation anatome requires Python>=3.9.0 PyTorch>=1.9.0 torchvision>=0.10.0 After the installation of PyTorch, install anatome as follows: pip install -U git+https://github.com/moskomule/anatome Representation Similarity To measure the similarity of learned representation, anatome.SimilarityHook is a useful tool. Currently, the followingmethods are implemented. from anatome import SimilarityHook model = resnet18() hook1 = SimilarityHook(model, “layer3.0.conv1”) hook2 = SimilarityHook(model, “layer3.0.conv2”) model.eval() […]

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Advanced, Libraries, NLP, Project, Python, Text, Unsupervised

Getting started with NLP using NLTK Library

1010010   01101001   01110100   01101000   01101001  01101011   01100001 Did you understand the above binary code? If yes, then you’re a computer. If no, then you’re a Human. 🙂 I know it’s a difficult task for us to understand binary code just like computers because binary code is a Machine Understandable Language. Likewise, even computers don’t understand human language. So, how to make computers understand human language? The answer is Natural Language Processing. With the help of NLP, we can teach computers […]

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Advanced, NLP, Project, Python, Text, Unstructured Data

Text Generation Using Bidirectional LSTM – A Walk-through in Tensorflow

This article was published as a part of the Data Science Blogathon Text Generation The Text Generation is a Natural Language Processing task that involves automatically generating meaningful texts. We can also utilize the Text Generation process for Autocomplete. Initially, we provide a prompt, which is a text that is used as the base to generate texts. The model will generate texts based on the prompt, the predicted text will be added to the base prompt and it is fed again […]

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Video

Real-time 6DoF Video View Synthesis using Multi-Sphere Images

MatryODShka Codes for the following paper: MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere ImagesBenjamin Attal, Selena Ling, Aaron Gokaslan, Christian Richardt, James TompkinECCV 2020 If you use these codes, please cite: @inproceedings{Attal:2020:ECCV, author = “Benjamin Attal and Selena Ling and Aaron Gokaslan and Christian Richardt and James Tompkin”, title = “{MatryODShka}: Real-time {6DoF} Video View Synthesis using Multi-Sphere Images”, booktitle = “European Conference on Computer Vision (ECCV)”, month = aug, year = “2020”, url = “https://visual.cs.brown.edu/matryodshka” } Note that […]

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Debugger

NoPdb: Non-interactive Python Debugger

NoPdb is a programmatic (non-interactive) debugger for Python. This means it gives you access to debugger-like superpowers directly from your code. With NoPdb, you can: capture function calls, including arguments, local variables, return values and stack traces set “breakpoints” that trigger user-defined actions when hit, namely: evaluate expressions to retrieve their values later execute arbitrary code, including modifying local variables enter an interactive debugger like pdb NoPdb is also a convenient tool for inspecting machine learning model internals. For example, […]

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Tutorial

A collection of write-ups and solutions for Cyber FastTrack Spring 2021

Cyber-FastTrack-Spring-2021 Cyber FastTrack Spring 2021 / National Cyber Scholarship Competition – Spring 2021 Mon 5th April 17:00 BST – Wed 7th April 17:00 BST The CD Skids are back and playing for fun! This CTF was run for both Cyber FastTrack and National Cyber Scholarship at the same time. Although these writeups were primarily made for Cyber FastTrack, they should also be valid for the National Cyber Scholarship competition. This repository is the only one to be officially endorsed by […]

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Language Models

A python tools for fine-tuning language models for a task

DataTuner You have just found the DataTuner. This repository provides tools for fine-tuning language models for a task. Installation Environment Creation Assuming you have an existing conda setup, you can setup the environment with the following script. In order to activate the conda environment within the bash script, you need the location of the conda.sh file: bash setup.sh ~/miniconda3/etc/profile.d/conda.sh You can update your existing environment: conda env update -f=environment.yml To start development, activate your environment: conda activate finetune Alternatively, you […]

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Face recognition

Pretrained Pytorch face detection and facial recognition models

Face Recognition Using Pytorch This is a repository for Inception Resnet (V1) models in pytorch, pretrained on VGGFace2 and CASIA-Webface. Pytorch model weights were initialized using parameters ported from David Sandberg’s tensorflow facenet repo. Also included in this repo is an efficient pytorch implementation of MTCNN for face detection prior to inference. These models are also pretrained. To our knowledge, this is the fastest MTCNN implementation available. Quick start Install: # With pip: pip install facenet-pytorch # or clone this […]

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Segmentation

A semantic segmentation toolbox based on PyTorch

vedaseg vedaseg is an open source semantic segmentation toolbox based on PyTorch. Features Modular Design We decompose the semantic segmentation framework into different components. The flexible and extensible design make it easy to implement a customized semantic segmentation project by combining different modules like building Lego. Support of several popular frameworks The toolbox supports several popular semantic segmentation frameworks out of the box, e.g. DeepLabv3+, DeepLabv3, U-Net, PSPNet, FPN, etc. High efficiency Multi-GPU data parallelism & distributed training. Multi-Class/Multi-Label segmentation […]

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Neural Network

Visualization toolkit for neural networks in PyTorch

FlashTorch A Python visualization toolkit, built with PyTorch, for neural networks in PyTorch. Neural networks are often described as “black box”. The lack of understanding on how neural networks make predictions enables unpredictable/biased models, causing real harm to society and a loss of trust in AI-assisted systems. Feature visualization is an area of research, which aims to understand how neural networks perceive images. However, implementing such techniques is often complicated. FlashTorch was created to solve this problem! You can apply […]

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