August 15, 2021 Deep Learning

A gym style toolkit for building lightweight NAS systems

gymnastics A “gym” style toolkit for building lightweight Neural Architecture Search systems. I know, the name is awful. Installation Preferred option: Install from source: git clone [email protected]:jack-willturner/gymnastics.git cd gymnastics python setup.py install To install the latest release version: pip install gymnastics If you want to use NAS-Bench-101, follow the instructions here to get it set up. Overview Over the course of the final year of my PhD I worked a lot on Neural Architecture Search (NAS) and built a bunch […]

August 15, 2021 Transformer

The “tl;dr” on a few notable transformer papers

# tldr-transformers The tl;dr on a few notable transformer/language model papers + other papers (alignment, memorization, etc). Models: GPT- *, * BERT *, Adapter- *, * T5, etc. Each set of notes includes links to the paper, the original code implementation (if available) and the Huggingface :hugs: implementation. Here is an example: t5. The transformers papers are presented somewhat chronologically below. Go to the “:point_right: Notes :point_left:” column below to find the notes for each paper. This repo also includes […]

August 15, 2021 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() […]

August 15, 2021 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 […]

August 15, 2021 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 […]

August 14, 2021 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 […]

August 14, 2021 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, […]

August 14, 2021 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 […]

August 14, 2021 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 […]

August 14, 2021 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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