deep learning paper
GitHub - labmlai/annotated_deep_learning_paper_implementations: 🧑 🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
This is a collection of simple PyTorch implementations of neural networks and related algorithms. We believe these would help you understand these algorithms better. We are actively maintaining this repo and adding new implementations almost weekly. If you use this for academic research, please cite it using the following BibTeX entry. This shows the most popular research papers on social media.
Four Deep Learning Papers to Read in January 2022
Welcome to the January edition of the ‚Machine-Learning-Collage' series, where I provide an overview of the different Deep Learning research streams. So what is a ML collage? Simply put, I draft one-slide visual summaries of one of my favourite recent papers. At the end of the month all of the resulting visual collages are collected in a summary blog post. Thereby, I hope to give you a visual and intuitive deep dive into some of the coolest trends.
Four Deep Learning Papers to Read in December 2021
Welcome to the December edition of the ‚Machine-Learning-Collage' series, where I provide an overview of the different Deep Learning research streams. So what is a ML collage? Simply put, I draft one-slide visual summaries of one of my favourite recent papers. At the end of the month all of the resulting visual collages are collected in a summary blog post. Thereby, I hope to give you a visual and intuitive deep dive into some of the coolest trends. So without further ado: Here are my four favourite papers that I read in November 2021 and why I believe them to be important for the future of Deep Learning.
Four Deep Learning Papers to Read in December 2021
Welcome to the December edition of the ‚Machine-Learning-Collage' series, where I provide an overview of the different Deep Learning research streams. So what is a ML collage? Simply put, I draft one-slide visual summaries of one of my favourite recent papers. At the end of the month all of the resulting visual collages are collected in a summary blog post. Thereby, I hope to give you a visual and intuitive deep dive into some of the coolest trends. So without further ado: Here are my four favourite papers that I read in November 2021 and why I believe them to be important for the future of Deep Learning.
Four Deep Learning Papers to Read in December 2021
Welcome to the December edition of the ‚Machine-Learning-Collage' series, where I provide an overview of the different Deep Learning research streams. So what is a ML collage? Simply put, I draft one-slide visual summaries of one of my favourite recent papers. At the end of the month all of the resulting visual collages are collected in a summary blog post. Thereby, I hope to give you a visual and intuitive deep dive into some of the coolest trends. So without further ado: Here are my four favourite papers that I read in November 2021 and why I believe them to be important for the future of Deep Learning.
Four Deep Learning Papers to Read in September 2021
Welcome to the September edition of the ‚Machine-Learning-Collage' series, where I provide an overview of the different Deep Learning research streams. So what is a ML collage? Simply put, I draft one-slide visual summaries of one of my favourite recent papers. At the end of the month all of the resulting visual collages are collected in a summary blog post. Thereby, I hope to give you a visual and intuitive deep dive into some of the coolest trends.
Four Deep Learning Papers to Read in July 2021
Welcome to the July edition of the ‚Machine-Learning-Collage' series, where I provide an overview of the different Deep Learning research streams. So what is a ML collage? Simply put, I draft one-slide visual summaries of one of my favourite recent papers. At the end of the month all of the resulting visual collages are collected in a summary blog post. Thereby, I hope to give you a visual and intuitive deep dive into some of the coolest trends.
Four Deep Learning Papers to Read in June 2021
Welcome to the June edition of the ‚Machine-Learning-Collage' series, where I provide an overview of the different Deep Learning research streams. So what is a ML collage? Simply put, I draft one-slide visual summaries of one of my favourite recent papers. At the end of the month all of the resulting visual collages are collected in a summary blog post. Thereby, I hope to give you a visual and intuitive deep dive into some of the coolest trends. May has been quite the month including the virtual ICLR 2021 conference, ICML review decisions as well as the NeurIPS deadlines.
Four Deep Learning Papers to Read in May 2021
Welcome to the end of April edition of the ‚Machine-Learning-Collage' series, where I provide an overview of the different Deep Learning research streams. So what is a ML collage? Simply put, I draft one-slide visual summaries of one of my favourite recent papers. At the end of the month all of the resulting visual collages are collected in a summary blog post. Thereby, I hope to give you a visual and intuitive deep dive into some of the coolest trends. So without further ado: Here are my four favourite papers that I read in April 2021 and why I believe them to be important for the future of Deep Learning.
Four Deep Learning Papers to Read in April 2021
Welcome to the April edition of the ‚Machine-Learning-Collage' series, where I provide an overview of the different Deep Learning research streams. So what is this series about? Simply put, I draft one-slide visual summaries of one of my favourite recent papers. At the end of the month all of the visual collages are collected in a summary blog post. Thereby, I hope to give you a visual and intuitive deep dive into some of the coolest trends.