Top /r/MachineLearning Posts, Mar 1-7: Stanford Deep Learning for NLP, Machine Learning with Scikit-learn
This week on /r/MachineLearning, we have a new NLP-focused deep learning course from Stanford, an introduction to scikit-learn, visualization of music collections, an implementation of DeepMind, and NLP using deep learning and Torch.
Grant Marshall.
This week on /r/MachineLearning, we have a number of great posts including deep learning with NLP and visualization of music collections.
1. Stanford University: Deep Learning for NLP - course by Richard Socher +130
Stanford has announced a completely new Deep Learning course focused on NLP. For those outside the university, the course notes and assignments will be made available online. So if you have experience with Python, probability, and machine learning, give this course a shot.
2. Introduction to Machine Learning with Python and Scikit-Learn +97
This post is a good first article for those already familiar with machine learning, but interested in seeing how to use the algorithms in practice with Python and Scikit-learn. It goes in depth from data loading to normalization to working with many different models. This is definitely a good first place to go if interested in learning about Scikit-learn.
3. Mapping your music collection with machine learning +78
This interesting post goes into applying machine learning to a music collection in order to compare different types of music in your collection. It then goes into some fascinating visualizations of the music, helping illustrate the technique. This is an interesting article if you’re into music or visualization.
4. Working Theano-based implementation of the DeepMind Atari-playing algorithm (NIPS, not Nature) +64
This post links to the code for an actual working implementation of the DeepMind Atari-playing algorithm presented at NIPS. Given how popular this paper has been, it’s good to see some actual implemented code on the web.
5. Facebook Research on Understanding Natural Language with Deep Neural Networks Using Torch +42
This post, written by Facebook AI researchers, details how to use Torch for NLP with deep learning networks. Beyond just showing how to perform these tasks with Torch, the post goes into the performance of running these algorithms on GPUs, which is an interesting addition.
Related:
This week on /r/MachineLearning, we have a number of great posts including deep learning with NLP and visualization of music collections.
1. Stanford University: Deep Learning for NLP - course by Richard Socher +130
Stanford has announced a completely new Deep Learning course focused on NLP. For those outside the university, the course notes and assignments will be made available online. So if you have experience with Python, probability, and machine learning, give this course a shot.
2. Introduction to Machine Learning with Python and Scikit-Learn +97
This post is a good first article for those already familiar with machine learning, but interested in seeing how to use the algorithms in practice with Python and Scikit-learn. It goes in depth from data loading to normalization to working with many different models. This is definitely a good first place to go if interested in learning about Scikit-learn.
3. Mapping your music collection with machine learning +78
This interesting post goes into applying machine learning to a music collection in order to compare different types of music in your collection. It then goes into some fascinating visualizations of the music, helping illustrate the technique. This is an interesting article if you’re into music or visualization.
4. Working Theano-based implementation of the DeepMind Atari-playing algorithm (NIPS, not Nature) +64
This post links to the code for an actual working implementation of the DeepMind Atari-playing algorithm presented at NIPS. Given how popular this paper has been, it’s good to see some actual implemented code on the web.
5. Facebook Research on Understanding Natural Language with Deep Neural Networks Using Torch +42
This post, written by Facebook AI researchers, details how to use Torch for NLP with deep learning networks. Beyond just showing how to perform these tasks with Torch, the post goes into the performance of running these algorithms on GPUs, which is an interesting addition.
Related:
