Top /r/MachineLearning Posts, Mar 814: Word vectors, Hardware for Deep Learning, and Neural Graphics Engines
Word vectors in NLP, Machine Learning's place in programming, hardware for deep learning, Machine Learning interviews, and neural graphics engines are all topics covered this week on /r/MachineLearning.
Grant Marshall
This week on /r/MachineLearning, we have articles on topics like hardware selection, word vectors, and CNNbased graphics engines.
1. A Word is Worth a Thousand Vectors +113
This post is a nice article about vector representations of words in NLP. It begins with a more general introduction and moves on to practical examples using word2vec and gensim. This is a nice introduction to NLP with word vectors.
2. Machine Learning for Programming by Peter Norvig +74
This presentation focuses on how machine learning can be applied to more general software development problems. It’s an interesting and welldelivered presentation that should be relevant to anyone working in implementing systems. Give it a watch.
3. A Full Hardware Guide to Deep Learning +72
It seems the intersection of GPUs and deep learning has become a very popular topic on /r/MachineLearning lately. This post adopts an analysis and review structure, detailing exactly what to look for in creating a consumer deep learning focused machine. If you want to build a system for processing large deep learning workloads, this is an excellent place to look. It even summarizes the suggestions at the end for easy digestion.
4. Would anyone be interested in giving me a fake ML interview over Skype today (in 15 hours from when this post was created)? +57
This selfpost has a member of the community asking others if they would be interested in interviewing them to practice for a job interview. Surprisingly enough, someone offered their time to do so. Based on the responses to the post, it seems that there’s a demand out there for machine learning and data science interview practice and questions. The resource linked in the comments, datasciencequestions.com, looks like a good resource, but it may be interesting to see a collaborative space where people can practice interviewing one another.
5. Neural Graphics Engine +54
This article details a deep convolutional neural network approach to the problem of interpreting the structure of an object in an image. If you’re interested in this problem or CNNs, the paper and the code are both provided in the link.
Related:
This week on /r/MachineLearning, we have articles on topics like hardware selection, word vectors, and CNNbased graphics engines.
1. A Word is Worth a Thousand Vectors +113
This post is a nice article about vector representations of words in NLP. It begins with a more general introduction and moves on to practical examples using word2vec and gensim. This is a nice introduction to NLP with word vectors.
2. Machine Learning for Programming by Peter Norvig +74
This presentation focuses on how machine learning can be applied to more general software development problems. It’s an interesting and welldelivered presentation that should be relevant to anyone working in implementing systems. Give it a watch.
3. A Full Hardware Guide to Deep Learning +72
It seems the intersection of GPUs and deep learning has become a very popular topic on /r/MachineLearning lately. This post adopts an analysis and review structure, detailing exactly what to look for in creating a consumer deep learning focused machine. If you want to build a system for processing large deep learning workloads, this is an excellent place to look. It even summarizes the suggestions at the end for easy digestion.
4. Would anyone be interested in giving me a fake ML interview over Skype today (in 15 hours from when this post was created)? +57
This selfpost has a member of the community asking others if they would be interested in interviewing them to practice for a job interview. Surprisingly enough, someone offered their time to do so. Based on the responses to the post, it seems that there’s a demand out there for machine learning and data science interview practice and questions. The resource linked in the comments, datasciencequestions.com, looks like a good resource, but it may be interesting to see a collaborative space where people can practice interviewing one another.
5. Neural Graphics Engine +54
This article details a deep convolutional neural network approach to the problem of interpreting the structure of an object in an image. If you’re interested in this problem or CNNs, the paper and the code are both provided in the link.
Related:
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