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K-Means Clustering with TensorFlow | Sachin Joglekar's blog
https://codesachin.wordpress.com/2015/11/14/k-means-clustering-with-tensorflow
Sachin Joglekar's blog. K-Means Clustering with TensorFlow. Google recently open-sourced its Artificial Intelligence/Numerical Computing library called TensorFlow. TensorFlow was developed by members of the Google Brain team, and has the flexibility to run on a variety of platforms – including GPUs and mobile devices. TensorFlow’s methodology uses what they called. Consider the following diagram from the Wikipedia page on Genetic Programming. And optimization techniques to fine-tune the parameters in the...
The basics of Likelihood | Sachin Joglekar's blog
https://codesachin.wordpress.com/2016/07/24/the-basics-of-likelihood
Sachin Joglekar's blog. The basics of Likelihood. Anyone who has done some course in statistics or data science, must have come across the term ‘likelihood’. In non-technical language, likelihood. But ask any mathematician, and their interpretation of the two concepts is significantly different. I went digging into likelihood for a bit this week, so thought of putting down the basics of what I revisited. Whenever we talk about. Data, we talk about models. Defining the Likelihood Function. But in real lif...
March | 2016 | Sachin Joglekar's blog
https://codesachin.wordpress.com/2016/03
Sachin Joglekar's blog. A (small) introduction to Boosting. Is a machine learning. In an attempt to generate a strong overall model. Lets look at the highlighted parts one-by-one:. Or smaller Decision Trees. In most cases, either one of these two is used as a weak learner in Boosting frameworks. Many ensemble methods such as bagging. How does Boosting work? Usually, a Boosting framework for regression. The overall model at step ‘i’ is denoted by. 1 Start with a simple model. In terms of a value. Since mu...
A practical introduction to Functional Programming for Python coders | Sachin Joglekar's blog
https://codesachin.wordpress.com/2016/04/03/a-practical-introduction-to-functional-programming-for-python-coders
Sachin Joglekar's blog. A practical introduction to Functional Programming for Python coders. Functional programming is a programming paradigm that revolves around pure functions. If you have ever done coding in your life, you probably associate. That is the ‘CS’ perspective. Here, we lean more towards the mathematical definition:. A pure function is essentially any function that can be represented as a. Which means no side-effects. The easiest way to initialize a pure function in Python is by using the.
Self-Organizing Maps with Google’s TensorFlow | Sachin Joglekar's blog
https://codesachin.wordpress.com/2015/11/28/self-organizing-maps-with-googles-tensorflow
Sachin Joglekar's blog. Self-Organizing Maps with Google’s TensorFlow. This post assumes that you know the basics of Google’s TensorFlow. Library. If you don’t, have a look at my earlier post. To get started.]. Falls under the rare domain of unsupervised learning in Neural Networks. Its essentially a grid of. Learned during training. Traditionally speaking, there is no concept of neuron ‘locations’ in ANNs. However, in an SOM, each neuron has a location, and. Neurons that lie close. 1) Since my post.
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Learning in HE – My Big Data and Machine Learning Hub
https://hisah.net/learning-in-he
My Big Data and Machine Learning Hub. Protected: Learning in HE. This content is password protected. To view it please enter your password below:. Power BI solution templates now support Azure Analysis Services. We’re pleased to announce support for Azure Analysis Services for Power BI solution templates. Effective today, we support Azure Analysis Services for the Campaign/Brand Management for Twitter,. Using templates to customize restored VMs from Azure Backup. Inside R: How To. Because it's Frida...
Best Practices When Starting And Working On A Data Science Project | Guerrilla Analytics
https://guerrilla-analytics.net/2015/07/02/data-science-best-practice
A Practical Approach to Data Science. Best Practices When Starting And Working On A Data Science Project. Best Practices When Starting And Working On A Data Science Project. July 2, 2015. July 15, 2015. Data Science Best Practice. Setting up Data Science Projects. The 5 Cs of Data Science Data Quality. The 7 Guerrilla Analytics Principles. Several interesting questions were asked recently on a Data Science social network. This post addresses the question. Soft Skills Best Practice. Explain the caveats in...
Data Mining Competition Feeds – My Big Data and Machine Learning Hub
https://hisah.net/open-competitions
My Big Data and Machine Learning Hub. Data Mining Competition Feeds. Blogs on Big Data, Business Analytics, Data Mining, and Data Science. Kaggle Joins Google Cloud. March 8, 2017 5:18 pm. I’m proud and excited to share that Kaggle is joining Google Cloud! Open Data Spotlight: Government of India Census. March 6, 2017 5:09 pm. What story do the hearts of over 1 billion Indians tell? Becoming a Data Scientist: Profiling Cisco’s Data Science Certification Program. March 2, 2017 3:00 pm. The Santander Produ...
Data Hub – My Big Data and Machine Learning Hub
https://hisah.net/data-hub
My Big Data and Machine Learning Hub. Data for Everyone is a library collection of open data and data jobs owned by Crowdflower. The data was collected, cleaned, and labelled through a combination of the best of human and machine intelligence. Tweets and sentiment analysis. Sentiment analyses of single words or short phrases. Disasters on social media. First GOP debate sentiment analysis. Classification of political social media. Twitter sentiment analysis: Self-driving cars. 2015 New Year’s resolutions.
Now – My Big Data and Machine Learning Hub
https://hisah.net/now
My Big Data and Machine Learning Hub. Issues am currently working on. Measuring the Quality of Interesting Entities (articles, ads, and news feeds) on the Web. Background study on quality of Web articles and news feed: in progress. Much academic work on Wikipedia articles. Social media feeds (Facebook, Reddit, Twitter): various approaches by the platform owners. Open problems in measuring the quality of Web articles and news feed. Quality ranking of news feeds. On April 20th 2017, Google will start block...
Theses Review – My Big Data and Machine Learning Hub
https://hisah.net/theses-review
My Big Data and Machine Learning Hub. Pseudo-Relevance Feedback for Twitter Search. A Web service that provides real-time Twitter Search results incorporating automatic. Power BI solution templates now support Azure Analysis Services. We’re pleased to announce support for Azure Analysis Services for Power BI solution templates. Effective today, we support Azure Analysis Services for the Campaign/Brand Management for Twitter,. Using templates to customize restored VMs from Azure Backup. Inside R: How To.
Tutorials – My Big Data and Machine Learning Hub
https://hisah.net/tutorials
My Big Data and Machine Learning Hub. Industry Reality and Competition Winning Solutions. According to Zygmunt Zając. Most data mining and machine learning competitions are won by meticulously improving the score by a tiny fraction (usually by feature engineering, constructing different feature sets and ensembling the models). This approach however differ from the industry reality, different companies have different code deployment or production approaches. Lessons from Interesting Kaggle Competitions.
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Sachin Joglekar's blog – Programming | Python | ML
Sachin Joglekar's blog. From Dictionary.com,. Why do models need to be interpretable? The primary reason why we need explainability in AI, is to develop a sense of understanding. And the judicial system. Word2Vec is currently one of the best algorithms for computing word-embeddings. Given a significant amount of text. It was originally trained on a 3 million-word corpus of Google News articles. In some research. Funny ain’t it? How do we increase interpretability of models? Would mean that you understand...
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