Sentimental Analysis

Sample sentence 1 Sample Sentence 2

Read more

Context free Grammer

Parsing: Input - Sentence Output - Parsed tree Parsing is a supervised machine learning problem. Training can be achieved by Treebank which consists of several sentences and their associated parsed trees. One example is Penn WSJ Treebank The leaf nodes makes up a sentences. THEN part of speech tagging THEN PHRASES/CONSTITUENTS. NP - noun phrase VP - verb phrase DT - Determiner S - Sentence V - Verb N - Noun

Read more

Probability Basic Questions

Always remember in probability questions, if multiple objects are being picked simulataneously or one by one. for practice try this and this Circular permutation (n-1)! , how? solve this

Read more

ROC Curve...AUC...What is that?

YouTube explainer Video Visualization Research paper

Read more

Serving Flask with Nginx + uWSGI

How this setup works? Flask is managed by uWSGI. uWSGI talks to nginx. nginx handles contact with the outside world. When a client connects to your server trying to reach your Flask app: nginx opens the connection and proxies it to uWSGI uWSGI handles the Flask instances you have and connects one to the client Flask talks to the client happily

Read more

Machine learning algorithms map

Machine Learning Algorithms in one glance.

Read more

Benchmarking right way

Table of contents Benchmarking right way - Introduction What is? Latency Throughput Packet Loss Processing time Resposne time Benchmarking time Network Latency Measure by ping Meaure by flent Test using curl Benchmark POST API using ab Benchmark POST API using wrk Memory benchmark CPU benchmark Benchmarking right way - Introduction Benchmakring API should be majorly concerned with time, cpu & memory. One should also be concerned about the number of concurrent connections the API can handle in Prod env. This post will help you understand the mechanisms of network slowdowns.

Read more

Cross Validation techniques

Cross validation is a model evaluation method that is better than residuals. The problem with residual evaluations is that they do not give an indication of how well the learner will do when it is asked to make new predictions for data it has not already seen. One way to overcome this problem is to not use the entire data set when training a learner. Some of the data is removed before training begins. Then when training is done, the data that was removed can be used to test the performance of the learned model on new data. This is the basic idea for a whole class of model evaluation methods called cross validation. Know more techniques? Start here

Read more

Machine learning evaluation metrics

One method of judging the quality of a particular model is by residuals. That means the model is fit using all the data points and the prediction for each data point is compared with its actual output. The absolute value of each error is taken and the mean of those values is computed to arrive at the mean absolute residual error. Models with lower values of this measure are deemed to be better. There are always a plethora of metrics in machine learning that can be used to evaluate the performance of a ML model. This is an attempt to draw a metric map, just to keep them all in one place.

Read more