Machine Learning Archives | Page 3 of 3

Deploying Machine Learning model in production

In this article, I am going to explain steps to deploy a trained and tested Machine Learning model in production environment.

Though, this article talks about Machine Learning model, the same steps apply to Deep Learning model too.

Below is a typical setup for deployment of a Machine Learning model, details of which we will be discussing in this article.

Process to build and deploy a REST service (for ML model) in production

The complete code for creating a REST service for your Machine Learning model can be found at the below link:

https://github.com/cloudxlab/ml/tree/master/projects/deploy_mnist

Let us say, you have trained, fine-tuned and tested Machine Learning(ML) model – sgd_clf, which was trained and tested using SGD Classifier on MNIST dataset. And now you want to deploy it in production, so that consumers of this model could use it. What are different options you have to deploy your ML model in production?

9 Indian Mathematicians Who Transformed The Norms Of Knowledge- Now It’s On Us

Mathematics is the science which deals with the logic of quantity, shape, and arrangement. Undeniably, math is all around us, in fact in everything we do. It wouldn’t be wrong to say, math is the building block for everything in our daily life period. Money, sports, architecture (ancient and modern), television, mobile devices, and even art, all of it has some mathematical concepts involved in it.

In India, mathematics has its origins in Vedic literature which is nearly four thousand years old. It should come as no surprise that the concept of number ‘0’ was discovered in India; also, various treatises on mathematics were authored by Indian mathematicians. The techniques of trigonometry, algebra, algorithm, square root, cube root, negative numbers, and the most significant decimal system are concepts which were discovered by Indian mathematician from ancient India and are employed worldwide even today.

Fashion-MNIST using Machine Learning

One of the classic problem that has been used in the Machine Learning world for quite sometime is the MNIST problem. The objective is to identify the digit based on image. But MNIST is not very great problem because we come up with great accuracy even if we are looking at few pixels in the image. So, another common example problem against which we test algorithms is Fashion-MNIST.

The complete code for this project you can find here : https://github.com/cloudxlab/ml/tree/master/projects/Fashion-MNIST

Fashion-MNIST is a dataset of Zalando’s fashion article images —consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each instance is a 28×28 grayscale image, associated with a label.

One-on-one discussion on Gradient Descent

Usually, the learners from our classes schedule 1-on-1 discussions with the mentors to clarify their doubts. So, thought of sharing the video of one of these 1-on-1 discussions that one of our CloudxLab learner – Leo – had with Sandeep last week.

Below are the questions from the same discussion.

You can go through the detailed discussion which happened around these questions, in the attached video below.

One-on-one discussion with Sandeep on Gradient Descent

Use-cases of Machine Learning in E-Commerce

What computing did to the usual industry earlier, Machine Learning is doing the same to usual rule-based computing now. It is eating the market of the same. Earlier, in organizations, there used to be separate groups for Image Processing, Audio Processing, Analytics and Predictions. Now, these groups are merged because machine learning is basically overlapping with every domain of computing. Let us discuss how machine learning is impacting e-commerce in particular.

The first use case of Machine Learning that became really popular was Amazon Recommendations. Afterwards, the Netflix launched a challenge of Movie Recommendations which gave birth to Kaggle, now an online platform of various machine learning challenges.

Before I dive deep into the details further, lets quickly brief the terms that are found often confusing. AI stands for Artificial Intelligence which means being able to display human-like intelligence. AI is basically an objective. Machine learning is making computers learn based on historical or empirical data instead of explicitly writing the rules. Artificial Neural networks are the computing constructs designed on a similar structure like the animal brain. Deep Learning is a branch of machine learning where we use a complex Artificial Neural network for predictions.

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Scholarship Test for Machine Learning Course

After receiving a huge response in our last scholarship test, we are once again back with a basic conceptual test to attain scholarship for our upcoming Specialization course on Machine Learning and Deep Learning.

Concepts to be tested: Linear algebra, probability theory, statistics, multivariable calculus, algorithms and complexity, aptitude and Data Interpretation.

Date and Time: September 2, 2018, 8:00 am PDT (8:30 pm IST)
Type: objective (MCQ)
Number of questions: 25
Duration: 90 minutes
Mode: Online

Register Now »

If you have a good aptitude and general problem-solving skills, this test is for you. So, go ahead and earn what you deserve.

If you have any questions on the test or if anything else comes up, just click here to let us know. We’re always happy to help.

How To Optimise A Neural Network?

When we are solving an industry problem involving neural networks, very often we end up with bad performance. Here are some suggestions on what should be done in order to improve the performance.

Is your model underfitting or overfitting?

You must break down the input data set into two parts – training and test. The general practice is to have 80% for training and 20% for testing.

You should train your neural network with the training set and test with the testing set. This sounds like common sense but we often skip it.

Compare the performance (MSE in case of regression and accuracy/f1/recall/precision in case of classification) of your model with the training set and with the test set.

If it is performing badly for both test and training it is underfitting and if it is performing great for the training set but not test set, it is overfitting.

Continue reading “How To Optimise A Neural Network?”

Machine Learning with Mahout

[This blog is from KnowBigData.com. It is pretty old. Many things have changed since then. People have moved to MLLib. We have also moved to CloudxLab.com.]

What is Machine Learning?

Machine Learning is programming computers to optimize a Performance using example data or past experience, it is a branch of Artificial Intelligence.

Types of Machine Learning

Machine learning is broadly categorized into three buckets:

Supervised Learning – Using Labeled training data, to create a classifier that can predict the output for unseen inputs.
Unsupervised Learning – Using Unlabeled training data to create a function that can predict the output.
Semi-Supervised Learning – Make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data.

Machine Learning Applications

Recommend Friends, Dates, Products to end-user.
Classify content into pre-defined groups.
Find Similar content based on Object Properties.
Identify key topics in large Collections of Text.
Detect Anomalies within given data.
Ranking Search Results with User Feedback Learning.
Classifying DNA sequences.
Sentiment Analysis/ Opinion Mining
Computer Vision.
Natural Language Processing,
BioInformatics.
Speech and HandWriting Recognition.

Mahout

Mahout – Keeper/Driver of Elephants. Mahout is a Scalable Machine Learning Library built on Hadoop, written in Java and its Driven by Ng et al.’s paper “MapReduce for Machine Learning on Multicore”. Development of Mahout Started as a Lucene sub-project and it became Apache TLP in Apr’10.

Topics Covered

Introduction to Machine Learning and Mahout
Machine Learning- Types
Machine Learning- Applications
Machine Learning- Tools
Mahout – Recommendation Example
Mahout – Use Cases
Mahout Live Example
Mahout – Other Recommender Algos

Machine Learning with Mahout Presentation

Machine Learning with Mahout Videohttps://www.youtube.com/embed/PZsTLIlSZhI

AutoQuiz: Generating ‘Fill in the Blank’ Type Questions with NLP

Can a machine create quiz which is good enough for testing a person’s knowledge of a subject?

So, last Friday, we wrote a program which can create simple ‘Fill in the blank’ type questions based on any valid English text.

This program basically figures out sentences in a text and then for each sentence it would first try to delete a proper noun and if there is no proper noun, it deletes a noun.

We are using textblob which is basically a wrapper over NLTK – The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.

Continue reading “AutoQuiz: Generating ‘Fill in the Blank’ Type Questions with NLP”