NLP & Deep learning | Always a learner

Machine Learning

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Source: ActiveLamp
  • The test time complexity of the machine learning model — Time took to predict output for a given input query point.

“Assuming training data has n points with d dimensions “

1) K-Nearest Neighbors:

Given query point (xq), K-NN follows these steps to predict output (yq). …


Careers, Machine Learning

Ace your machine learning interviews

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Image by mohamed Hassan from Pixabay

1. Explain the 68–95–99 rule in normal distribution?

  • As shown in the image below. nearly 68% of the data is within 1 standard deviation (σ) from the mean (μ), nearly 95% of the data is within 2 standard deviations (σ) from the mean (μ), and nearly 99.7% of the data is within 3 standard deviations (σ) from the mean (μ).


Careers, Machine Learning

Ace your machine learning interviews

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Photo by ThisisEngineering RAEng on Unsplash

1) What is the internal covariate shift and what are the consequences of it?

  • Internal covariate shift occurs when the statistical distribution of input data changes drastically with respect to other input data.
  • When the input data distribution changes, hidden layers try to learn to adapt to the new distribution. This slows down the training process, thus taking a long time to converge to a global minimum.

2) What is early stopping in deep learning?

  • Early stopping is a regularization technique. Overtraining a model on a dataset will cause overfitting.
  • Therefore it is required to stop the training when the model starts to overfit. This process of stopping the training early is called early stopping. In early stopping, hyperparameters could be no…


Machine learning is not model training.

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Photo by Tolga Ulkan on Unsplash

1) Contents

a. Data Collection

b. Exploratory Data Analysis

c. Data Preprocessing

d. feature engineering

e. Feature Selection

f. Model Selection and Hyperparameter Tuning

h. Model Evaluation and Analysis

2) Data Collection :

  • Many people think machine learning only concerns train models, but in fact, there are many to follow before training our model.

Machine Learning

What to use?

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Introduction:

It's very important to know where our model works well and where it fails. If there is a low latency requirement, definitely KNN will be a worse choice. Similarly, if data is non-linear, then choosing logistic regression is not good so let's dive deep into the discussion and find the pros and cons of models.

1) KNN:


Untangle hypothesis testing with a detailed walkthrough

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https://pixabay.com/photos/question-question-mark-survey-2736480/

Introduction:

what is a Hypothesis testing?

  • A statistical test that gives evidence to accept or reject the null hypothesis with a sample of data from the condition which is true for the entire population.
  • If we have to show two distributions are different then we prove by contradiction by assuming both distributions are the same which is our null hypothesis.

Example:

Steps to follow for Hypothesis Testing:

a)Choose the Test Statistic:

  • Test statistic=The difference in population means.

Observed difference in mean (uc1-uc2) = 30


Detailed code walkthrough

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Photo by Марьян Блан | @marjanblan on Unsplash

Introduction:

This blog strictly limits to code walkthrough to generate a summary using Text to text transfer transformer(T-5). If you guys are curious about how T-5 works and how it was pretrained and fine-tuned on downstream NLP tasks check out the following the blog.

1) Installing Hugging-face transformers:

  • Hugging face an open-source NLP library that made our life easy to deal with State of the art transformers just like sci-kit learn for machine learning algorithms

2) Import T5 tokenizer and T5 model from Hugging-face:

  1. Model and tokenizer initialization: Here I used pretrained T5-small for this task…


Interesting ideas that help you master the subject

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Photo by Hope House Press — Leather Diary Studio on Unsplash

Deep learning a trending word in technology for the past 6 years and hundreds of research papers have been publishing every week with new techniques to solve various Natural Language Processing, Natural Language Understanding, and computer vision tasks.

However, as a beginner, one has to be focus on the basics and need to understand how things work.

1)Image classification:


Careers, Machine Learning

It’s all about How and Why?

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Photo by Sebastian Herrmann on Unsplash

Introduction:

These are some interesting questions I encountered while preparing for a machine learning interview and tried to answer them.

Check out this for the first part

1. What is hinge loss and how to differentiate it?

  • Hinge Loss = max(0,1-Y*(W.X))

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