Why are GPUs necessary for training Deep Learning models?

Most of you would have heard exciting stuff happening using deep learning. You would have also heard that Deep Learning requires a lot of hardware. I have seen people training a simple deep learning model for days on their laptops (typically without GPUs) which leads to an impression that Deep Learning requires big systems to run execute. However, this is only partly true and this creates a myth around deep learning which creates a roadblock for beginners. Numerous people have asked me as to what kind of hardware would be better for doing deep learning. With this article, I hope to answer them.

Learn Python for Data Science from Scratch

Python is a multipurpose programming language and widely used for Data Science, which is termed as the sexiest job of this century. Data Scientist mine thru the large dataset to gain insight and make meaningful data driven decisions. Python is used as general purposed programming language and used for Web Development, Networking, Scientific computing etc. We will be discussing further about the series of awesome libraries in python such as numpy, scipy & pandas for data manipulation & wrangling and matplotlib, seaborn & bokeh for data visualization. So Python & R is just used as a tool for data science but for being a data scientist you need to know more about the statistical & mathematical aspects of the data and on top of everything a good domain knowledge is must. In my this post I will pave the path for learning Data science with Python and will share some useful resources for learning it. Remember learning for data science is time taking stuff and cannot be completed in a month or so and it requires a lot of practice, dedication and self confidence. So never giveup and happy learning.

Unsupervised Learning and Text Mining of Emotion Terms Using R

Unsupervised learning refers to data science approaches that involve learning without a prior knowledge about the classification of sample data. In Wikipedia, unsupervised learning has been described as “the task of inferring a function to describe hidden structure from ‘unlabeled’ data (a classification of categorization is not included in the observations)”. The overarching objectives of this post were to evaluate and understand the co-occurrence and/or co-expression of emotion words in individual letters, and if there were any differential expression profiles /patterns of emotions words among the 40 annual shareholder letters? Differential expression of emotion words was being used to refer to quantitative differences in emotion word frequency counts among letters, as well as qualitative differences in certain emotion words occurring uniquely in some letters but not present in others.

Introducing the TensorFlow Research Cloud

Researchers require enormous computational resources to train the machine learning (ML) models that have delivered recent breakthroughs in medical imaging, neural machine translation, game playing, and many other domains. We believe that significantly larger amounts of computation will make it possible for researchers to invent new types of ML models that will be even more accurate and useful. To accelerate the pace of open machine-learning research, we are introducing the TensorFlow Research Cloud (TFRC), a cluster of 1,000 Cloud TPUs that will be made available free of charge to support a broad range of computationally-intensive research projects that might not be possible otherwise.

4 Things Machine-Learning Algorithms Can Do for DevOps

As the IT industry struggles to elevate performance, companies are looking to DevOps to deliver on the promise of a newly efficient process that includes frequent release cycles to feed the higher demanding consumer. However, speeding up release cycles is far easier said, than done. IT practitioners are constantly seeking new tools to improve their responsiveness to business needs in this new environment. Transforming to a digital environment can prove to be a difficult evolution for most enterprise organizations; fruition will require a new mindset that is facilitated by technological progress.

Getting Into Data Science: What You Need to Know

Ready to embark on an exciting and in-demand career? Here’s what you need to know about what a data scientist does—and how you can become competitive in this in-demand field.

Descriptive Statistics Key Terms, Explained

This is a collection of 15 basic descriptive statistics key terms, explained in easy to understand language, along with an example and some Python code for computing simple descriptive statistics.
1. Descriptive Statistics
2. Population
3. Sample
4. Parameter
5. Statistic
6. Generalizability
7. Distribution
8. Mean
9. Median
10. Mode
11. Skew
12. Range
13. Variance
14. Standard Deviation
15. Interquartile Range (IQR)

An Introduction to Spatial Data Analysis and Visualization in R

The Consumer Data Research Centre, the UK-based organization that works with consumer-related organisations to open up their data resources, recently published a new course online: An Introduction to Spatial Data Analysis and Visualization in R. Created by James Cheshire (whose blog Spatial.ly regularly features interesting R-based data visualizations) and Guy Lansley, both of University College London Department of Geography, this practical series is designed to provide an accessible introduction to techniques for handling, analysing and visualising spatial data in R.

Training Neural Networks with Backpropagation. Original Publication.

Neural networks have been a very important area of scientific study that has evolved by different disciplines such as mathematics, biology, psychology, computer science, etc. The study of neural networks leapt from theory to practice with the emergence of computers. Training a neural network by adjusting the weights of the connections is computationally very expensive so its application to practical problems took until the mid-80s when a more efficient algorithm was discovered. That algorithm is now known as back-propagation errors or simply backpropagation.

Databases using R

Using databases is unavoidable for those who analyze data as part of their jobs. As R developers, our first instinct may be to approach databases the same way we do regular files. We may attempt to read the data either all at once or as few times as possible. The aim is to reduce the number of times we go back to the data ‘well’, so our queries extract as much data as possible.