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Andrea Manero-Bastin's Blog (64)

Everything You Need to Know About Google Brain’s TensorFlow

This article was written by Jeffry Thurana.

Anybody who has tried Google Photos would agree that this free photo storage and management service from Google is smart. It packs in various smart features like advanced search, ability to categorize your pictures by locations and dates, automatically create albums and videos based on similarities, and walk you down the memory…

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Added by Andrea Manero-Bastin on January 3, 2020 at 7:00am — No Comments

Essentials of Deep Learning : Introduction to Long Short Term Memory

This article was written by Pranjal Srivastava.

Sequence prediction problems have been around for a long time. They are considered as one of the hardest problems to solve in the data science industry. These include a wide range of problems; from predicting sales to finding patterns in stock markets’ data, from understanding movie plots to recognizing your…

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Added by Andrea Manero-Bastin on January 1, 2020 at 4:30am — No Comments

A Majority of Data Scientists Lack Competency in Advanced Machine Learning Areas and Techniques

This article was written by Bob Hayes.

Data science requires the effective application of skills in a variety of machine learning areas and techniques. A recent survey by Kaggle, however, revealed that a limited number of data professionals possess competency in advanced machine learning skills. About half of data professionals said they were competent in…

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Added by Andrea Manero-Bastin on December 23, 2019 at 1:00pm — 1 Comment

Regression analysis using Python

This article was written by Stuart Reid. 

 

This tutorial covers regression analysis using the Python StatsModels package with Quandl integration. For motivational purposes, here is what we are working towards: a regression analysis program which receives multiple data-set names from Quandl.com, automatically downloads the data, analyses it, and plots the results in a new window.…

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Added by Andrea Manero-Bastin on December 8, 2019 at 6:30am — No Comments

R Package Install Troubleshooting

This article was written by Laura Ellis.

 

One of the reasons why I love R is that I feel like I’m constantly finding out about cool new packages through an ever-growing community of users and teachers. 

To understand the current state of R packages on CRAN, I ran some code provided by Gergely Daróczi on Github .  As of today there have been almost 14,000 R packages published on CRAN and the rate of…

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Added by Andrea Manero-Bastin on December 1, 2019 at 6:30am — No Comments

Deep Learning from first principles in Python, R and Octave – Part 1

This article was written by Tinniam V Ganesh.

 

This is the first in the series of posts, I intend to write on Deep Learning. This post is inspired by the Deep Learning Specialization by Prof Andrew Ng on Coursera and Neural Networks for Machine Learning by Prof Geoffrey Hinton also on Coursera.…

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Added by Andrea Manero-Bastin on November 30, 2019 at 9:00am — No Comments

How to build a deep learning model in 15 minutes

This article was written by Montana Low. 

 

An open source framework for configuring, building, deploying and maintaining deep learning models in Python.

As Instacart has grown, we’ve learned a few things the hard way. We’re open sourcing Lore, a framework to make machine learning approachable for Engineers and maintainable for Machine Learning Researchers.…

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Added by Andrea Manero-Bastin on November 30, 2019 at 9:00am — No Comments

Beginners Guide To Statistical Cluster Analysis

This article was written by Mohammad Sajid.

 

Statistical cluster analysis is an Exploratory Data Analysis Technique which groups heterogeneous objects(M.D.) into homogeneous groups. We will learn the basics of cluster analysis with mathematical way.

Cluster Analysis can be done by two…

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Added by Andrea Manero-Bastin on November 30, 2019 at 8:30am — No Comments

Introduction to Markov Chains

This article was written by Devin Soni.  

 

Markov chains are a fairly common, and relatively simple, way to statistically model random processes. They have been used in many different domains, ranging from text generation to financial modeling. A popular example is r/SubredditSimulator, which uses Markov chains to automate the creation of content for an entire subreddit. Overall, Markov Chains are conceptually quite intuitive,…

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Added by Andrea Manero-Bastin on November 18, 2019 at 5:00am — 1 Comment

New Theory Cracks Open the Black Box of Deep Learning

This article was written by Natalie Wolchover.

 Even as machines known as “deep neural networks” have learned to converse, drive cars, beat video games and Go champions, dream, paint pictures and help make scientific discoveries, they have also confounded their human creators, who never expected so-called “deep-learning”…

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Added by Andrea Manero-Bastin on November 1, 2019 at 5:30am — No Comments

How to Detect Objects with Deep Learning on Raspberry Pi

This article was written by Sarthak Jain.

 The real world poses challenges like having limited data and having tiny hardware like Mobile Phones and Raspberry Pis which can’t run complex Deep Learning models. This post demonstrates how you can do object detection using a Raspberry Pi. Like cars on a road,…

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Added by Andrea Manero-Bastin on October 31, 2019 at 1:30pm — No Comments

SQL and RDBMS Basics

This article was originally posted here.

If you meet 10 people who have been in data science for more than 5 years, chances are that all of them would know of or would…

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Added by Andrea Manero-Bastin on October 24, 2019 at 8:00am — No Comments

Semantic Image Segmentation with DeepLab in TensorFlow

This article was written by Liang-Chieh Chen and Yukun Zhu.

Semantic image segmentation, the task of assigning a semantic label, such as “road”, “sky”, “person”, “dog”, to every pixel in an image enables numerous new applications, such as the synthetic shallow depth-of-field effect shipped in the portrait mode of the Pixel 2 and Pixel 2 XL smartphones and …

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Added by Andrea Manero-Bastin on October 24, 2019 at 7:30am — No Comments

5 Algorithms to Train a Neural Network

This article was written by Alberto Quesada.

 

The procedure used to carry out the learning process in a neural network is called the optimization algorithm (or optimizer). There are many different optimization algorithms. All have different characteristics and performance in terms of memory requirements, speed and…

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Added by Andrea Manero-Bastin on October 20, 2019 at 2:00am — No Comments

Why Every Data Scientist Needs A Data Engineer

This article was written by Laurel Brunk.

Data scientists spend most of their time (up to 79%!) on the part of their job they hate most.

 The Role of…

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Added by Andrea Manero-Bastin on October 20, 2019 at 2:00am — 2 Comments

When Bayes, Ockham, and Shannon come together to define machine learning

This article was written by Tirthajyoti Sarkar.

 

Acknowledgements

Thanks to my CS7641 class at Georgia Tech in my MS Analytics program, where I discovered this concept and was inspired to write about it. Thanks to Matthew Mayo for editing and re-publishing this…

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Added by Andrea Manero-Bastin on October 15, 2019 at 12:30am — No Comments

Automated Speech Processing using Filter Banks and MFCCs

This article was written by Haytham Fayek.

Speech processing plays an important role in any speech system whether its Automatic Speech Recognition (ASR) or speaker recognition or something else. Mel-Frequency Cepstral Coefficients (MFCCs) were very popular features for a long time; but more recently, filter banks are becoming increasingly popular. In this post, I will discuss filter…

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Added by Andrea Manero-Bastin on October 6, 2019 at 7:00am — No Comments

Machine Learning’s Limits (Part 1): Why machine learning works in some cases and not in others.

This article was written by Ed Sperling.

 

Semiconductor Engineering sat down with Rob Aitken, an Arm fellow; Raik Brinkmann, CEO of OneSpin Solutions; Patrick Soheili, vice president of business and corporate development at eSilicon; and Chris Rowen, CEO of Babblelabs. What follows are excerpts of that…

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Added by Andrea Manero-Bastin on October 6, 2019 at 6:30am — No Comments

A Comprehensive Guide to Stochastic Gradient Descent Algorithms

This article was written by Giuseppe Bonaccorso.

 

Stochastic Gradient Descent (SGD) is a very powerful technique, currently employed to optimize all deep learning models. However, the vanilla algorithm has many limitations, in particular when the system is ill-conditioned and could never find the global minimum. In this post, we’re going to analyze how it…

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Added by Andrea Manero-Bastin on September 30, 2019 at 3:00am — No Comments

Using Machine Learning to Solve Business Problems

This article was written by Soft Media Lab.

It has the following sections.

Contents

  • What is Machine Learning?
  • How to apply machine learning to…
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Added by Andrea Manero-Bastin on September 24, 2019 at 6:30am — No Comments

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