Here’s an interesting and thoughtful look the role open source plays in AI development.

Think about it: just about every major AI tool is open source. TensorFlow being the most obvious example.

The biggest advantage of open-source AI systems is that they involve no licensing fee for using open-source AI systems. And that’s especially useful for people having little or no experience in IT infrastructure. But what’s free is not always free. Although open-source AI comes without any license fee, it has other hidden costs like commercial software such as training, implementation, and maintenance costs. But, open-source AI can also reduce these costs.

There are an abundance of ML tools available today.

For beginners, this can be overwhelming.

This article from Analytics India Magazine asks the top Kagglers for their favorite toolsets.

In the next section, we look at the top tools, frameworks, cloud services, libraries used by the Kaggle masters and Grand Masters, which they revealed to us in our exclusive interviews. That said, we have to admit that all these top Kagglers are of the opinion that one should not fall in love with tools, and it is all right as long any tools get the job done right!

Deep learning imitates the network of neurons in a human brain.

It consists of algorithms, which allow machines to train to perform tasks that include computer vision, speech recognition, natural language processing, and more.

Here’s an interesting look at what deep learning means for industry.

Computer vision is the most popular deep learning application used across the industry. Pattern recognition, optical character recognition, code recognition, facial recognition, object recognition, natural language processing and digital image processing are all driving the demand for deep learning.

Null Byte explains how to Use Android & Raspberry Pi for Local Voice communications.

It can be difficult to communicate off the grid when there’s no infrastructure. That’s also true when you’re in situations where there is no cellular service or reliable Wi-Fi hotspots, such as a convoy of vehicles that want to talk to talk to each other, or protestors around the world where law enforcement cut out the cell signals.

On this episode of Cyber Weapons Lab, we’ll show how you can use a cheap $35 Raspberry Pi with PirateBox to enable Android phones to talk to each other without using any cell towers.  

To learn more, check out the article: https://nulb.app/x6vtu

deeplizard debugs the PyTorch DataLoader to see how data is pulled from a PyTorch data set and is normalized.

We see the impact of several of the constructor parameters and see how the batch is built.

Content index:

  • 0:00 Welcome to DEEPLIZARD – Go to deeplizard.com
  • 0:45 Overview of Program Code
  • 3:12 How to Use Zen Mode
  • 3:56 Start the Debugging Process
  • 4:38 Initializing the Sampler Based on the Shuffle Parameter
  • 5:35 Debugging next(iter(dataloader))
  • 7:57 Building the Batch Using the Batch Size
  • 10:37 Get the Elements from Dataset
  • 18:43 Tensor to PIL Image
  • 20:41 Thanks for Contributing to Collective Intelligence

The Career Force goes through her top 5 free dataset resources in this video.

  1. Data.gov: https://data.govData.gov is a large dataset aggregator and the home of the US Government’s open data.
  2. FiveThirtyEight: https://data.fivethirtyeight.com/ This is a great resource to not only see datasets, but also see how a well-respected analytics organization provides meaningful insights and commentary on the data.
  3. Kaggle: https://www.kaggle.com/Kaggle  is a great resource not only for free datasets, but for data science topics in general.
  4. Data.World: https://data.world/ There are hundreds of thousands of free datasets for anyone that sets up an account on data.world.
  5. Google Dataset Search: https://datasetsearch.research.google.com/ By accessing thousands of different repositories across the web, Google Dataset Search provides access to almost 25 million different publicly available datasets.