Monday, December 9, 2019

Deep Learning, AI/ML, Computer Vision, and Data Science Gift Buying Guide - 2019

It’s the holiday season! This is the season of the year we like to celebrate the most. And it is the part of the year that we'd like to give, and receive, gifts.

While it's easy to pick a gift (relatively speaking) for some people, the interests of some others might make it hard nail down a gift for. (Take an AI, ML, Data Science and programming enthusiast like me for an example)

Now, you might have a family member, colleague, or a friend that is into AI, Deep Learning, and Machine Learning. Or, they might be working on or interested in Data Science or Computer Vision. You might be wondering what sort of gifts to get them.

Or simply as an enthusiast in those areas yourself, you're thinking of buying a gift for yourself this season. (I know that I’m thinking the same)

So, how do you select a gift for someone with interest in such vast and technical fields?

Well today I’m going to give you some gift ideas that just might work.

AI, ML, Deep Learning, Computer Vision, and Data Science Gift Guide 2019

When thinking of gifts relating to the AI/ML, CV, Data Science fields, we can consider three categories of gifts,

  1. Items to Improve their ability to perform tasks in those areas
  2. Give them new tech toys to play around in those areas
  3. Help them improve their knowledge in those areas

Let’s see what items we can select for each of those categories.

(Note: the links to the products are affiliate links. I earn a small percentage from qualifying purchases through those links)

Items to Improve their ability to perform tasks in those areas

As you might know, these types of AI/ML tasks require massive amounts of processing power. So, one of the best gifts for them might be items which would help them overcome that problem. While there are specialized hardware devices to perform machine learning and data processing tasks, most of them are beyond the budget of a typical holiday shopper. But, one of the more practical and affordable options that addresses the same problem are GPUs.

Most machine learning algorithms, libraries, and frameworks are built so that they can harness the parallel processing power of computer graphics cards. The unique ability of modern GPUs to perform parallel matrix calculations allows massive speedups in processing AI/ML (and even data science) tasks which heavily employs such operations. Technologies such as Nvidia CUDA has allowed anyone with a compatible graphics card to harness its power to speed up their AI/ML and Data Science tasks. And even if they already have a graphics card, multiple graphics cards can be used to parallel process in order to speed up the tasks even further.

Since Nvidia CUDA is the most popular and the technology most compatible the many popular libraries and frameworks, let’s look at a few of Nvidia graphics cards that might fit your bill.

(Note: While AMD has some excellent graphics card models, their compatibility and support with ML tasks are still experimental. So, we’ll stick to Nvidia here)

Things to consider when buying a graphics card for use with AI, ML, Data Science tasks:

  • CUDA Core Count: Higher the core count, the better it can parallelize the processing
  • Memory: Higher memory allows you to fit more training data at a time for processing. (If your dataset is bigger than the available GPU memory, you will have to chunk it and perform incremental learning)
  • Clock Speed: Higher clock speed, the better (If you’re just starting, don’t think too much about the ‘base clock’ number, as there are several other factors affecting the speed of the card)
  • Other Features: Having Tensor Cores might help increase the processing speed in the future. Library support is still experimental.

GeForce 16 series

The GeForce 16 series, released on February 2019, is one of the two main current GPU series in Nvidia right now (the other being the GeForce 20 series mentioned below). The GeForce 16 series is based on the same Turing GPU microarchitecture as in the GeForce 20 series but lacks the specialized Tensor and RT cores. Still, these are excellent choices for light to medium ML tasks if you’re on a budget.

GeForce GTX 1660 Super

  • CUDA Cores: 1408
  • Memory: 6GB GDDR6
  • Base Clock: 1530 MHz

GeForce GTX 1660 Ti

  • CUDA Cores: 1536
  • Memory: 6GB GDDR6
  • Base Clock: 1500 MHz

GeForce 20 series

The GeForce 16 series is the current mainstream consumer GPU series in the Nvidia lineup. The GeForce RTX Super line was released on July 2019. The RTX 20 series is based on the Turing GPU microarchitecture and are an excellent choice for AI/ML tasks due to its CUDA Compute 7.5 compatibility, dedicated Tensor Cores (mixed-precision FPUs specifically designed for large matrix arithmetic operations. Although the support from ML libraries are still experimental), and dedicated Integer (INT) cores for concurrent execution of integer and floating point operations. The 20 series GPUs also have larger memory capacities, allowing you to work with larger datasets at a time.

GeForce RTX 2060 Super

  • CUDA Cores: 2176
  • Memory: 8GB GDDR6
  • Base Clock: 1407 MHz
  • Tensor Cores: 272
Buy now at Amazon

GeForce RTX 2070 Super

  • CUDA Cores: 2560
  • Memory: 8GB GDDR6
  • Base Clock: 1605 MHz
  • Tensor Cores: 320
Buy now at Amazon

GeForce RTX 2080 Super

  • CUDA Cores: 3072
  • Memory: 8GB GDDR6
  • Base Clock: 1650 MHz
  • Tensor Cores: 384
Buy now at Amazon

GeForce RTX 2080 Ti

  • CUDA Cores: 4352
  • Memory: 11GB GDDR6
  • Base Clock: 1350 MHz
  • Tensor Cores: 544
Buy now at Amazon

Nvidia Titan RTX

  • CUDA Cores: 4608
  • Memory: 24GB GDDR6
  • Base Clock: 1350 MHz
  • Tensor Cores: 576
Buy now at Amazon

Give them new tech toys to play around in those areas

Intel Movidius Neural Compute Stick

Give deep learning capabilities to any device with a USB port.
A USB-based development kit, the Intel Neural Compute Stick 2 makes it easier to develop computer vision and AI applications at the network edge by enabling testing, tuning and prototyping directly on edge and IoT devices.

Buy now at Amazon

Google Coral Dev Board

A development board to quickly prototype on-device ML products. Scale from prototype to production with a removable system-on-module (som)

Buy now at Amazon

Google Coral USB Accelerator

Coral USB accessory that brings machine learning inferencing to existing systems. Works with Raspberry Pi (Pi2/3/4 Model B / B+) and other Linux systems. Featuring the Edge TPU - a small ASIC designed and built by Google - the USB Accelerator provides high performance ML inferencing with a low power cost over a USB 3.0 interface.

Google Coral Camera

A 5-megapixel camera module that's compatible with the Coral Dev Board. Connects through the MIPI-CSI interface, and provides an easy way to bring visual input into your models. Compatible with Coral Connects to the Coral Dev Board with ease through the CSI camera connector. Scalable to production Designed to scale with your manufacturing needs

NVIDIA Jetson Nano Developer Kit

NVIDIA Jetson Nano developer kit is a low-cost AI computer. It delivers the compute performance to run modern AI workloads at unprecedented size. It is incredibly power-efficient, consuming as little as 5 watts.

Buy now at Amazon

OpenMV Cam

Have you ever wanted to put computer vision into an embedded device? But wasn't sure whether it's possible to cram in a computer vision algorithm into a small hardware device?
Well, that's what the OpenMV project is all about.
OpenMV is a programmable embedded device, with a built-in camera, that you can program with variety of vision tasks.

Buy now at Amazon

Help them improve their knowledge in those areas

Build Deeper: The Path to Deep Learning

Whether they’re just a beginner or an intermediate, my book on deep learning should help them take the next step. Build Deeper: The Path to Deep Learning covers everything from the history of deep learning to practical step-by-step guides with full code samples to build your own deep learning and computer vision models. Get hands-on in Deep Learning with Python, TensorFlow, Keras, and OpenCV.

Buy now at Amazon

Deep Learning (Adaptive Computation and Machine Learning series)

If you like to get a more in-depth theoretical views on Deep Learning, then this is the book for it.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.

So, those are some gift ideas for AI, ML, Deep Learning, Computer Vision, and Data Science enthusiasts. Hope you find something in this list that works for you.

And remember to forward this list to others that might be buying gifts for you as well.

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