Quantum computing is still in at the early stage. However, the technology is maturing rapidly.

To take advantage of tomorrow’s ultra-fast quantum machines, researchers will have to write specialized algorithms that can run on qubits instead of bits, and they’ll need equally specialized development tools to help with the task.

That’s where TensorFlow Quantum comes into the picture. It provides a set of operators, low-level programming building blocks, for creating AI models that work with qubits, quantum logic gates and quantum circuits. These operators abstract away some of the underlying complexity to reduce the amount of code researchers need to write.

In this tutorial, learn how to visualize class activation maps for debugging deep neural networks using an algorithm called Grad-CAM.

Then you’ll learn how to implement Grad-CAM using Keras and TensorFlow.

While deep learning has facilitated unprecedented accuracy in image classification, object detection, and image segmentation, one of their biggest problems is model interpretability, a core component in model understanding and model debugging.

This tutorial on TensorFlow.org implements a simplified Quantum Convolutional Neural Network (QCNN), a proposed quantum analogue to a classical convolutional neural network that is also translationally invariant.

Wow.

This tutorial implements a simplified Quantum Convolutional Neural Network (QCNN), a proposed quantum analogue to a classical convolutional neural network that is also translationally invariant . This example demonstrates how to detect certain properties of a quantum data source, such as a quantum sensor or a complex simulation from […]

This is Part 2 of a four-part series that breaks up a talk that Seth Juarez gave at the Toronto AI Meetup. (Watch Part 1)

Index:

  • [00:13] Optimization (I explain calculus!!!)
  • [04:40] Gradient descent
  • [06:26] Perceptron (or linear models – we learned what these are in part 1 but I expound a bit more)
  • [07:04] Neural Networks (as an extension to linear models)
  • [09:28] Brief Review of TensorFlow

Here’s a great guide on how to turn your sweet PC gaming rig into a lean, mean machine learning machine.

Installation for Anaconda3 is straightforward. Just follow the prompts in the visual installer and install on your computer. Note that if you install for all users, you’ll have to get in the habit of running some Anaconda related things as administrator for permission purposes.

Siraj Raval explores why does a computer algorithm classify an image the way that it does? This is a question that is critical when it comes to AI applied to diagnostics, driving, or any other form of critical decision making.

In this video, he raises awareness around one technique in particular that I found called “Grad-Cam” or Gradient Class Activation Mappings.

Siraj Raval has a video exploring a paper about genomics and creating reliable machine learning systems.

Deep learning classifiers make the ladies (and gentlemen) swoon, but they often classify novel data that’s not in the training set incorrectly with high confidence. This has serious real world consequences! In Medicine, this could mean misdiagnosing a patient. In autonomous vehicles, this could mean ignoring a stop sign. Machines are increasingly tasked with making life or death decisions like that, so it’s important that we figure out how to correct this problem! I found a new, relatively obscure yet extremely fascinating paper out of Google Research that tackles this problem head on. In this episode, I’ll explain the work of these researchers, we’ll write some code, do some math, do some visualizations, and by the end I’ll freestyle rap about AI and genomics. I had a lot of fun making this, so I hope you enjoy it!

Likelihood Ratios for Out-of-Distribution Detection paper: https://arxiv.org/pdf/1906.02845.pdf 

The researcher’s code: https://github.com/google-research/google-research/tree/master/genomics_ood

Siraj Raval gets back to inspiring people to get into AI and pokes fun at himself.

Almost exactly 4 years ago I decided to dedicate my life to helping educate the world on Artificial Intelligence. There were hardly any resources designed for absolute beginners and the field was dominated by PhDs. In 2020, thanks to the extraordinary contributions of everyone in this community, all that has changed. It’s easier than ever before to enter into this field, even without an IT background. We’ve seen brave entrepreneurs figure out how to deploy this technology to save lives (medical imaging, automated diagnosis) and accelerate Science (AlphaFold). We’ve seen algorithmic advances (deepfakes) and ethical controversies (automated surveillance) that shocked the world. The AI field is now a global, cross-cultural movement that’s not limited to academics alone. And that’s something all of us should be proud of, we’re all apart of this. I’ve packed a lot into this episode! I’ll give my annual lists of the best ML language and libraries to learn this year, how to learn ML in 2020, as well as 8 predictions about where this field is headed. I had a lot of fun making this, so I hope you enjoy it!