deeplizard teaches us how to normalize a dataset. We’ll see how dataset normalization is carried out in code, and we’ll see how normalization affects the neural network training process.
Content index:
deeplizard demonstrates how to process numerical data that we’ll later use to train our very first artificial neural network in this video.
In this video, deeplizard discusses GPU support for TensorFlow and the integrated Keras API and how to get your code running with a GPU.
deeplizard demonstrates how we can use the CUDA capabilities of PyTorch to run code on the GPU in this episode.
In this episode, deeplizard discusses the Keras integration with TensorFlow (tf.keras), compatibility requirements, and how to get Keras installed on your machine!
deeplizard has put together a course on how to use Keras.
In this course, we will learn how to use Keras, a neural network API written in Python! In this episode, we discuss the prerequisites required to start working with Keras, why Keras is a good library to learn, and what resources you’ll have available to ensure your success in this course.
Two Minute Papers examines the paper “Manipulating Attributes of Natural Scenes via Hallucination” in the video below.
On Friday, someone asked me about linear regression with neural networks.
I didn’t have a good answer – I knew that you *could* do linear regression but neural networks, but never had actually done it in practice.
Promising to learn more, I came across this video by giant_neural_network on YouTube.
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!
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