jamescalam/channel-metadata huggingface dataset:
Title: How I work from anywhere
Category: Science & Technology
Published: 2022-08-16 13:55:16 UTC
Overview of how I deal with travel and work. Remote desk setup for staying as ergonomic and productive as possible, enjoy!

🔗 Links to products (mostly affiliate):
Laptop stand: https://amzn.to/3bZqMHM
Second screen: https://amzn.to/3w6IT5B
Cable bag (international): https://amzn.to/3QBH7S7
                              ... or UK: https://amzn.to/3ps5lT2
Peak Design backpacks: https://www.peakdesign.com/products/everyday-backpack
Title: Bag of *Visual* Words for Image Classification and Retrieval
Category: Science & Technology
Published: 2022-08-03 13:00:35 UTC
In computer vision, bag of visual words (BoVW) is one of the pre-deep learning models used for building image embeddings. Allowing us to retrieve images from a database that are similar to another "query" image, perform object detection, and image classification.

🌲 Pinecone article:
https://www.pinecone.io/learn/bag-of-visual-words/
Title: Fast intro to multi-modal ML with OpenAI's CLIP
Category: Science & Technology
Published: 2022-08-11 13:03:08 UTC
OpenAI's CLIP is "multi-modal" model capable of understanding the relationships and concepts between both text and images. As we'll see, CLIP is very capable, and when used via the Hugging Face library, could not be easier to work with.

📕  Article:
https://towardsdatascience.com/quick-fire-guide-to-multi-modal-ml-with-openais-clip-2dad7e398ac0

📖  Friend Link (free access):
https://towardsdatascience.com/quick-fire-guide-to-multi-modal-ml-with-openais-clip-2dad7e398ac0?sk=89bb2d8b8e583ed109d8a05e00366645

00:00 Intro
00:15 What is CLIP?
02:13 Getting started
05:38 Creating text embeddings
07:23 Creating image embeddings
10:26 Embedding a lot of images
15:08 Text-image similarity search
21:38 Alternative image and text search
Title: AlexNet and ImageNet Explained
Category: Science & Technology
Published: 2022-08-24 13:00:22 UTC
Today’s deep learning revolution traces back to the 30th of September, 2012. On this day, a Convolutional Neural Network (CNN) called AlexNet won the ImageNet 2012 challenge. AlexNet didn’t just win; it dominated.

AlexNet was unlike the other competitors. This new model demonstrated unparalleled performance on the largest image dataset of the time, ImageNet. This event made AlexNet the first widely acknowledged, successful application of deep learning. It caught people’s attention with a 9.8 percentage point advantage over the nearest competitor.

Until this point, deep learning was a nice idea that most deemed as impractical. AlexNet showed that deep learning was more than a pipedream, and the authors showed the world how to make it practical. Yet, the surge of deep learning that followed was not fueled solely by AlexNet. Indeed, without the huge ImageNet dataset, there would have been no AlexNet.

The future of AI was to be built on the foundations set by the ImageNet challenge and the novel solutions that enabled the synergy between ImageNet and AlexNet.

🌲 Pinecone article:
https://pinecone.io/learn/imagenet

00:00 Intro
01:06 Birth of Deep Learning
02:52 ImageNet
07:56 Lack of Readiness for Big Datasets
09:57 ImageNet Challenge (ILSVRC)
11:47 AlexNet
19:30 PYTORCH IMPLEMENTATION
19:55 Data Preprocessing
27:06 Class Prediction with AlexNet
31:50 Goldfish Results
34:27 Closing Notes
Title: Ultra-efficient Classifier Fine-tuning with Vector Search
Category: Science & Technology
Published: 2022-08-31 13:00:26 UTC
Learn how to use vector search to create highly targeted training for any classification model using a final linear classification layer. Easily fine-tune models in 10 minutes with less than 100 labeled examples.

🌲 Pinecone article:
https://pinecone.io/learn/classifier-train-vector-search/

00:00 Intro
01:14 Classification
02:49 Better Classifier Training
06:33 Classification as Vector Search
08:47 How Fine-tuning Works
10:50 Identifying Important Samples
12:39 CODE IMPLEMENTATION
13:13 Indexing
18:59 Fine-tuning the Classifier
27:37 Classifier Predictions
30:43 Closing Notes
Title: Hugging Face Datasets #1 - Hosting your datasets
Category: Science & Technology
Published: 2022-09-09 14:18:34 UTC
Introduction to Hugging Face datasets, how it works, and how to host your own simple datasets (JSONL, TSV, CSV, etc) for free via Hugging Face Datasets Hub

Warp download:
https://app.warp.dev/referral/7G3N39

Git LFS Install:
Mac:
$ brew install git-lfs
Debian/Ubuntu:
$ curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
$ sudo apt-get install git-lfs
Windows:
Get install from https://github.com/git-lfs/git-lfs/releases

00:00 Intro
04:36 Creating our own Datasets
08:29 Creating JSONL for Hugging Face
15:15 Uploading Datasets for Git
19:10 LFS for Large Files
21:56 Closing Notes
Title: CLIP Explained | Multi-modal ML
Category: Science & Technology
Published: 2022-09-15 13:00:22 UTC
Language models (LMs) can not rely on language alone. That is the idea behind the "Experience Grounds Language" paper, that proposes a framework to measure LMs' current and future progress. A key idea is that, beyond a certain threshold LMs need other forms of data, such as visual input.

The next step beyond well-known language models; BERT, GPT-3, and T5 is ”World Scope 3”. In World Scope 3, we move from large text-only datasets to large multi-modal datasets. That is, datasets containing information from multiple forms of media, like *both* images and text.

The world, both digital and real, is multi-modal. We perceive the world as an orchestra of language, imagery, video, smell, touch, and more. This chaotic ensemble produces an inner state, our "model" of the outside world.

AI must move in the same direction. Even specialist models that focus on language or vision must, at some point, have input from the other modalities. How can a model fully understand the concept of the word "person" without *seeing* a person?

OpenAI's Contrastive Learning In Pretraining (CLIP) is a world scope three model. It can comprehend concepts in both text and image and even connect concepts between the two modalities. In this video we will learn about multi-modality, how CLIP works, and how to use CLIP for different use cases like encoding, classification, and object detection.

🌲 Pinecone article:
https://pinecone.io/learn/clip/
Title: Hugging Face Datasets #2 - Dataset Builder Scripts
Category: Science & Technology
Published: 2022-09-23 14:45:22 UTC
How to work with dataset builder scripts, intro to the download manager, and Apache Arrow datatypes used in Hugging Face Datasets.

00:00 Intro
00:49 Creating Compressed Files
02:41 Creating Dataset Build Script
04:49 Download Manager
08:59 Finishing Split Generator
10:13 Generate Examples Method
14:47 Add Dataset to Hugging Face
17:49 Apache Arrow Features
22:52 What's Next?
Title: Zero-Shot Image Classification with OpenAI's CLIP
Category: Science & Technology
Published: 2022-10-05 14:00:03 UTC
State-of-the-art (SotA) computer vision (CV) models are characterized by a *restricted* understanding of the visual world specific to their training data [1].

These models can perform *very well* on specific tasks and datasets, but they do not generalize well. They cannot handle new classes or images beyond the domain they have been trained with.

Ideally, a CV model should learn the contents of images without excessive focus on the specific labels it is initially trained to understand.

Fortunately, OpenAI's CLIP has proved itself as an incredibly flexible CV classification model that often requires *zero* retraining. In this chapter, we will explore CLIP in zero-shot image classification.

🌲 Pinecone article:
https://pinecone.io/learn/clip-classification/