jamescalam/channel-metadata huggingface dataset:
Title: Streamlit for ML #5.1 - Custom React Components in Streamlit Setup
Category: Science & Technology
Published: 2022-02-17 15:45:58 UTC
▶️ Streamlit for ML Part 5.2:
https://www.youtube.com/watch?v=mxm8ihWoVbk&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=6

There are plenty of prebuilt components designed by Streamlit themselves, and if you can't find what you need, there are even community-built components.

If you're still stuck, and there is just no component that covers what you need, we can build our own custom components.

To do this we do need to start playing with the lower-level web technologies that Streamlit itself is built upon. So it isn't as simple as using a prebuilt component. However, thanks to pre-made templates, it isn't too hard to create a new component.

In this sub-series, we'll learn exactly how to create custom components. We'll focus on designing an interactive card component using Material UI design elements.

▶️ Streamlit for ML Playlist:
https://www.youtube.com/watch?v=JLKUV-LiXjk&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=1

📕 Article:
Coming soon

📖 Friend link to article:
Coming soon

00:00 Intro
02:19 Environment Setup
03:42 Starting with a Template
07:41 Naming for Card Component
11:31 Installing Node Packages
15:12 Running the Component
Title: Streamlit for ML #5.2 - MUI Card Component Build
Category: Science & Technology
Published: 2022-02-21 14:00:31 UTC
▶️ Streamlit for ML Part 5.3:
https://www.youtube.com/watch?v=lZ2EaPUnV7k&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=7

There are plenty of prebuilt components designed by Streamlit themselves, and if you can't find what you need, there are even community-built components.

If you're still stuck, and there is just no component that covers what you need, we can build our own custom components.

To do this we do need to start playing with the lower-level web technologies that Streamlit itself is built upon. So it isn't as simple as using a prebuilt component. However, thanks to pre-made templates, it isn't too hard to create a new component.

In this sub-series, we'll learn exactly how to create custom components. We'll focus on designing an interactive card component using Material UI design elements.

▶️ Streamlit for ML Playlist:
https://www.youtube.com/watch?v=JLKUV-LiXjk&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=1

📕 Article:
Coming soon

📖 Friend link to article:
Coming soon

00:00 Intro
01:59 Clearing Card Component
04:59 Building the Component
14:22 Pulling in MUI Code
24:08 Adding Roboto Font
26:05 Final Points
Title: Streamlit for ML #5.3 - Publishing Components to Pip
Category: Science & Technology
Published: 2022-02-28 17:00:29 UTC
🎁 Free NLP for Semantic Search Course:
https://www.pinecone.io/learn/nlp

There are plenty of prebuilt components designed by Streamlit themselves, and if you can't find what you need, there are even community-built components.

If you're still stuck, and there is just no component that covers what you need, we can build our own custom components.

To do this we do need to start playing with the lower-level web technologies that Streamlit itself is built upon. So it isn't as simple as using a prebuilt component. However, thanks to pre-made templates, it isn't too hard to create a new component.

In this sub-series, we'll learn exactly how to create custom components. We'll focus on designing an interactive card component using Material UI design elements.

❗ Python Packaging Video:
https://youtu.be/JkeNVaiUq_c

▶️ Streamlit for ML Playlist:
https://www.youtube.com/watch?v=JLKUV-LiXjk&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=1

📕 Article:
Coming soon

📖 Friend link to article:
Coming soon

00:00 Intro
01:09 PyPI
02:41 Preparing for Distribution
05:43 Build React Component
06:39 Create Python Package
11:57 Pip Install
13:58 Ending
Title: Train Sentence Transformers by Generating Queries (GenQ)
Category: Science & Technology
Published: 2022-03-08 14:52:23 UTC
🎁 Free NLP for Semantic Search Course:
https://www.pinecone.io/learn/nlp

Fine-tuning effective dense retrieval models is challenging. Bi-encoders (sentence transformers) are the current best models for dense retrieval in semantic search. Unfortunately, they're also notoriously data-hungry models that typically require a particular type of labeled training data.

Hard problems like this attract attention. As expected, there is plenty of attention on building ever better techniques for training retrievers.

One of the most impressive is GenQ. This approach to building bi-encoder retrievers uses the latest text generation techniques to synthetically generate training data. In short, all we need are passages of text. The generation model then augments these passages with synthetic queries, giving us the exact format we need to train an effective bi-encoder model.

🌲 Pinecone article:
https://www.pinecone.io/learn/genq/

00:00 Intro
00:32 Why GenQ?
02:23 GenQ Overview
04:28 Training Data
06:48 Asymmetric Semantic Search
07:54 T5 Query Generation
13:52 Finetuning Bi-encoders
16:02 GenQ Code Walkthrough
21:40 Finetuning Bi-encoder Walkthrough
26:48 Final Points
Title: Testing the New Haystack Doc Store
Category: Science & Technology
Published: 2022-03-22 19:26:00 UTC
🥳  Released with Haystack v1.3! Install direct from PyPI with:

pip install 'farm-haystack[pinecone]'

PR:
https://github.com/deepset-ai/haystack/pull/2254

00:00 Intro
01:19 Demo Start and Install
03:25 Initialization
06:30 Download and Write Documents
10:55 Extractive QA Pipeline
11:23 Fetch by ID
19:01 Metadata Filtering
22:24 Get All Documents
Title: Is GPL the Future of Sentence Transformers? | Generative Pseudo-Labeling Deep Dive
Category: Science & Technology
Published: 2022-03-30 12:52:39 UTC
🎁 Free NLP for Semantic Search Course:
https://www.pinecone.io/learn/nlp

Training sentence transformers is hard; they need vast amounts of labeled data. On one hand, the internet is full of data, and, on the other, this data is *not* in the format we need. We usually need to use a supervised training method to train a high-performance bi-encoder (sentence transformer) model.

There is research producing techniques placing us ever closer to fine-tuning high-perfomance bi-encoder models with unlabeled text data. One of the most promising is GPL. At its core, GPL allows us to take unstructured text data and use it to build models that can understand this text. These models can then intelligently respond to natural language queries regarding this same text data.

It is a fascinating approach, with massive potential across innumerous use cases spanning all industries and borders. With that in mind, let's dive into the details of GPL and how we can implement it to build high-performance LMs with nothing more than plain text.

🌲 Pinecone article:
https://www.pinecone.io/learn/gpl/

🔗 Notebooks:
https://github.com/pinecone-io/examples/tree/master/learn/nlp_course/gpl

00:00 Intro
01:08 Semantic Web and Other Uses
04:36 Why GPL?
07:31 How GPL Works
10:37 Query Generation
12:08 CORD-19 Dataset and Download
13:27 Query Generation Code
21:53 Query Generation is Not Perfect
22:39 Negative Mining
26:28 Negative Mining Implementation
27:21 Negative Mining Code
35:19 Pseudo-Labeling
35:55 Pseudo-Labeling Code
37:01 Importance of Pseudo-Labeling
41:20 Margin MSE Loss
43:40 MarginMSE Fine-tune Code
46:30 Choosing Number of Steps
48:54 Fast Evaluation
51:43 What's Next for Sentence Transformers?
Title: Implementing Filters in the New Haystack Doc Store
Category: Science & Technology
Published: 2022-04-06 16:26:54 UTC
🥳  Released with Haystack v1.3! Install direct from PyPI with:

pip install 'farm-haystack[pinecone]'

Join me as I work through the final few PR issues on the latest Haystack document store, and figure out how Haystack's filter_utils work.

PR:
https://github.com/deepset-ai/haystack/pull/2254

00:00 Intro
02:41 Filtering
05:36 Testing Existing Filter Utils
07:57 Making Sense of Filter Utils
10:35 Writing the First Filter
16:26 First Working Filter
18:24 Testing New Filters
21:27 Implementing in the Doc Store
24:02 Testing Pipeline Filters
27:11 Final Issue and Outro
Title: Spotify's Podcast Search Explained
Category: Science & Technology
Published: 2022-04-14 13:14:50 UTC
The market for podcasts has grown tremendously in recent years.

Driving the charge in podcast adoption is Spotify. In a few short years, they have become the undisputed leaders in podcasting. Despite only entering the game in 2018, by late 2021, Spotify had already usurped Apple, the long-reigning leader in podcasts, with more than 28M monthly podcast listeners.

To back their podcast investments, Spotify has worked on making the podcast experience as seamless and accessible as possible. From their all-in-one podcast creation app (Anchor) to podcast APIs and their latest natural language enabled podcast search.

Spotify’s natural language search for podcasts is a fascinating use case. In the past, users had to rely on keyword/term matching to find the podcast episodes they wanted. Now, they can search in natural language, in much the same way we might ask a real person where to find something.

In this video, we will take a look under the hood of Spotify's podcast search, and learn how to implement a similar system ourselves.

🌲 Pinecone article:
https://www.pinecone.io/learn/spotify-podcast-search

🔗 Code and tests:
https://github.com/pinecone-io/examples/tree/spotify-podcast-search/learn/search-in-wild/spotify-podcast-search

00:00 Intro
04:16 NLP in Semantic Search
08:35 Why Now?
09:29 Transformer Models
11:52 Sentence Transformers
13:12 Vector Search
15:56 How Spotify Built Podcast Search
17:35 Data Source, Fine-tuning, and Eval
22:58 Code Implementation, Dataset
24:44 Data Preparation
26:39 Query Generation
29:54 Fine-tuning a Podcast Model
41:40 Evaluation
48:05 Does it Scale?
49:00 Sharing Your Work
Title: How to learn NLP for free
Category: Science & Technology
Published: 2022-04-26 13:05:48 UTC
Knowing what to learn is one of the hardest parts about self-learning. Imagine being thrown into the wilderness and being told to find a specific landmark. Without a map you will end up wandering to wilderness with no better option than taking one step after another.

I spent a long time wandering step-by-step and eventually found my way into working with deep learning and NLP full-time.

Here I will share many of the resources I used or wish I had used in the past. You can this "curriculum" as a rough guideline in self-learning ML and working towards a full-time position.

ALL LINKS in article/friend link below:

📕 Medium article:
https://jamescalam.medium.com/the-self-taught-nlp-engineer-curriculum-c425c3fc3ff6

📖 Friend link:
https://jamescalam.medium.com/the-self-taught-nlp-engineer-curriculum-c425c3fc3ff6?sk=986263c644d9b36699d800713faa478a

---

00:00 Intro
01:53 ML 101 + Prerequisites
04:58 Sentdex + Neural Nets from Scratch
07:32 ML Coursera
09:31 100 Page ML Book
11:14 Applied ML + Daniel Bourke
13:17 Origin of Modern NLP
13:41 CS224N
14:44 NLP Specialization Coursera
15:57 Modern NLP + Transformers Intro
16:54 Transformer Courses
18:14 Doing Projects
19:18 Semantic + Vector Search
19:54 NLP for Semantic Search
20:44 Mining of Massive Datasets
22:27 Final Points
Title: BERTopic Explained
Category: Science & Technology
Published: 2022-05-11 15:10:23 UTC
90% of the world's data is unstructured. It is built by humans, for humans. That's great for human consumption, but it is *very* hard to organize when we begin dealing with the massive amounts of data abundant in today's information age.

Organization is complicated because unstructured text data is not intended to be understood by machines, and having humans process this abundance of data is wildly expensive and *very slow*.

Fortunately, there is light at the end of the tunnel. More and more of this unstructured text is becoming accessible and understood by machines. We can now search text based on *meaning*, identify the sentiment of text, extract entities, and much more.

Transformers are behind much of this. These transformers are (unfortunately) not Michael Bay's Autobots and Decepticons and (fortunately) not buzzing electrical boxes. Our NLP transformers lie somewhere in the middle, they're not sentient Autobots (yet), but they can understand language in a way that existed only in sci-fi until a short few years ago.

Machines with a human-like comprehension of language are pretty helpful for organizing masses of unstructured text data. In machine learning, we refer to this task as *topic modeling*, the automatic clustering of data into particular topics.

BERTopic takes advantage of the superior language capabilities of these (not yet sentient) transformer models and uses some other ML magic like UMAP and HDBSCAN (more on these later) to produce what is one of the most advanced techniques in language topic modeling today.

🌲 Pinecone article:
https://www.pinecone.io/learn/bertopic

🔗 Code notebooks:
https://github.com/pinecone-io/examples/tree/master/learn/algos-and-libraries/bertopic

00:00 Intro
01:40 In this video
02:58 BERTopic Getting Started
08:48 BERTopic Components
15:21 Transformer Embedding
18:33 Dimensionality Reduction
25:07 UMAP
31:48 Clustering
37:22 c-TF-IDF
40:49 Custom BERTopic
44:04 Final Thoughts
Title: Long Form Question Answering (LFQA) in Haystack
Category: Science & Technology
Published: 2022-05-17 15:46:21 UTC
Question-Answering (QA) has exploded as a subdomain of Natural Language Processing (NLP) in the last few years. QA is a widely applicable use case in NLP yet was out of reach until the introduction of [transformer models](/learn/transformers/) in 2017.

Without transformer models, the level of language comprehension required to make something as complex as QA work simply was not possible.

Although QA is a complex topic, it comes from a simple idea. The automatic retrieval of information via a more human-like interaction. The task of information retrieval (IR) is performed by almost every organization in the world. Without other options, organizations rely on person-to-person IR and rigid keyword search tools. This haphazard approach to IR generates a lot of friction, particularly for larger organizations.

QA offers a solution to this problem. Rather than these documents being lost in an abyss, they can be stored within a space where an intelligent QA agent can access them. Unlike humans, our QA agent can scan millions of documents in seconds and return answers from these documents almost instantly.

With QA tools, employees can stop wasting time searching for snippets of information and focus on their *real*, value-adding tasks.

A small investment in QA is, for most organizations, a no-brainer.

🌲 Pinecone article:
https://www.pinecone.io/learn/haystack-lfqa

🔗 Code notebooks:
https://github.com/pinecone-io/examples/blob/master/integrations/haystack/haystack_lfqa.ipynb

00:00 Intro
04:20 Approaches to Question Answering
05:43 Components of QA Pipeline
08:58 LFQA Generator
09:40 Haystack Setup
10:32 Initialize Document Store
13:02 Getting Data
17:53 Indexing Embeddings
21:51 Initialize Generator
24:10 Asking Questions
26:12 Common Problems
29:32 Generator Memory
31:30 Few More Questions
34:54 Outro
Title: New GPU-Acceleration for PyTorch on M1 Macs! + using with BERT
Category: Science & Technology
Published: 2022-05-24 13:00:34 UTC
GPU-acceleration on Mac is finally here!

Today's deep learning models owe a great deal of their exponential performance gains to ever increasing model sizes. Those larger models require more computations to train and run.

These models are simply too big to be run on CPU hardware, which performs large step-by-step computations. Instead, they need massively parallel computations. That leaves us with either GPU or TPU hardware.

Our home PCs aren't coming with TPUs anytime soon, so we're left with the GPU option. GPUs use a highly parallel structure, originally designed to process images for visual heavy processes. They became essential components in gaming for rendering real-time 3D images.

GPUs are essential for the scale of today's models. Using CPUs makes many of these models too slow to be useful, which can make deep learning on M1 machines rather disappointing.

Fortunately, this is changing with the support of GPU on M1 machines beginning with PyTorch v1.12. In this video we will explain the new integration and how to implement it yourself.

📕  Article:
https://towardsdatascience.com/gpu-acceleration-comes-to-pytorch-on-m1-macs-195c399efcc1

📖  Friend Link (free access):
https://towardsdatascience.com/gpu-acceleration-comes-to-pytorch-on-m1-macs-195c399efcc1?sk=a88acd35f600858093c177b97d690b03

🔗  Code notebooks:
https://github.com/jamescalam/pytorch-mps

🤖  70% Discount on the NLP With Transformers in Python course:
https://bit.ly/3DFvvY5

🎉  Subscribe for Article and Video Updates!
https://jamescalam.medium.com/subscribe
https://medium.com/@jamescalam/membership

👾  Discord:
https://discord.gg/c5QtDB9RAP

00:00 Intro
01:34 PyTorch MPS
04:57 Installing ARM Python
09:09 Using PyTorch with GPU
12:14 BERT on PyTorch GPU
13:51 Best way to train LLMs on Mac
16:01 Buffer Size Bug
17:24 When we would use Mac M1 GPU
Title: How to Build an AI-Powered Video Search App
Category: Science & Technology
Published: 2022-06-01 16:29:43 UTC
Technology and culture have advanced and become ever more entangled. Some of the most significant technological breakthroughs are integrated so tightly into our culture that we never even notice they’re there.

One of those is AI-powered search. It powers your Google results, Netflix recommendations, and ads you see everywhere. It is being rapidly weaved throughout all aspects of our lives. Further, this is a new technology; its full potential is unknown.

This technology weaves directly into the cultural phenomenon of YouTube. Imagine a search engine like Google that allows you to rapidly access the billions of hours of YouTube content. There is no comparison to that level of highly engaging video content in the world.

🌲 Pinecone article:
https://www.pinecone.io/learn/youtube-search

🔗 Code:
https://github.com/pinecone-io/examples/tree/master/learn/projects/yt-search

00:00 Intro
02:56 YouTube Search App
04:43 Getting Data
07:58 Enhancing the Data
12:45 Scraping Other Metadata
14:52 Loading Data from Hugging Face
15:42 Index and Query the Data
20:43 Streamlit App Code
Title: Using Semantic Search to Find GIFs
Category: Science & Technology
Published: 2022-06-07 12:05:40 UTC
Vector search powers some of the most popular services in the world. It serves your Google results, delivers the best podcasts on Spotify, and accounts for at least 35% of consumer purchases on Amazon.

In this article, we will use vector search applied to language, called semantic search, to build a GIF search engine. Unlike more traditional search where we rely on keyword matching, semantic search enables search based on the human meaning behind text and images. That means we can find highly relevant GIFs with natural language prompts.

The pipeline for a project like this is simple, yet powerful. It can easily be adapted to tasks as diverse as video search or answering Super Bowl questions, or as we’ll see, finding GIFs.

🌲 Pinecone article:
https://www.pinecone.io/learn/gif-search

🔗 Code:
https://github.com/pinecone-io/examples/tree/master/learn/projects/gif-search

00:00 Intro
00:17 GIF Search Demo
01:56 Pipeline Overview
05:33 Data Preparation
08:17 Vector Database and Retriever
12:37 Querying
15:42 Streamlit App Code
Title: How to Learn Data Science | ML | Programming
Category: Education
Published: 2022-06-15 13:11:47 UTC
In this video I share five of the approaches/thoughts I have regarding learning, in particular for learning data science, machine learning, or programming.

00:00 Intro
01:33 Scale of Theory vs. Applied
02:55 Shape of Learning
05:52 Courses vs. Projects
08:37 Open Source
10:44 Writing
12:44 Following Interests
15:42 Final Notes
Title: Evaluation Measures for Search and Recommender Systems
Category: Science & Technology
Published: 2022-06-28 15:06:40 UTC
In this video you will learn about popular offline metrics (evaluation measures) like Recall@K, Mean Reciprocal Rank (MRR), Mean Average Precision@K (MAP@K), and Normalized Discounted Cumulative Gain (NDCG@K). We will also demonstrate how each of these metrics can be replicated in Python.

Evaluation of information retrieval (IR) systems is critical to making well-informed design decisions. From search to recommendations, evaluation measures are paramount to understanding what does and does not work in retrieval.

Many big tech companies contribute much of their success to well-built IR systems. One of Amazon’s earliest iterations of the technology was reportedly driving more than 35% of their sales. Google attributes 70% of YouTube views to their IR recommender systems.

IR systems power some of the greatest companies in the world, and behind every successful IR system is a set of evaluation measures.

🌲 Pinecone article:
https://www.pinecone.io/learn/offline-evaluation

🔗 Code notebooks:
https://github.com/pinecone-io/examples/tree/master/learn/algos-and-libraries/offline-evaluation

00:00 Intro
00:51 Offline Metrics
02:38 Dataset and Retrieval 101
06:08 Recall@K
07:57 Recall@K in Python
09:03 Disadvantages of Recall@K
10:21 MRR
13:32 MRR in Python
14:18 MAP@K
18:17 MAP@K in Python
19:27 NDCG@K
29:26 Pros and Cons of NDCG@K
29:48 Final Thoughts
Title: How to build next-level Q&A with OpenAI
Category: Science & Technology
Published: 2022-07-07 13:24:35 UTC
Walkthrough of the OpenAI x Pinecone Q&A app I built for a webinar with OpenAI. This is the coolest Q&A app I've ever built thanks to Pinecone vector search and OpenAI's incredible embeddings and generation endpoints.

LINKS:
🕹 App:
https://pinecone-io-playground-beyond-search-openaisrcserver-h65vzl.streamlitapp.com
👨‍💻 Code and Data:
https://github.com/pinecone-io/examples/tree/master/integrations/openai/beyond_search_webinar
OpenAI x Pinecone Webinar:
▶️ https://www.youtube.com/watch?v=HtI9easWtAA
Title: How to use Color Histograms for Image Retrieval
Category: Science & Technology
Published: 2022-07-13 16:22:08 UTC
Browsing, searching, and retrieving images has never been easy. Traditionally, many technologies relied on manually appending metadata to images and searching via this metadata. This approach works for datasets with high-quality annotation, but most datasets are too large for manual annotation.

That means any large image dataset must rely on Content-Based Image Retrieval (CBIR). Search with CBIR focuses on comparing the *content* of an image rather than its metadata. Content can be color, shapes, textures – or with some of the latest advances in ML — the "human meaning" behind an image.

Color histograms represent one of the first CBIR techniques, allowing us to search through images based on their color profiles rather than metadata.

🌲 Pinecone article:
https://pinecone.io/learn/color-histograms

00:00 Intro
01:23 What are Color Histograms?
08:39 How to Built Color Histograms
16:56 Using OpenCV calcHist
20:36 Image Retrieval
27:37 Pros and Cons
30:40 Final Points
Title: 🤗 Hugging Face just released *Diffusers* - for models like DALL-E 2 and Imagen!
Category: Science & Technology
Published: 2022-07-26 15:27:46 UTC
Hugging Face of transformer fame have created a whole new Python library for diffusion models! Diffusion models are a key component of models like OpenAI's DALL-E-2, Google's Imagen, and Midjourney's image generation service. HuggingFace Diffusers brings these models to a new level of accessibility (and open source!).

📕  Article:
https://towardsdatascience.com/hugging-face-just-released-the-diffusers-library-846f32845e65

📖  Friend Link (free access):
https://towardsdatascience.com/hugging-face-just-released-the-diffusers-library-846f32845e65?sk=9ec4027460defa1fd25178af9a55da13

🧨 Diffusers:
https://github.com/huggingface/diffusers

00:00 What are Diffusers?
01:55 Getting started
04:20 Prompt engineering
09:34 Testing other diffusers