Video ID string | Channel ID string | Title string | Time Created string | Time Published string | Duration string | Description string | Category string | Like Count float64 | Dislike Count float64 |
|---|---|---|---|---|---|---|---|---|---|
Jk1YP4Y_U_0 | UCv83tO5cePwHMt1952IVVHw | Stoic Philosophy Text Generation with TensorFlow | 2020-04-19 11:33:45 UTC | 2020-04-19 13:52:43 UTC | 1859 seconds | Explanation of key parts to a RNN text generator built in TensorFlow with Python.
🤖 70% Discount on the NLP With Transformers in Python course:
https://bit.ly/3DFvvY5
I've written a couple of Medium articles on this project, if you're interested check them out here:
Stoic Philosophy - Built by Algorithms
https://tow... | People & Blogs | 10 | 0 |
gXqHd6-NKBo | UCv83tO5cePwHMt1952IVVHw | How to Build TensorFlow Pipelines with tf.data.Dataset | 2020-11-02 08:23:38 UTC | 2020-11-02 08:57:48 UTC | 1853 seconds | Link to updated version (without video freeze): https://youtu.be/f6XVfgJTbp4
An introduction to building better input pipelines for Machine Learning in TF2.
🤖 70% Discount on the NLP With Transformers in Python course:
https://bit.ly/3DFvvY5
Link to tf.data API docs: https://www.tensorflow.org/guide/data | People & Blogs | 46 | 9 |
yYEPNla4tlQ | UCv83tO5cePwHMt1952IVVHw | Every New Feature in Python 3.10.0a2 | 2020-11-08 18:09:49 UTC | 2020-11-10 16:44:05 UTC | 883 seconds | Every new feature in the early release alpha 2 preview of Python 3.10
There is video lag 5:00 - 9:55 covering the Type Alias section (sorry!) - the audio is okay though
🤖 70% Discount on the NLP With Transformers in Python course:
https://bit.ly/3DFvvY5 | People & Blogs | 88 | 5 |
GYDFBfx8Ts8 | UCv83tO5cePwHMt1952IVVHw | How-to Build a Transformer for Language Classification in TensorFlow | 2020-11-19 09:57:27 UTC | 2020-11-19 12:20:35 UTC | 2299 seconds | 🎁 Free NLP for Semantic Search Course:
https://www.pinecone.io/learn/nlp
How to build a transformer model for sentiment analysis (language classification) using HuggingFace's Transformers library in TensorFlow 2 with Python.
We cover the full process from downloading data all the way through to building and training... | People & Blogs | 384 | 12 |
DgGFhQmfxHo | UCv83tO5cePwHMt1952IVVHw | How-to use the Kaggle API in Python | 2020-11-22 20:19:30 UTC | 2020-11-22 20:29:27 UTC | 462 seconds | Simple step-by-step tutorial covering the setup and use of the Kaggle API for downloading datasets using the Kaggle library in Python.
🤖 70% Discount on the NLP With Transformers in Python course:
https://bit.ly/3DFvvY5 | People & Blogs | 121 | 6 |
YvVQgvAz9dY | UCv83tO5cePwHMt1952IVVHw | Language Generation with OpenAI's GPT-2 in Python | 2020-11-23 12:36:44 UTC | 2020-11-24 14:22:46 UTC | 498 seconds | Easy natural language generation with Transformers and PyTorch. We apply OpenAI's GPT-2 model to generate text in just a few lines of Python code.
Language generation is one of those natural language tasks that can really produce an incredible feeling of awe at how far the fields of machine learning and artificial int... | People & Blogs | 133 | 1 |
egDIqQIjDCI | UCv83tO5cePwHMt1952IVVHw | Text Summarization with Google AI's T5 in Python | 2020-11-24 21:26:27 UTC | 2020-11-27 06:00:07 UTC | 419 seconds | Easy text summarization using Google AI's T5 model using HuggingFace transformers and PyTorch in Python.
Automatic text summarization allows us to shorten long pieces of text into easy-to-read, short snippets that still convey the most important and relevant information of the original text.
In this video, we’ll buil... | People & Blogs | 115 | 1 |
DFtP1THE8fE | UCv83tO5cePwHMt1952IVVHw | How-to do Sentiment Analysis with Flair in Python | 2020-12-04 11:15:10 UTC | 2020-12-04 14:00:03 UTC | 848 seconds | Learn how to perform powerful sentiment analysis with no fine-tuning or pre-training required using the Flair NLP library in Python.
With the real-time information available to us on massive social media platforms like Twitter, we have all the data we could ever need to create these accurate and up-to-date sentiment m... | People & Blogs | 64 | 2 |
8o3jvkK2GGU | UCv83tO5cePwHMt1952IVVHw | Python Environment Setup for Machine Learning | 2020-12-23 13:50:07 UTC | 2020-12-23 13:53:02 UTC | 754 seconds | Everything you need for a Python environment set up for Machine Learning and Data Science!
📕 Article:
https://towardsdatascience.com/how-to-setup-python-for-machine-learning-173cb25f0206
🤖 70% Discount on the NLP With Transformers in Python course:
https://bit.ly/3DFvvY5
Thumbnail background by Christian Wiediger ... | People & Blogs | 38 | 1 |
BYbJ_HH788U | UCv83tO5cePwHMt1952IVVHw | Functional API - TensorFlow Essentials #2 | 2020-12-28 16:41:11 UTC | 2020-12-29 10:04:40 UTC | 341 seconds | A look at the functional API method for building models in TensorFlow 2 for Python.
🤖 70% Discount on the NLP With Transformers in Python course:
https://bit.ly/3DFvvY5
Thumbnail background by Darius Bashar on Unsplash
https://unsplash.com/@dariusbashar?utm_source=unsplash&utm_medium=referral&utm_content=cre... | Education | 20 | 0 |
_8Bydxud1XU | UCv83tO5cePwHMt1952IVVHw | Training Parameters - TensorFlow Essentials #3 | 2020-12-28 19:30:23 UTC | 2020-12-29 23:37:57 UTC | 450 seconds | Learn how to set up model training parameters and compile the model before training.
🤖 70% Discount on the NLP With Transformers in Python course:
https://bit.ly/3DFvvY5
Thumbnail background by Alex McCarthy on Unsplash
https://unsplash.com/@4lexmccarthy?utm_source=unsplash&utm_medium=referral&utm_content=cr... | Education | 17 | 0 |
f6XVfgJTbp4 | UCv83tO5cePwHMt1952IVVHw | Input Data Pipelines - TensorFlow Essentials #4 | 2020-12-28 23:25:54 UTC | 2020-12-30 11:30:02 UTC | 751 seconds | Learn how to set-up efficient and clean input data pipelines using tf.data.Dataset
🤖 70% Discount on the NLP With Transformers in Python course:
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Thumbnail background by Daria Nepriakhina on Unsplash
https://unsplash.com/@epicantus?utm_source=unsplash&utm_medium=referral&utm_content=cre... | Education | 54 | 0 |
MQD1yMnZ_jk | UCv83tO5cePwHMt1952IVVHw | Sequential Model - TensorFlow Essentials #1 | 2020-12-29 09:46:00 UTC | 2020-12-29 09:50:23 UTC | 391 seconds | Learn how to use the sequential model building approach in TensorFlow 2.
🤖 70% Discount on the NLP With Transformers in Python course:
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Background thumbnail by Aryan Dhiman on Unsplash
https://unsplash.com/@mylifeasaryan_?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyTex... | Education | 84 | 1 |
KTFWNI0qL28 | UCv83tO5cePwHMt1952IVVHw | 6 of Python's Newest and Best Features (3.7-3.9) | 2021-01-12 23:31:26 UTC | 2021-01-12 23:58:12 UTC | 1084 seconds | A rundown of the six most recent, and coolest features added to Python in the past few years!
2018 brought us a plethora of new features with the release of Python 3.7, followed by 3.8 in 2019, and 3.9 in 2020.
Many of those changes were behind the scenes. Optimizations and upgrades that the vast majority of us will ... | Education | 15 | 2 |
GyJtxd14DTc | UCv83tO5cePwHMt1952IVVHw | Novice to Advanced RegEx in Less-than 30 Minutes + Python | 2021-01-27 09:06:42 UTC | 2021-01-27 09:51:32 UTC | 1769 seconds | A full tutorial covering everything you need to know about Regular Expressions - an essential for anyone learning to code - and even more so for anyone interested in Natural Language Processing.
This video includes:
- metacharacters
- quantifiers
- capture groups
- using capture groups in Python
- character sets
- lo... | Education | 239 | 8 |
1ZcXmjZtJJ8 | UCv83tO5cePwHMt1952IVVHw | Building a PlotLy $GME Chart in Python | 2021-02-02 13:38:16 UTC | 2021-02-07 13:24:45 UTC | 4492 seconds | A code-along video covering the coding process from imagination to Python.
Something a little different, I'm not overly keen on this format - it's pretty long - but I've recorded it and I think maybe this can be useful for a few of you.
I haven't prepared anything beforehand, this is just going into the coding process ... | Education | 10 | 0 |
ZIRmXkHp0-c | UCv83tO5cePwHMt1952IVVHw | How to Build Custom Q&A Transformer Models in Python | 2021-02-09 20:42:56 UTC | 2021-02-12 13:30:03 UTC | 4216 seconds | In this video, we will learn how to take a pre-trained transformer model and train it for question-and-answering. We will be using the HuggingFace transformers library with the PyTorch implementation of models in Python.
Transformers are one of the biggest developments in Natural Language Processing (NLP) and learning... | Education | 163 | 5 |
FdjVoOf9HN4 | UCv83tO5cePwHMt1952IVVHw | How-to Use The Reddit API in Python | 2021-02-12 11:36:48 UTC | 2021-02-12 12:02:48 UTC | 1401 seconds | Learn how to use the Reddit API in Python, including setup, authorization, and pulling data from subreddits.
Reddit API docs:
https://www.reddit.com/dev/api/
🤖 70% Discount on the NLP With Transformers in Python course:
https://bit.ly/3DFvvY5
📙 Medium article:
https://towardsdatascience.com/how-to-use-the-reddit-a... | Education | 627 | 11 |
scJsty_DR3o | UCv83tO5cePwHMt1952IVVHw | How to Build Q&A Models in Python (Transformers) | 2021-02-17 21:03:29 UTC | 2021-02-19 15:00:21 UTC | 1189 seconds | In this video we'll cover how to build a question-answering model in Python using HuggingFace's Transformers.
You will need to install the transformers library with:
pip install transformers
Alongside either TensorFlow or PyTorch (to follow this video exactly you will need PyTorch). To install TensorFlow just type:
p... | Education | 151 | 1 |
QJq9RTp_OVE | UCv83tO5cePwHMt1952IVVHw | How-to Decode Outputs From NLP Models (Python) | 2021-02-21 18:02:42 UTC | 2021-02-24 15:00:10 UTC | 577 seconds | In this video, we will cover three ways to decode the output probabilities from NLP models - greedy search, random sampling, and beam search.
Learning how to decode outputs can make a huge difference in diagnosing model issues and improving text output quality - and as an added bonus it's super easy.
One of the often... | Education | 28 | 0 |
TCZgXFPNnbc | UCv83tO5cePwHMt1952IVVHw | Identify Stocks on Reddit with SpaCy (NER in Python) | 2021-03-01 21:47:29 UTC | 2021-03-03 14:27:48 UTC | 1307 seconds | We will learn how to process unstructured text data from Reddit and extract organization names so that any further analysis is automatically classified and results assigned to the correct stocks.
Organizations are mentioned in each subreddit in a variety of formats. Typically we will find two formats:
- Organization ... | Education | 33 | 0 |
yDGo9z_RlnE | UCv83tO5cePwHMt1952IVVHw | Sentiment Analysis on ANY Length of Text With Transformers (Python) | 2021-03-10 08:15:21 UTC | 2021-03-10 13:15:03 UTC | 1630 seconds | The de-facto standard in many natural language processing (NLP) tasks nowadays is to use a transformer. Text generation? Transformer. Question-and-answering? Transformer. Language classification? Transformer!
However, one of the problems with many of these models (a problem that is not just restricted to transformer m... | Education | 111 | 2 |
9Od9-DV9kd8 | UCv83tO5cePwHMt1952IVVHw | Unicode Normalization for NLP in Python | 2021-03-16 09:27:24 UTC | 2021-03-17 13:30:00 UTC | 927 seconds | ℕ𝕠-𝕠𝕟𝕖 𝕚𝕟 𝕥𝕙𝕖𝕚𝕣 𝕣𝕚𝕘𝕙𝕥 𝕞𝕚𝕟𝕕 𝕨𝕠𝕦𝕝𝕕 𝕖𝕧𝕖𝕣 𝕦𝕤𝕖 𝕥𝕙𝕖𝕤𝕖 𝕒𝕟𝕟𝕠𝕪𝕚𝕟𝕘 𝕗𝕠𝕟𝕥 𝕧𝕒𝕣𝕚𝕒𝕟𝕥𝕤. 𝕋𝕙𝕖 𝕨𝕠𝕣𝕤𝕥 𝕥𝕙𝕚𝕟𝕘, 𝕚𝕤 𝕚𝕗 𝕪𝕠𝕦 𝕕𝕠 𝕒𝕟𝕪 𝕗𝕠𝕣𝕞 𝕠𝕗 ℕ𝕃ℙ 𝕒𝕟𝕕 𝕪𝕠𝕦 𝕙𝕒𝕧𝕖 𝕔𝕙𝕒𝕣𝕒𝕔𝕥𝕖𝕣𝕤 𝕝𝕚𝕜𝕖 𝕥𝕙𝕚𝕤 𝕚𝕟 𝕪𝕠𝕦𝕣 𝕚𝕟𝕡𝕦𝕥, 𝕪𝕠𝕦𝕣 𝕥𝕖𝕩𝕥 𝕓𝕖𝕔�... | Education | 43 | 0 |
2qJavL-VX9Y | UCv83tO5cePwHMt1952IVVHw | The NEW Match-Case Statement in Python 3.10 | 2021-03-17 20:37:52 UTC | 2021-03-19 16:00:03 UTC | 1088 seconds | Python 3.10 is beginning to fill-out with plenty of fascinating new features. One of those, in particular, caught my attention - structural pattern matching - or as most of us will know it, switch/case statements.
Switch-statements have been absent from Python despite being a common feature of most languages. Python i... | Education | 310 | 11 |
pjtnkCGElcE | UCv83tO5cePwHMt1952IVVHw | Multi-Class Language Classification With BERT in TensorFlow | 2021-03-24 17:51:01 UTC | 2021-03-25 16:00:15 UTC | 2604 seconds | Chapters for each section of the video (preprocessing, model build, prediction) are in the video timeline.
Transformers have been described as the fourth pillar of deep learning [1], alongside the three big neural net architectures of CNNs, RNNs, and MLPs.
However, from the perspective of natural language processing ... | Education | 264 | 1 |
JkeNVaiUq_c | UCv83tO5cePwHMt1952IVVHw | How to Build Python Packages for Pip | 2021-04-02 14:51:14 UTC | 2021-04-02 15:19:32 UTC | 1267 seconds | The most powerful feature of Python is its community. Almost every use-case out there has a package built specifically for it.
Need to send mobile/email alerts? pip install knockknock - Build ML apps? pip install streamlit - Bored of your terminal? pip install colorama - It's too easy!
I know this is obvious, b... | Education | 390 | 11 |
4Jmq28RQ3hU | UCv83tO5cePwHMt1952IVVHw | How-to Structure a Q&A ML App | 2021-04-09 15:02:44 UTC | 2021-04-09 15:22:50 UTC | 585 seconds | ▶️ Stoic Q&A App Playlist: https://www.youtube.com/playlist?list=PLIUOU7oqGTLixb-CatMxNCO-mJioMmZEB
I'm planning on doing something different, a series of videos where we work through the steps - from start-to-finish - of (attempting) to build a Q&A web app that answers our questions with Stoic answers.
In this video... | Education | 46 | 0 |
Vwq7Ucp9UCw | UCv83tO5cePwHMt1952IVVHw | How to Index Q&A Data With Haystack and Elasticsearch | 2021-04-11 21:30:32 UTC | 2021-04-12 15:00:11 UTC | 807 seconds | ▶️ Stoic Q&A App Playlist: https://www.youtube.com/playlist?list=PLIUOU7oqGTLixb-CatMxNCO-mJioMmZEB
The second video in 'Building a Stoic Q&A App' - here we're setting up Elasticsearch and Haystack to store the data (Meditations) ready for retrieval when we ask our app questions.
Find the code here:
https://github.co... | Education | 79 | 3 |
DBsxUSUhfRg | UCv83tO5cePwHMt1952IVVHw | Q&A Document Retrieval With DPR | 2021-04-12 14:44:59 UTC | 2021-04-15 15:00:10 UTC | 890 seconds | ▶️ Stoic Q&A App Playlist: https://www.youtube.com/playlist?list=PLIUOU7oqGTLixb-CatMxNCO-mJioMmZEB
The third video in building our Stoic Q&A app.
In open-domain question answering, we typically design a model architecture that contains a data source, retriever, and reader/generator.
The first of these components is... | Education | 57 | 0 |
QrzHImDEq_w | UCv83tO5cePwHMt1952IVVHw | How to Use Type Annotations in Python | 2021-04-23 21:44:38 UTC | 2021-04-27 14:53:25 UTC | 907 seconds | Type annotations - also known as type signatures - are used to indicate the datatypes of variables and input/outputs of functions and methods.
In many languages, datatypes are explicitly stated. In these languages, if you don't declare your datatype - the code will not run.
Type annotations have a long and convoluted... | Education | 132 | 3 |
2tdLYIKPafc | UCv83tO5cePwHMt1952IVVHw | Extractive Q&A With Haystack and FastAPI in Python | 2021-04-26 22:03:55 UTC | 2021-04-29 15:00:04 UTC | 1058 seconds | ▶️ Stoic Q&A App Playlist: https://www.youtube.com/playlist?list=PLIUOU7oqGTLixb-CatMxNCO-mJioMmZEB
In this video we work through building an extractive Q&A stack using Haystack, and embedding it within a FastAPI instance in Python.
We use the BERT transformer for our reader model, alongside Elasticsearch and the BM2... | Education | 71 | 1 |
jVPd7lEvjtg | UCv83tO5cePwHMt1952IVVHw | Sentence Similarity With Transformers and PyTorch (Python) | 2021-05-04 15:25:17 UTC | 2021-05-05 15:00:20 UTC | 1270 seconds | Easy mode: https://youtu.be/Ey81KfQ3PQU
All we ever seem to talk about nowadays are BERT this, BERT that. I want to talk about something else, but BERT is just too good - so this video will be about BERT for sentence similarity.
A big part of NLP relies on similarity in highly-dimensional spaces. Typically an NLP s... | Education | 233 | 2 |
Ey81KfQ3PQU | UCv83tO5cePwHMt1952IVVHw | Sentence Similarity With Sentence-Transformers in Python | 2021-05-04 19:55:42 UTC | 2021-05-05 15:00:09 UTC | 370 seconds | 🎁 Free NLP for Semantic Search Course:
https://www.pinecone.io/learn/nlp
Hard mode: https://youtu.be/jVPd7lEvjtg
All we ever seem to talk about nowadays are BERT this, BERT that. I want to talk about something else, but BERT is just too good - so this video will be about BERT for sentence similarity.
A big part o... | Education | 371 | 4 |
W8ZPQOcHnlE | UCv83tO5cePwHMt1952IVVHw | NER With Transformers and spaCy (Python) | 2021-05-09 20:57:10 UTC | 2021-05-11 15:00:28 UTC | 567 seconds | Named entity recognition (NER) consists of extracting 'entities' from text - what we mean by that is given the sentence:
"Apple reached an all-time high stock price of 143 dollars this January."
We might want to extract the key pieces of information - or 'entities' - and categorize each of those entities. Like so:
-... | Education | 120 | 2 |
q9NS5WpfkrU | UCv83tO5cePwHMt1952IVVHw | Training BERT #1 - Masked-Language Modeling (MLM) | 2021-05-19 09:31:26 UTC | 2021-05-19 14:51:39 UTC | 984 seconds | 🎁 Free NLP for Semantic Search Course:
https://www.pinecone.io/learn/nlp
BERT, everyone's favorite transformer costs Google ~$7K to train (and who knows how much in R&D costs). From there, we write a couple of lines of code to use the same model - all for free.
BERT has enjoyed unparalleled success in NLP thanks to ... | Education | 277 | 3 |
R6hcxMMOrPE | UCv83tO5cePwHMt1952IVVHw | Training BERT #2 - Train With Masked-Language Modeling (MLM) | 2021-05-19 11:38:10 UTC | 2021-05-19 14:51:49 UTC | 1666 seconds | 🎁 Free NLP for Semantic Search Course:
https://www.pinecone.io/learn/nlp
BERT has enjoyed unparalleled success in NLP thanks to two unique training approaches, masked-language modeling (MLM), and next sentence prediction (NSP).
In many cases, we might be able to take the pre-trained BERT model out-of-the-box and app... | Education | 223 | 1 |
1gN1snKBLP0 | UCv83tO5cePwHMt1952IVVHw | Training BERT #3 - Next Sentence Prediction (NSP) | 2021-05-23 18:14:04 UTC | 2021-05-25 14:56:47 UTC | 823 seconds | Next sentence prediction (NSP) is one-half of the training process behind the BERT model (the other being masked-language modeling - MLM).
Where MLM teaches BERT to understand relationships between words - NSP teaches BERT to understand relationships between sentences.
In the original BERT paper, it was found that wi... | Education | 94 | 6 |
x1lAcT3xl5M | UCv83tO5cePwHMt1952IVVHw | Training BERT #4 - Train With Next Sentence Prediction (NSP) | 2021-05-27 15:52:57 UTC | 2021-05-27 16:15:39 UTC | 2205 seconds | Next sentence prediction (NSP) is one-half of the training process behind the BERT model (the other being masked-language modeling - MLM).
Although NSP (and MLM) are used to pre-train BERT models, we can use these exact methods to further pre-train our models to better understand the specific style of language in our ... | Education | 95 | 1 |
5-A435hIYio | UCv83tO5cePwHMt1952IVVHw | New Features in Python 3.10 | 2021-06-03 16:41:56 UTC | 2021-06-08 15:00:02 UTC | 800 seconds | The Python 3.10 release has several new features like structural pattern matching, a new typing Union operator, and parenthesized context managers!
Python 3.10 has now been released, here we test all of the best new features introduced.
We'll cover some of the most interesting additions to Python - structural pattern... | Education | 375 | 2 |
IC9FaVPKlYc | UCv83tO5cePwHMt1952IVVHw | Training BERT #5 - Training With BertForPretraining | 2021-06-04 05:13:06 UTC | 2021-06-15 15:00:19 UTC | 1306 seconds | NSP Logic
https://youtu.be/1gN1snKBLP0
MLM Logic
https://youtu.be/q9NS5WpfkrU
🤖 70% Discount on the NLP With Transformers in Python course:
https://bit.ly/3DFvvY5
📙 Medium article:
https://towardsdatascience.com/how-to-train-bert-aaad00533168
📖 Here's a free link:
https://towardsdatascience.com/how-to-train-bert... | Education | 128 | 1 |
fA0dFQacmic | UCv83tO5cePwHMt1952IVVHw | FREE 11 Hour NLP Transformers Course (Next 3 Days Only) | 2021-06-04 07:56:44 UTC | 2021-06-04 13:00:19 UTC | 267 seconds | The offer has now expired! You can find the final 70% discount here:
https://bit.ly/3DFvvY5
In total, 10823 people redeemed the code - which is incredible, I'm very happy so many of you were interested in the course and I hope it will help many of you in learning about transformers and NLP where it may have been too e... | Education | 51 | 0 |
GhGUZrcB-WM | UCv83tO5cePwHMt1952IVVHw | How-to Use HuggingFace's Datasets - Transformers From Scratch #1 | 2021-06-21 21:56:31 UTC | 2021-06-22 13:00:07 UTC | 861 seconds | How can we build our own custom transformer models?
Maybe we'd like our model to understand a less common language, how many transformer models out there have been trained on Piemontese or the Nahuatl languages?
In that case, we need to do something different. We need to build our own model - from scratch.
In this v... | Education | 147 | 3 |
JIeAB8vvBQo | UCv83tO5cePwHMt1952IVVHw | Build a Custom Transformer Tokenizer - Transformers From Scratch #2 | 2021-06-22 20:07:37 UTC | 2021-06-24 14:00:06 UTC | 857 seconds | How can we build our own custom transformer models?
Maybe we'd like our model to understand a less common language, how many transformer models out there have been trained on Piemontese or the Nahuatl languages?
In that case, we need to do something different. We need to build our own model - from scratch.
In this v... | Education | 80 | 3 |
ziiF1eFM3_4 | UCv83tO5cePwHMt1952IVVHw | 3 Vector-based Methods for Similarity Search (TF-IDF, BM25, SBERT) | 2021-06-28 13:25:28 UTC | 2021-06-29 13:00:23 UTC | 1764 seconds | Vector similarity search is one of the fastest-growing domains in AI and machine learning. At its core, it is the process of matching relevant pieces of information together.
Similarity search is a complex topic and there are countless techniques for building effective search engines.
In this video, we'll cover three... | Education | 416 | 1 |
AY62z7HrghY | UCv83tO5cePwHMt1952IVVHw | 3 Traditional Methods for Similarity Search (Jaccard, w-shingling, Levenshtein) | 2021-06-28 17:44:01 UTC | 2021-06-29 12:00:04 UTC | 1520 seconds | Similarity search is one of the fastest-growing domains in AI and machine learning. At its core, it is the process of matching relevant pieces of information together.
Similarity search is a complex topic and there are countless techniques for building effective search engines.
In this video, we'll cover three tradit... | Education | 86 | 0 |
heTYbpr9mD8 | UCv83tO5cePwHMt1952IVVHw | Building MLM Training Input Pipeline - Transformers From Scratch #3 | 2021-07-02 15:28:46 UTC | 2021-07-05 14:00:30 UTC | 1392 seconds | The input pipeline of our training process is the more complex part of the entire transformer build. It consists of us taking our raw OSCAR training data, transforming it, and preparing it for Masked-Language Modeling (MLM). Finally, we load our data into a DataLoader ready for training!
Part 1: https://youtu.be/GhGUZ... | Education | 69 | 0 |
ee71R4Cqb5o | UCv83tO5cePwHMt1952IVVHw | Angular App Setup With Material - Stoic Q&A #5 | 2021-07-05 08:50:04 UTC | 2021-07-20 14:00:28 UTC | 814 seconds | ▶️ Stoic Q&A App Playlist: https://www.youtube.com/playlist?list=PLIUOU7oqGTLixb-CatMxNCO-mJioMmZEB
The fifth video in our Stoic Q&A series - setting up our Angular app with Angular Material.
Prerequisites:
Installation of Node.js and NPM - https://nodejs.org/en/
Angular - https://angular.io/guide/setup-local
👾 Dis... | Science & Technology | 17 | 0 |
35Pdoyi6ZoQ | UCv83tO5cePwHMt1952IVVHw | Training and Testing an Italian BERT - Transformers From Scratch #4 | 2021-07-05 18:22:41 UTC | 2021-07-06 13:00:03 UTC | 1838 seconds | We need two things for training, our DataLoader and a model. The DataLoader we have — but no model.
For training, we need a raw (not pre-trained) RobertaForMaskedLM. To create that, we first need to create a RoBERTa config object to describe the parameters we’d like to initialize FiliBERTo with.
Once we have our mode... | Science & Technology | 94 | 1 |
sKyvsdEv6rk | UCv83tO5cePwHMt1952IVVHw | Faiss - Introduction to Similarity Search | 2021-07-09 13:47:26 UTC | 2021-07-13 15:00:19 UTC | 1896 seconds | Full Similarity Search Playlist:
https://www.youtube.com/watch?v=AY62z7HrghY&list=PLIUOU7oqGTLhlWpTz4NnuT3FekouIVlqc&index=1
Facebook AI Similarity Search (FAISS) is one of the most popular implementations of efficient similarity search, but what is it - and how can we use it?
What is it that makes FAISS special? How... | Science & Technology | 354 | 5 |
bWLvGGJLzF8 | UCv83tO5cePwHMt1952IVVHw | Why are there so many Tokenization methods in HF Transformers? | 2021-07-27 07:12:07 UTC | 2021-07-27 14:00:10 UTC | 1080 seconds | HuggingFace's transformers library is the de-facto standard for NLP - used by practitioners worldwide, it's powerful, flexible, and easy to use. It achieves this through a fairly large (and complex) code-base, which has resulted in the question:
"Why are there so many tokenization methods in HuggingFace transformers?"... | Science & Technology | 51 | 0 |
B7wmo_NImgM | UCv83tO5cePwHMt1952IVVHw | Choosing Indexes for Similarity Search (Faiss in Python) | 2021-08-09 14:33:47 UTC | 2021-08-09 15:04:10 UTC | 1893 seconds | Facebook AI Similarity Search (Faiss) is a game-changer in the world of search. It allows us to efficiently search a huge range of media, from GIFs to articles - with incredible accuracy in sub-second timescales for billion+ size datasets.
The success in Faiss is due to many reasons. One of those, in particular, is it... | Science & Technology | 122 | 1 |
e_SBq3s20M8 | UCv83tO5cePwHMt1952IVVHw | Locality Sensitive Hashing (LSH) for Search with Shingling + MinHashing (Python) | 2021-08-19 16:53:50 UTC | 2021-08-20 16:00:16 UTC | 1627 seconds | Locality sensitive hashing (LSH) is a widely popular technique used in approximate nearest neighbor (ANN) search. The solution to efficient similarity search is a profitable one - it is at the core of several billion (and even trillion) dollar companies.
LSH consists of a variety of different methods. In this video, w... | Science & Technology | 208 | 19 |
8bOrMqEdfiQ | UCv83tO5cePwHMt1952IVVHw | How LSH Random Projection works in search (+Python) | 2021-08-24 05:09:11 UTC | 2021-08-24 16:00:04 UTC | 1148 seconds | Locality sensitive hashing (LSH) is a widely popular technique used in approximate similarity search. The solution to efficient similarity search is a profitable one - it is at the core of several billion (and even trillion) dollar companies.
The problem with similarity search is scale. Many companies deal with millio... | Science & Technology | 66 | 3 |
ZLfdQq_u7Eo | UCv83tO5cePwHMt1952IVVHw | IndexLSH for Fast Similarity Search in Faiss | 2021-08-24 05:25:21 UTC | 2021-08-24 16:00:12 UTC | 1119 seconds | Faiss - or Facebook AI Similarity Search - is an open-source framework built for enabling similarity search.
Faiss has many super-efficient implementations of different indexes that we can use in similarity search. That long list of indexes includes IndexLSH - an easy-to-use implementation of everything we have co... | Science & Technology | 27 | 0 |
BMYBwbkbVec | UCv83tO5cePwHMt1952IVVHw | Faiss - Vector Compression with PQ and IVFPQ (in Python) | 2021-08-30 14:35:01 UTC | 2021-08-30 15:30:04 UTC | 1161 seconds | So far we’ve worked through the logic behind a simple, readable implementation of product quantization (PQ) in Python for semantic search. Realistically we wouldn’t use this because it is not optimized and we already have excellent implementations elsewhere. Instead, we would use a library like Faiss (Facebook AI Simil... | Science & Technology | 36 | 1 |
t9mRf2S5vDI | UCv83tO5cePwHMt1952IVVHw | Product Quantization for Vector Similarity Search (+ Python) | 2021-08-30 15:22:47 UTC | 2021-08-30 15:37:46 UTC | 1777 seconds | Vector similarity search can require huge amounts of memory. Indexes containing 1M dense vectors (a small dataset in today’s world) will often require several GBs of memory to store. When building recommendation systems or semantic search engines, this is not acceptable.
The problem of excessive memory usage is exaspe... | Science & Technology | 116 | 2 |
GEhmmcx1lvM | UCv83tO5cePwHMt1952IVVHw | Composite Indexes and the Faiss Index Factory | 2021-09-11 17:27:12 UTC | 2021-09-24 12:53:58 UTC | 1063 seconds | In the world of vector search, there are many indexing methods and vector processing techniques that allow us to prioritize between recall, latency, and memory usage.
Using specific methods such as IVF, PQ, or HNSW, we can often return good results. But for best performance we will usually want to use composite indexe... | Science & Technology | 21 | 0 |
3Wqh4iUupbM | UCv83tO5cePwHMt1952IVVHw | Best Indexes for Similarity Search in Faiss | 2021-09-12 07:02:26 UTC | 2021-09-24 12:54:07 UTC | 1582 seconds | In the world of vector search, there are many indexing methods and vector processing techniques that allow us to prioritize between recall, latency, and memory usage.
Using specific methods such as IVF, PQ, or HNSW, we can often return good results. But for best performance we will usually want to use composite indexe... | Science & Technology | 31 | 0 |
cR4qMSIvX28 | UCv83tO5cePwHMt1952IVVHw | How to Build a Bert WordPiece Tokenizer in Python and HuggingFace | 2021-09-13 20:13:08 UTC | 2021-09-14 13:30:06 UTC | 1880 seconds | Building a transformer model from scratch can often be the only option for many more specific use cases. Although BERT and other transformer models have been pre-trained for a vast number of languages and domains, they do not cover everything.
Often, it is these less common use cases that stand to gain the most from h... | Science & Technology | 95 | 1 |
H_kJDHvu-v8 | UCv83tO5cePwHMt1952IVVHw | Metadata Filtering for Vector Search + Latest Filter Tech | 2021-09-20 12:23:11 UTC | 2021-09-20 14:04:27 UTC | 2054 seconds | Vector similarity search makes massive datasets searchable in fractions of a second. Yet despite the brilliance and utility of this technology, often what seem to be the most straightforward problems are the most difficult to solve. Such as filtering.
Filtering takes the top place in being seemingly simple — but actua... | Science & Technology | 20 | 0 |
r-zQQ16wTCA | UCv83tO5cePwHMt1952IVVHw | Build NLP Pipelines with HuggingFace Datasets | 2021-09-20 14:58:03 UTC | 2021-09-23 13:30:07 UTC | 2030 seconds | HF Datasets is an essential tool for NLP practitioners - hosting over 1.4K (mostly) high-quality language-focused datasets, and an easy-to-use treasure trove of functions for building efficient pre-processing pipelines.
In this article, we will take a look at the massive repository of datasets available, and explore s... | Science & Technology | 53 | 1 |
QvKMwLjdK-s | UCv83tO5cePwHMt1952IVVHw | HNSW for Vector Search Explained and Implemented with Faiss (Python) | 2021-09-29 08:13:49 UTC | 2021-10-05 13:00:23 UTC | 2075 seconds | Hierarchical Navigable Small World (HNSW) graphs are among the top-performing indexes for vector similarity search. HNSW is a hugely popular technology that time and time again produces state-of-the-art performance with super-fast search speeds and flawless recall — HNSW is not to be missed.
Despite being a popular an... | Science & Technology | 131 | 3 |
g_yMowQikOE | UCv83tO5cePwHMt1952IVVHw | Intro to APIs in Python - API Series #1 | 2021-09-29 12:21:47 UTC | 2021-09-29 14:00:18 UTC | 1704 seconds | Taking those first steps into interacting with the web using Python can seem daunting - but it need not be. It is a surprisingly simple process, with well established rules and guidelines.
We'll cover the absolute essentials for getting started, including:
- Application Program Interfaces (APIs)
- Javascript Object N... | Science & Technology | 119 | 0 |
bVZJ_O_-0RE | UCv83tO5cePwHMt1952IVVHw | Intro to Dense Vectors for NLP and Vision | 2021-10-04 08:28:38 UTC | 2021-10-12 17:47:15 UTC | 2629 seconds | There is perhaps no greater component to the success of modern Natural Language Processing (NLP) technology than vector representations of language. The meteoric early 2010s rise of NLP was ignited with the introduction of word2vec by a team lead by Tomáš Mikolov in 2013.
Word2vec is one of the most iconic and earlies... | Science & Technology | 92 | 0 |
MF75aNH3Gjs | UCv83tO5cePwHMt1952IVVHw | API Series #2 - Building an API with Flask in Python | 2021-10-05 07:01:25 UTC | 2021-10-07 14:52:32 UTC | 1902 seconds | Next video - how to deploy to the cloud: https://youtu.be/3fsIcMgUOY8
How can we set up a way to communicate from one software instance to another? It sounds simple, and — to be completely honest — it is.
All we need is an API.
An API (Application Programming Interface) is a simple interface that defines the types o... | Science & Technology | 117 | 2 |
WS1uVMGhlWQ | UCv83tO5cePwHMt1952IVVHw | Intro to Sentence Embeddings with Transformers | 2021-10-19 09:44:58 UTC | 2021-10-20 17:06:20 UTC | 1866 seconds | Transformers have wholly rebuilt the landscape of natural language processing (NLP). Before transformers, we had okay translation and language classification thanks to recurrent neural nets (RNNs) — their language comprehension was limited and led to many minor mistakes, and coherence over larger chunks of text was pra... | Science & Technology | 188 | 1 |
aSx0jg9ZILo | UCv83tO5cePwHMt1952IVVHw | Fine-tune Sentence Transformers the OG Way (with NLI Softmax loss) | 2021-10-22 14:16:49 UTC | 2021-10-22 14:39:46 UTC | 2223 seconds | Sentence embeddings with transformers can be used across a range of applications, such as semantic textual similarity (STS), semantic clustering, or information retrieval (IR) using concepts rather than words.
This video dives deeper into the training process of the first sentence transformer, sentence-BERT, or more c... | Science & Technology | 83 | 0 |
or5ew7dqA-c | UCv83tO5cePwHMt1952IVVHw | Fine-tune High Performance Sentence Transformers (with Multiple Negatives Ranking) | 2021-10-25 20:18:30 UTC | 2021-10-26 13:00:22 UTC | 2213 seconds | Transformer-produced sentence embeddings have come a long way in a very short time. Starting with the slow but accurate similarity prediction of BERT cross-encoders, the world of sentence embeddings was ignited with the introduction of SBERT in 2019. Since then, many more sentence transformers have been introduced. The... | Science & Technology | 86 | 0 |
iCkftKsnQgg | UCv83tO5cePwHMt1952IVVHw | Hybrid Search Walkthrough in Pinecone | 2021-10-29 01:44:06 UTC | 2021-10-29 15:05:00 UTC | 1040 seconds | Pinecone offers a production-ready vector database for high performance and reliable *semantic search* at scale. But did you know Pinecone's semantic search can be paired with the more traditional keyword search?
Semantic search is a compelling technology allowing us to search using abstract concepts and *meaning* rat... | Science & Technology | 17 | 1 |
3fsIcMgUOY8 | UCv83tO5cePwHMt1952IVVHw | API Series #3 - How to Deploy Flask APIs to the Cloud (GCP) | 2021-11-01 23:16:31 UTC | 2021-11-02 14:30:00 UTC | 806 seconds | Building that first API is for many of us, a significant step towards creating impactful tools that may one day be used by many developers. But often those APIs don't make it out of our local machines.
Fortunately, it's incredibly easy to deploy APIs. Assuming you have no idea what you're doing right now - you will pr... | Science & Technology | 75 | 2 |
NNS5pOpjvAQ | UCv83tO5cePwHMt1952IVVHw | All You Need to Know on Multilingual Sentence Vectors (1 Model, 50+ Languages) | 2021-11-04 11:27:18 UTC | 2021-11-04 13:00:10 UTC | 2392 seconds | We’ve learned about how sentence transformers can be used to create high-quality vector representations of text. We can then use these vectors to find similar vectors, which can be used for many applications such as semantic search or topic modeling.
These models are very good at producing meaningful, information-dens... | Science & Technology | 30 | 0 |
-td57YvJdHc | UCv83tO5cePwHMt1952IVVHw | Question-Answering in NLP (Extractive QA and Abstractive QA) | 2021-11-13 19:09:02 UTC | 2021-11-16 12:06:13 UTC | 2886 seconds | Search is a crucial functionality in many applications and companies globally. Whether in manufacturing, finance, healthcare, or *almost* any other industry, organizations have vast internal information and document repositories.
Unfortunately, the scale of many companies’ data means that the organization and accessib... | Science & Technology | 72 | 0 |
pNvujJ1XyeQ | UCv83tO5cePwHMt1952IVVHw | Today Unsupervised Sentence Transformers, Tomorrow Skynet (how TSDAE works) | 2021-11-24 14:20:20 UTC | 2021-11-24 16:24:24 UTC | 2661 seconds | To adapt a pretrained transformer to produce meaningful sentence vectors, we typically need a more supervised fine-tuning approach. We can use datasets like natural language inference (NLI) pairs, labeled semantic textual similarity (STS) data, or parallel data (pairs of translations).
For some domains and languages, ... | Science & Technology | 70 | 0 |
3IPCEeh4xTg | UCv83tO5cePwHMt1952IVVHw | Making The Most of Data: Augmented SBERT | 2021-12-16 15:46:03 UTC | 2021-12-17 14:24:40 UTC | 3310 seconds | 🎁 Free NLP for Semantic Search Course:
https://www.pinecone.io/learn/nlp
ML models are data-hungry. They consume massive amounts of data to identify generalized patterns and apply those learned patterns to new data.
As models get bigger, so do datasets. And although we have seen an explosion of data in the past deca... | Science & Technology | 42 | 0 |
mjKqP3kRxbQ | UCv83tO5cePwHMt1952IVVHw | Building Transformer Tokenizers (Dhivehi NLP #1) | 2021-12-28 15:02:22 UTC | 2021-12-28 15:45:03 UTC | 1982 seconds | 🎁 Free NLP for Semantic Search Course:
https://www.pinecone.io/learn/nlp
Get in touch with Ashraq:
https://www.linkedin.com/in/ismailashraq/
The language of Dhivehi (or Maldivian) is fascinating. It uses a complex writing system known as Thaana, and I absolutely cannot comprehend any of it. It is so wildly different... | Science & Technology | 49 | 0 |
a8jyue22SJM | UCv83tO5cePwHMt1952IVVHw | AugSBERT: Domain Transfer for Sentence Transformers | 2022-01-04 05:14:16 UTC | 2022-01-04 14:59:50 UTC | 1750 seconds | 🎁 Free NLP for Semantic Search Course:
https://www.pinecone.io/learn/nlp
When building language models, we can spend months optimizing training and model parameters, but it’s useless if we don't have the correct data.
The success of our language models relies first and foremost on data. The augmented SBERT training ... | Science & Technology | 41 | 0 |
w1dMEWm7jBc | UCv83tO5cePwHMt1952IVVHw | How to build a Q&A AI in Python (Open-domain Question-Answering) | 2022-01-10 07:19:13 UTC | 2022-01-11 14:00:20 UTC | 2364 seconds | 🎁 Free NLP for Semantic Search Course:
https://www.pinecone.io/learn/nlp
How can we design these natural, human-like Q&A interfaces? The answer is open-domain question-answering (ODQA). ODQA allows us to use natural language to query a database.
That means that, given a dataset like a set of internal company documen... | Science & Technology | 66 | 1 |
-fzCSPsfMic | UCv83tO5cePwHMt1952IVVHw | How to build a Q&A Reader Model in Python (Open-domain QA) | 2022-01-18 12:17:09 UTC | 2022-01-18 16:37:37 UTC | 1504 seconds | 🎁 Free NLP for Semantic Search Course:
https://www.pinecone.io/learn/nlp
Open-domain question-answering (ODQA) is a wildly popular *pipeline* of databases and language models that allow us to ask a machine human-like questions and return comprehensible and even intelligent answers.
Despite the outward guise of simpl... | Science & Technology | 26 | 0 |
JLKUV-LiXjk | UCv83tO5cePwHMt1952IVVHw | Streamlit for ML #1 - Installation and API | 2022-01-25 12:04:00 UTC | 2022-01-25 16:00:09 UTC | 735 seconds | ▶️ Streamlit for ML Part 2:
https://www.youtube.com/watch?v=U0EoaFFGyTg&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=2
Streamlit has proven itself as an incredibly popular tool for quickly putting together high-quality ML-oriented web apps. More recently, it has seen wider adoption in production environments by ever-... | Science & Technology | 32 | 0 |
U0EoaFFGyTg | UCv83tO5cePwHMt1952IVVHw | Streamlit for ML #2 - ML Models and APIs | 2022-01-26 16:07:51 UTC | 2022-01-26 16:30:36 UTC | 911 seconds | ▶️ Streamlit for ML Part 3:
https://www.youtube.com/watch?v=lYDiSCDcxmc&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=3
Streamlit has proven itself as an incredibly popular tool for quickly putting together high-quality ML-oriented web apps. More recently, it has seen wider adoption in production environments by ever-... | Science & Technology | 19 | 0 |
lYDiSCDcxmc | UCv83tO5cePwHMt1952IVVHw | Streamlit for ML #3 - Make Apps Fast with Caching | 2022-01-27 13:13:14 UTC | 2022-01-27 15:00:36 UTC | 584 seconds | ▶️ Streamlit for ML Part 4:
https://www.youtube.com/watch?v=XdxeKiY2UXg&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=4
Streamlit has proven itself as an incredibly popular tool for quickly putting together high-quality ML-oriented web apps. More recently, it has seen wider adoption in production environments by ever-... | Science & Technology | 24 | 0 |
XdxeKiY2UXg | UCv83tO5cePwHMt1952IVVHw | Streamlit for ML #4 - Adding Bootstrap Components | 2022-01-28 10:05:43 UTC | 2022-01-28 15:11:42 UTC | 590 seconds | ▶️ Streamlit for ML Part 5.1:
https://www.youtube.com/watch?v=SGazDb8o-to&list=PLIUOU7oqGTLg5ssYxPGWaci6695wtosGw&index=5
Streamlit has proven itself as an incredibly popular tool for quickly putting together high-quality ML-oriented web apps. More recently, it has seen wider adoption in production environments by eve... | Science & Technology | 38 | 1 |
JydpRavoJqI | UCv83tO5cePwHMt1952IVVHw | Adding New Doc Stores to Haystack | 2022-02-15 04:56:36 UTC | 2022-03-15 15:00:14 UTC | 1825 seconds | 🥳 Released with Haystack v1.3! Install direct from PyPI with:
pip install 'farm-haystack[pinecone]'
PR:
https://github.com/deepset-ai/haystack/pull/2254
🤖 70% Discount on the NLP With Transformers in Python course:
https://bit.ly/3DFvvY5
🎉 Subscribe for Article and Video Updates!
https://jamescalam.medium.com/s... | Science & Technology | 14 | 0 |
SGazDb8o-to | UCv83tO5cePwHMt1952IVVHw | Streamlit for ML #5.1 - Custom React Components in Streamlit Setup | 2022-02-17 15:24:47 UTC | 2022-02-17 15:45:58 UTC | 1158 seconds | ▶️ 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 n... | Science & Technology | 26 | 1 |
mxm8ihWoVbk | UCv83tO5cePwHMt1952IVVHw | Streamlit for ML #5.2 - MUI Card Component Build | 2022-02-20 15:25:56 UTC | 2022-02-21 14:00:31 UTC | 1619 seconds | ▶️ 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 n... | Science & Technology | 16 | 1 |
lZ2EaPUnV7k | UCv83tO5cePwHMt1952IVVHw | Streamlit for ML #5.3 - Publishing Components to Pip | 2022-02-27 16:28:49 UTC | 2022-02-28 17:00:29 UTC | 858 seconds | 🎁 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 bu... | Science & Technology | 10 | 0 |
J0cntjLKpmU | UCv83tO5cePwHMt1952IVVHw | Train Sentence Transformers by Generating Queries (GenQ) | 2022-03-08 03:10:28 UTC | 2022-03-08 14:52:23 UTC | 1634 seconds | 🎁 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 requir... | Science & Technology | 39 | 0 |
Dn8OYkatiU0 | UCv83tO5cePwHMt1952IVVHw | Testing the New Haystack Doc Store | 2022-03-22 17:15:10 UTC | 2022-03-22 19:26:00 UTC | 1399 seconds | 🥳 Released with Haystack v1.3! Install direct from PyPI with:
pip install 'farm-haystack[pinecone]'
PR:
https://github.com/deepset-ai/haystack/pull/2254
🤖 70% Discount on the NLP With Transformers in Python course:
https://bit.ly/3DFvvY5
🎉 Subscribe for Article and Video Updates!
https://jamescalam.medium.com/s... | Science & Technology | 5 | 0 |
uEbCXwInnPs | UCv83tO5cePwHMt1952IVVHw | Is GPL the Future of Sentence Transformers? | Generative Pseudo-Labeling Deep Dive | 2022-03-29 10:46:39 UTC | 2022-03-30 12:52:39 UTC | 3175 seconds | 🎁 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 ... | Science & Technology | 76 | 2 |
j3psNM5y-eA | UCv83tO5cePwHMt1952IVVHw | Implementing Filters in the New Haystack Doc Store | 2022-04-06 15:53:46 UTC | 2022-04-06 16:26:54 UTC | 1695 seconds | 🥳 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
🤖 70% Discount on the ... | Science & Technology | 3 | 0 |
ok0SDdXdat8 | UCv83tO5cePwHMt1952IVVHw | Spotify's Podcast Search Explained | 2022-04-13 15:02:31 UTC | 2022-04-14 13:14:50 UTC | 2998 seconds | 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, ... | Science & Technology | 58 | 1 |
gVAJ_l_S7uQ | UCv83tO5cePwHMt1952IVVHw | How to learn NLP for free | 2022-04-24 16:41:28 UTC | 2022-04-26 13:05:48 UTC | 1402 seconds | 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 even... | Science & Technology | 165 | 1 |
fb7LENb9eag | UCv83tO5cePwHMt1952IVVHw | BERTopic Explained | 2022-05-10 14:13:06 UTC | 2022-05-11 15:10:23 UTC | 2714 seconds | 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 ... | Science & Technology | 153 | 3 |
O9lrWt15wH8 | UCv83tO5cePwHMt1952IVVHw | Long Form Question Answering (LFQA) in Haystack | 2022-05-17 15:22:17 UTC | 2022-05-17 15:46:21 UTC | 2159 seconds | 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 re... | Science & Technology | 55 | 1 |
uYas6ysyjgY | UCv83tO5cePwHMt1952IVVHw | New GPU-Acceleration for PyTorch on M1 Macs! + using with BERT | 2022-05-22 16:37:37 UTC | 2022-05-24 13:00:34 UTC | 1140 seconds | 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 com... | Science & Technology | 115 | 3 |
FzLIIwiaXSU | UCv83tO5cePwHMt1952IVVHw | How to Build an AI-Powered Video Search App | 2022-06-01 12:37:21 UTC | 2022-06-01 16:29:43 UTC | 1343 seconds | 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 ev... | Science & Technology | 58 | 0 |
xXsDIK9z_fg | UCv83tO5cePwHMt1952IVVHw | Using Semantic Search to Find GIFs | 2022-06-06 09:17:01 UTC | 2022-06-07 12:05:40 UTC | 1050 seconds | 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.... | Science & Technology | 20 | 1 |
_OAU1kQdmgE | UCv83tO5cePwHMt1952IVVHw | How to Learn Data Science | ML | Programming | 2022-06-15 10:37:57 UTC | 2022-06-15 13:11:47 UTC | 992 seconds | In this video I share five of the approaches/thoughts I have regarding learning, in particular for learning data science, machine learning, or programming.
🤖 70% Discount on the NLP With Transformers in Python course:
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https://jamescalam.medium.com/su... | Education | 24 | 0 |
BD9TkvEsKwM | UCv83tO5cePwHMt1952IVVHw | Evaluation Measures for Search and Recommender Systems | 2022-06-25 14:35:27 UTC | 2022-06-28 15:06:40 UTC | 1885 seconds | 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 ret... | Science & Technology | 49 | 0 |
coaaSxys5so | UCv83tO5cePwHMt1952IVVHw | How to build next-level Q&A with OpenAI | 2022-07-06 19:48:54 UTC | 2022-07-07 13:24:35 UTC | 1168 seconds | 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
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