Sanyam Bhutani
@bhutanisanyam1
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👨💻 Sr Data Scientist @h2oai | Previously: @weights_biases 🎙 Podcast Host @ctdsshow 👨🎓 International Fellow @fastdotai 🎲 Grandmaster @Kaggle
India
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This is the best week of my life 🙏
✅ Reached Kaggle Grandmaster tier
✅ My ML Hero & Guru:
@jeremyphoward
was kind enough to host me for an interview about my journey
I promise to continue creating ML content to the best of my ability & sincerely take up competitions next 🍵
This week I'm filling in for regular "chai time data science" podcast host
@bhutanisanyam1
, with a very special interview with a recently-anointed Kaggle grandmaster...
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“Transformers from scratch” by Brandon Rohrer 🤖
This is one of the best write ups, that starts from 0 and explains every single detail of the model architecture.
Even if you need a refresher or don’t, I would still highly recommend reading it:
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Easily the best paper on current State of LLMs! 🙏
A 50 page read but it’s not “just another” survey paper, that only documents facts. The authors actually add very useful commentary capturing all aspects of building Large Language Models.
Hence the result is a collection of
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An absolute masterclass by World's Top Data Scientists 🙏
The awesome Kaggle Grandmaster Team at NVIDIA shares their winning tips and tricks in this series:
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Google Colab has now a Subscription model for Power users:
- Faster GPUs
- Longer runtimes
- More memory
It's for $9.99/Month. I know many power users might enjoy it:
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How to become an expert at any thing 🙏
I rediscovered this gem by
@karpathy
in my bookmarks today
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My favourite LLM paper is finally open source! 🙏
Running a single Large Language Model agent is easy
Running multiple is hard
Running multiple over days of sustained interactions is really hard
I’ve spent the last 3 days reading through the code of the paper that solved
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This is the best resource to get started in NLP in 2023 🙏
In 2 days, I will be kicking off a weekly study group to learn with everyone:
@_lewtun
will kindly join us for an opening AMA
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CS 324 Notes are a LLM Book! 🙏
The Large Language Model course notes are a crispy book covering the foundations
Perfectly structured like an onion, starting at an overview & then levelling up
It’s Mixture of Expert section is one of the best:
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The best summary of Transformers and it’s evolution 🙏
I found
@giffmana
’s slides from 2022 to be the best “pocket reference” on the topic.
Many posts have covered Transformers however this one also covers the state of field before and how it got adopted to different domains.
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Best tutorial on setting up LLMs locally! 🙏
@Rob_Mulla
made an end to end video teaching how to install, run with GUI and connect a Large Language Model to your own data on your own machine.
All open source, running offline:
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Watching “State of GPT” by
@karpathy
is the best 40 minutes you will spend this week 🙏
I actually found it really helpful for filling a lot of my knowledge gaps:
- Comparisons against human brain and LLM brain
- Why prompting works and why is it helpful to ask a model to “be
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Officially wrapping up the
@kaggle
Top Solutions Series 🙏
I’ve hosted over 25 videos sharing and explaining tricks, secrets of Kagglers for all domains of machine learning.
The series is quite complete & I’m graduating to more challenges:
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The best tutorials on building LLM powered applications 📚
@GregKamradt
is an incredible teacher of
@LangChainAI
:
✅ Top down & applied series
✅ Amazing teaching style
✅ Very practical examples
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Papers I’ve read in the last month! 🙏
I’m currently writing a LLM roadmap along with my notes.
If anyone is interested in reviewing and providing early feedback-please reach out!
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MAJOR personal update:
I’ll be starting my work in a full time role
@h2oai
today as a Machine Learning Engineer and AI Content Creator!
I’m really excited to be a part of a team of many of my “ML Heroes” and THE best kagglers.
Recap on my ML journey:
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Implementing LLaMA from scratch! 🙏
This implements a LLM in the style that Karpathy implemented nanoGPT
Even though it focuses on LLaMA-1, it’s a refreshing code first read
Perfect for a Sunday crispy read:
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Arxiv Chat: Chat w the latest papers 🙏
I made a really simple demo that makes it easy for me to understand the latest papers.
The whole app is <100 lines of code:
✅
@LangChainAI
for the main logic
✅
@h2oai
Wave for the UI
✅ ChatGPT for asking Qs
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I'll say this out loud since no one does.
I studied CS at college, it didn't make me a better programmer.
Practising Programming makes you a better programmer, not studying it. If you're starting your ML Journey,trust me a "CS background" won't be as helpful as practising code
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Run 13B model on an iPhone! 🤯
Just finished reading
@Tim_Dettmers
’ amazing work on SpQR.
SpQR unlocks 3.35 bit quantisation which lets us run 33B models on 3090s and 13B models on an iPhone.
Here are my notes from the paper:
- Quantisation is basically like compressing the
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Insanely detailed notes on Training LLMs! 🙏
@StasBekman
has shared his field notes on training foundational models.
These are insanely detailed, cover a lot of gotchas and caveats.
A crispy read with well documented code, in depth discussions:
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If you're looking for the best resources to prepare for ML interviews in 2020,
Here's a wiki from the
@fastdotai
forums:
Also, all contributions are welcomed!
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"Start by learning the basics really well [..] Most advanced research projects require you to be excellent at the basics [..]
@AndrewYNg
always told me to work on thorough mastery of these basics"
Read the complete interview w
@goodfellow_ian
@hackernoon
:
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NLP for absolute beginners 🙏
@jeremyphoward
kindly shared the Stanford materials which are incredibly high signal resource for NLP.
Here’s another tutorial teaching you the absolute NLP basics upto how to make a submission on Kaggle, by Jeremy himself!
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We can now train a 7B model from scratch on a single GPU! 🤯
DeepSpeed Chat: a framework offering insane optimisations and speed ups for training RLHF models:
✅ Efficient and more affordable
✅ Insane Scalability
✅ Easy to use scripts
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Simulating a software company with LLMs! 🚀
Remember the 25 agents living in a simulation? This does the same but for a software company
ChatDev asks the questions around effectively getting Large Language Model agents collaborate on writing entire code bases:
- Writing a
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I'm tea-ry eyed. I can't believe this :')
I've reached the
@kaggle
Grandmaster tier today! Thank you so much everyone!🍵
+ My sincerest gratitude to
@jeremyphoward
for introducing me to Kaggle and to
@vopani
for pushing me to pursue it! 🙏
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The definitive guide to RAG in production! 🙏
@GokuMohandas
walks us through implementing RAG from scratch, building a scalable app
It now has updated discussion on embedding fine-tuning, re-ranking and effectively routing requests
I think this is easily the most complete
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Outperforming LLMs with 2000x smaller models! 🚀
“Distilling Step-by-Step!” is an incredible paper showcasing the promise of using CoT prompting with LLMs to generate steps of logical thinking and high quality labels that can produce great smaller models:
✅ Outperforms both
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My favourite LLM blogs! 🙏
For your weekend learning, here’s an opinionated list of my favourite Large Language Model educators
Pick any or all of their articles and read them cover to cover
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The Deep Learning book study group 🙏
Starting this Saturday, we will be going through the Bible for understanding the basics of DL 📚
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This is just surreal!
I just won the
@hackernoon
Contributor of the year award for 2 categories! 🙏🍵
- Machine Learning
- Tutorial
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CS 25 has a great roadmap of LLM papers! 🙏
Transformers United has great guest lectures spanning the foundations of Large Language Models
An underrated aspect of the course is the curated list of papers on every topic
Perfect for your weekend reads:
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Combining Knowledge Graphs and LLMs! 🙏
To my surprise, this paper is extremely detailed around training strategies of building such models and goes beyond “just prompting” ChatGPT and GPT-4.
In fact, I realised after reading-it doesn’t even mention these models in most of the
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I finally got my copy of Deep Learning w Python by
@fchollet
! 📚
I couldn't be more excited about this 🙏
I'll be starting a reading group on Jan 8, and Francois has kindly agreed to join for an AMA! 🍵
Please send your Qs around Keras/the book as here!
Links TBD Soon!
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A 50 page book on LLM Agents! 🙏
This is my new favourite survey paper
It reads like a perfect book on the why we need different techniques to make Large Language Model agents work and how different papers approached it
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The most detailed and practical write up on applying LLMs! 🙏
This reads like a survey paper but written for the industry and applications
@eugeneyan
is known as the best NLP writer for a reason. It’s the most comprehensive overview of patterns on building Large Language Models
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CS 25: Transformers United! 🦾
One of the best courses covering concepts of Transformers in the context of LLMs along with applications and secrets to building these models.
My favourite part is the guest lectures from the best of our field!
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After all the travelling, I’ve invested my remaining savings into more 3090 GPUs for Kaggle 🙏
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A masterpiece on applying LLM agents! 🙏
MetaGPT paper is a golden treat on effectively applying Large Language Model agents. It takes inspiration from how humans work. Here’s my summary:
- Assembly line: Every agent has a role assigned to it
- Software Engineering: The above
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Personal Update Thread on The
@GoogleAI
Residency:
Earlier this year I got a life-changing email. My Google AI Residency application had made it to the final interview rounds! This spring, Google flew me out to NYC where I gave my "On-site" interviews.
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"Machine Learning doesn’t have to be a black box anymore. What use is a good model if we cannot explain the results to others. Interpretability is as important as creating a model."
A neat kernel on "Intrepreting Machine Learning models" by
@pandeyparul
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I'm super excited to share that I've joined
@weights_biases
! 🍵
I've been a fan of their community since the early days, I'm really looking forward to contributing to it further.
Please expect study groups, events, Kaggle deep dives, and much more! 🙏
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The mindset of "Completing an online course" isn't right:
It's not a college degree-hacking your way to completion shouldn't be the goal and def won't be helpful
Take your time, even build a project midway: Gaining knowledge & building Projects/Solving Prob should be the goal!
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Incredible recap of key Transformer concepts! 🙏
What I really like about this write up is it covers 30 key papers and flows really well as a recap
@lilianweng
has written so many incredible posts, this one captures all key architectural concepts:
- Transformer basics:
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The most underrated LLM Cookbook! 🙏
@OpenAI
’s guide is an incredibly underrated resource
My favourite bit is the practical advice and guides sprinkled throughout the examples
It also has the highest quality code of many learning resources
The examples cover all important
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Great tutorial on Deploying Deep Learning Models On Web And Mobile (Along with a working demo!) by
@reshamas
and Nidhin P.
They've used the library, but the tutorial can be used to create a web and mobile app using any framework
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GitHub GPT: Understand any repository! 🚀
Here is a demo where I played with connecting GPT-4 to any repository.
The main logic is <20 lines of code:
✅
@LangChainAI
for the main logic
✅
@activeloopai
for storing embeddings
✅ Simple App that runs in the terminal
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I’m in happy tears to awarded “Top GenAI Scientist” award by
@AnalyticsVidhya
🙏
I feel really honoured by the recognition. Will make this one count!
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A truly open source assistant chabot: GPT4All-J 🙏
A new model based was shipped to the GPT4All family.
This one permits commercial usage and is completely open source:
✅ Model weights
✅ Training logs
✅ Training dataset
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The NLP study group is back after a break 🙏
Today, I’ll explain and summarise the BloombergGPT paper.
This was an incredible read since the authors have kindly shared a fair bit of model details along with the reasons for their architectural choices:
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Another great roadmap of LLM papers! 👌
CS224n has a really good curated list of papers to read for Large Language Models
I would recommend starting with the papers before the slides and lectures:
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I’m writing a guide on building Multi-GPU machines! 🙏
Over the past few years, I’ve spent a lot of time learning how to build ML servers
I’ve decided to write a guide on the topic
What questions/topics would you want covered?
TIA!
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A perfect intro to open source LLMs! 🙏
The course by
@asangani7
is now my top recommendation for getting started with Large Language Models:
- Just enough theory for a whole picture
- Teaches prompting, special tokens and conversational agents
- Perfectly abstracts the
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Efficient Deep Learning course! 👌
The lectures cover various techniques relevant to LLMs
Happy Sunday learning:
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The best NLP lectures! 🙏
@chrmanning
’s latest CS224n lectures are finally live!
The 14 hours of new content covers Large Language Models, Interpretability, and some crispy framework tutorials:
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My next goal:
I will spend at least 500 hours this year competing on
@kaggle
🍵
If I fail to do it, I will not drink chai for an entire year and giveaway all my GPUs 🙏
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The Interview with
@kaggle
Grandmaster and Senior CV Engineer
@LyftLevel5
: Vladimir Iglovikov
@viglovikov
just got published
@hackernoon
. The Grandmaster has really been kind enough to share *ALL* of his secrets, you can find all of them here:
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The definitive guide to Multimodal deep learning! 🙏
Since the GPT-4 demo, multimodal has become one of the coolest domains in our field.
This is a 240 page no-nonsense book to the domain, it starts from the basics of individual modalities upto the key details of the domain.
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Very practical course on applying LLMs! 🙏
@HamelHusain
had mentioned that langchain makes for a great cookbook of cutting edge ideas.
This course is a refreshingly applied one teaching how to use
@LangChainAI
to build different applications.
My favourite part is it’s
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The most comprehensive series I’ve read on Vector databases! 💾
Most of us got exposed to vector dbs via Langchain or llamaindex documentation
However, There’s a lot of nuance and options to select from when building Large Language Model apps
@tech_optimist
has written a 4
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If you’re interested in diving into ML research & understanding more papers this year:
The Deep Learning book is an incredible resource teaching the basics & math behind DL
I’ve created a 5 part series explaining chapters here:
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Terrific tutorial on fine-tuning LLMs to your own data 👨🔬
Tomas Bratnic has shared a really crispy write up on creating a Cypher generating LLM:
✅ All Open Source tools
✅ Walkthrough of setup
✅ Detailed steps on how to solve this
@h2oai
LLMStudio
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Masterclass of Pythonic Thinking: PyTudes 🤌
Its the highest quality resource for learning “the Pythonic way” and problem solving
The large number of problems cater to everyone at all levels
Every revisit, there’s something new to learn
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Today is a glorious day for
@kaggle
community! 🍵
Kaggle legend:
@sudalairajkumar
has conquered all categories and become the newest 4x Grandmaster! 🙏
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“A cookbook of Self-Supervised Learning”
@ylecun
et al👩🍳👨🍳
SSL is the tasty sauce behind a lot of the success in Language models, Computer Vision and beyond.
It permits working with limited data by allowing you to include unlabelled data in your workflow. Hence becoming “the
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NLP with Transformers Study Group 🤗
Starting next week, I’m hosting a study group on the absolute gem book by
@huggingface
team 🙏
@_lewtun
has kindly agreed to join the kickoff session. I can’t think of a better way to learn NLP:
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AutoAgents: Autonomously generate LLM agents for any goal! 🤖
This tries to solve the need for strong prompting and role definition by autogenerating agents
The code is sparsely documented but readable:
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Weekly
@kaggle
Top Solutions Study group 🙏
Starting this Sunday, I will be going through top solutions of recently ended competitions that might be relevant to the ongoing ones:
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Am I doing
@karpathy
and chill right?
Cafe overlooking Himalayas, tasty breakfast and lecture rewatch 😋🙏
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This is THE BEST CAREER ADVICE for Data Science that I’ve ever read:
IMO
@kaggle
forums often have write ups/advice of *much* higher quality than most of the blogposts out there
I’d highly recommend reading all of
@ryan_chesler
’s write ups on Kaggle.
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A hands on guide to train LLaMA with RLHF 🤗
It’s one of the most complete tutorials on the topic with detailed explanations around why and how to follow the fine-tuning approaches.
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This really helped me understand why LLMs work! 🙏
- Why next word prediction is powerful
- Why prompting works
- What we know about emergence
Thanks
@_jasonwei
for the gem:
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The next 30 days, everyday w/o exception, I will:
- Wake up at 4 AM
- Workout for 2 hr
- Kaggle for 3 hr
- Move onto other tasks after 10 AM
Extra rules:
- No 📱before 5 PM
- No Emails before 12 PM
I'll post a video announcing the micro-resolution tom
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🧵 Top Kaggle solutions always feature many great insights and hidden details:
I spent the past two days reading top solutions from the recently ended Great Barrier reef competition
There were many fascinating tricks shared, my short summary
TL;DR👇
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The first open source Financial LLM! 🚀
BloombergGPT was the first proprietary financial LLM. This week, we witness the first open source one.
FinLLM takes a “data centric” approach towards finance by building on top of multiple APIs/resources. Here’s an overview of its
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My 15 day LLM Study Vacation! 🚀
The plan for next 2 weeks:
✅ Hike/Visit a Himalayan mountain daily with a paper to read
✅ Build 15
@LangChainAI
apps
✅ Finish catching up on LLM research
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I just sat for 5 minutes straight smiling and trying not tear up after today's interview.
It's done.
I was able to record enough interviews to complete my dream goal of publishing 2 episodes-every Sunday and Thursday, at 9AM PT. No exceptions in 2020.
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An extremely crispy intro to Vector Databases! 🙏
Have you watched those wired videos explaining concepts at incremental levels of detail?
@helloiamleonie
done the same for Vector Dbs
She teaches the topic using Feynman technique, in 3 levels of detail
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The hardest challenge I’ve done! 🙏
Last week, I completed 200 days of writing everyday about Large Language Models
After ~2k hours of learning, I’m ready to make crispy videos:
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If you're looking for a code first NLP course 👨💻
There is an NLP course by
@fastdotai
covering 🕵️♂️
- What is NLP
- Topic Modelling
- Sentiment Classification
- Regex
- LM
- RNNs
- Transformers
- Bias & Ethics
Blog:
YT Playlist:
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I'm really excited to share the interview with my and
@fastdotai
family's greatest ML Hero:
@jeremyphoward
Not adding any Tweet introductions this time 🍵
Audio:
Show Notes:
Video:
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Strong LLM blog recommendation! 🙏
For any practitioner interested in Large Language Models, this is the best blog
@eugeneyan
is magical at combining industrial patterns, experiments & research ideas very clearly
His weekend exp are my fav read:
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This is my favourite type of tutorial! 🙏
Remember the awesome fastai tutorials that share only the necessary theory and quickly dive into applying it?
@Sentdex
teaches us QLoRA in the exact manner by applying it to give llama-2 more personality
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2022 Goals:
1 Workout for 350 hours
2 Compete on
@kaggle
for 200 Hours
3 Write 3 Kaggle Kernels
4 Host 50 meetups & interviews
@weights_biases
5 Spend 500 hours reading
6 Write 4 High-Quality
@PyTorch
blogposts
7 Publish 1 Open Source Repo
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150 interview with ML heroes 🙏
I’ve actively hosted interviews with the best Kagglers, Researchers and practitioners from 2019-22.
The questions discuss the guest’s journey and approach in a timeless and educational way:
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Huge congratulations to
@pandeyparul
on becoming the 1st Woman
@kaggle
Kernels Grandmaster from India.
And to the best of my knowledge, 2nd one in the world.
Although, I really hope she won't stop sharing her amazing kernels with us🍵
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Tutorial on how to efficiently read papers 🙏
For today’s NLP study group, I will show you how to parse papers by going through the LLaMa paper 🦙
We’ll also learn text generation with 🤗 transformers
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Official guide on the GPT-4 API 👨🍳
@OpenAI
cookbook has a set of crispy examples teaching how to build on the API.
While, it doesn’t have a “0 to 100” flow, it’s worth going through in an evening. The code is very readable.
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The biggest LLM release!🚨
@AnthropicAI
have given us the “dream” Large Language model with Claude 2:
- Trained till early 2023
- Context length upto 200k tokens
- Ability to upload documents
- Better code abilities
- Free beta for public
Please see my demo below of comparing
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20 really high quality channels to learn ML & Python from 📚👨💻
This is a list of my favourite creators that produce high signal technical education content:
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