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How I'd Learn AI in 2024 (if I could start over) 121gamers 0 Views • 1 year ago

Here's the roadmap that I would follow to learn artificial intelligence (AI).
📚 Get the FREE roadmap here ➡️ https://bit.ly/data-alchemy

⏱️ Timestamps
00:00 Introduction
00:34 Why learn AI?
01:28 Code vs. Low/No-code approach
02:27 Misunderstandings about AI
03:27 Ask yourself this question
04:19 What makes this approach different
05:42 Step 1: Set up your environment
06:54 Step 2: Learn Python and key libraries
08:02 Step 3: Learn Git and GitHub Basics
08:35 Step 4: Work on projects and portfolio
13:12 Step 5: Specialize and share knowledge
14:31 Step 6: Continue to learn and upskill
15:39 Step 7: Monetize your skills
16:53: What is Data Alchemy?

🛠️ Explore ProjectPro
https://bit.ly/3q837w8

👋🏻 About Me
Hey there! I'm Dave, an AI Engineer and the founder of Datalumina, where our mission is to facilitate entrepreneurial and technological proficiency in professionals and businesses. Through my videos here on this channel, my posts on LinkedIn, and courses on Skool, I share practical strategies and tools to navigate the complexities of data, artificial intelligence, and entrepreneurship.

🎓 My Courses
https://www.skool.com/data-alchemy
https://www.skool.com/data-freelancer

✔️ How I manage my business and dev projects
https://clickup.pxf.io/k0EN9N

📊 How I'm using data to track my health
https://join.whoop.com/datalumina

🔗 Let's Connect
https://www.linkedin.com/in/daveebbelaar/
https://www.instagram.com/daveebbelaar/

📥 Datalumina's Newsletter
https://www.datalumina.com/newsletter

#ai #roadmap #datalumina

📌 Video Description
In this video, Dave shares a comprehensive and actionable roadmap for anyone looking to start their journey into the exciting world of artificial intelligence (AI) in 2024. Whether you're a complete beginner or someone looking to pivot your career towards AI, this video lays out a step-by-step guide that demystifies the process of learning AI from the ground up. Dave highlights the significance of AI in today's tech landscape and addresses common misconceptions that newcomers might have.

With a focus on practical learning, the video emphasizes the importance of choosing between a code-centric or a low/no-code approach, making AI accessible to a broader audience. Dave's unique approach involves asking a critical question that shapes the learning path, ensuring that viewers embark on a journey tailored to their goals and interests.

The roadmap detailed in the video covers essential steps such as setting up your learning environment, mastering Python and key libraries crucial for AI, understanding the basics of Git and GitHub, and the importance of working on projects to build a strong portfolio. Dave also talks about the importance of specialization and the continuous process of learning and upskilling in fields like generative AI, large language models, chatbots, and machine learning.

Furthermore, Dave shares insights on how to monetize your AI skills, turning your passion into a profession. The video concludes with an introduction to Data Alchemy, a concept that encapsulates the transformative power of AI knowledge.

For those eager to dive into the AI world, Dave offers a free roadmap accessible through the link provided in the video description. This invaluable resource serves as a compass for navigating the complexities of AI learning, making it an essential watch for anyone interested in artificial intelligence, machine learning, and related technologies.

learning AI and ChatGPT isn’t that hard 121gamers 0 Views • 1 year ago

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Anyone can learn machine learning. You don’t need a fancy college degree nor do you need to be a math genius. No matter where you are in your journey, you can start learning machine learning and Artificial Intelligence. In this video, NetworkChuck will show you how you can get started with Machine Learning, an insanely lucrative path. So, don’t wait, start learning the technologies behind things like ChatGPT and OpenAI.

Learn more about Santiago (a REAL ML Engineer)
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ALTERNATE TITLES:
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- AI for Beginners: A Guide to Understanding Artificial Intelligence
- AI 101: Everything You Need to Know About Artificial Intelligence
- Demystifying AI: Understanding the Basics of Machine Learning
- AI Made Easy: A Beginner's Guide to Artificial Intelligence
- Unlocking the Power of AI: A Comprehensive Guide to Machine Learning
- i taught my computer to tell me I suck at video games
- i taught my computer to play video games
- i used AI to win League of Legends
- chatgpt thinks I suck at video games
- i used machine learning to suck less at video games
- i taught my computer LoL (machine learning)
- my computer predicted i suck at video games (machine learning)
- you need to learn Machine Learning RIGHT NOW!!
- machine learning is easy
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- learning AI isn’t that hard
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- AI: It's Easier Than You Realize
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- Learning AI: Start Small and Achieve Big Results
- AI: It's Easier than You Think - Get Started Today
- AI: Learn It and Conquer It
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SUPPORT NETWORKCHUCK
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#machinelearning #chatgpt #artificialintelligence

How to Build an LLM from Scratch | An Overview 121gamers 0 Views • 1 year ago

Want to learn more? I’m launching a 6-week live BootCamp for AI Builders.
👉 Learn more: https://maven.com/s/course/13437a45a7
Save 50% at checkout with the code FOUNDER50

This is the 6th video in a series on using large language models (LLMs) in practice. Here, I review key aspects of developing a foundation LLM based on the development of models such as GPT-3, Llama, Falcon, and beyond.

More Resources:
▶️ Series Playlist: https://www.youtube.com/playli....st?list=PLz-ep5RbHos Read more: https://towardsdatascience.com..../how-to-build-an-llm

[1] BloombergGPT: https://arxiv.org/pdf/2303.17564.pdf
[2] Llama 2: https://ai.meta.com/research/p....ublications/llama-2-
[3] LLM Energy Costs: https://www.statista.com/stati....stics/1384401/energy
[4] arXiv:2005.14165 [cs.CL]
[5] Falcon 180b Blog: https://huggingface.co/blog/falcon-180b
[6] arXiv:2101.00027 [cs.CL]
[7] Alpaca Repo: https://github.com/gururise/AlpacaDataCleaned
[8] arXiv:2303.18223 [cs.CL]
[9] arXiv:2112.11446 [cs.CL]
[10] arXiv:1508.07909 [cs.CL]
[11] SentencePience: https://github.com/google/sent....encepiece/tree/maste
[12] Tokenizers Doc: https://huggingface.co/docs/tokenizers/quicktour
[13] arXiv:1706.03762 [cs.CL]
[14] Andrej Karpathy Lecture: https://www.youtube.com/watch?v=kCc8FmEb1nY&t=5307s
[15] Hugging Face NLP Course: https://huggingface.co/learn/n....lp-course/chapter1/7
[16] arXiv:1810.04805 [cs.CL]
[17] arXiv:1910.13461 [cs.CL]
[18] arXiv:1603.05027 [cs.CV]
[19] arXiv:1607.06450 [stat.ML]
[20] arXiv:1803.02155 [cs.CL]
[21] arXiv:2203.15556 [cs.CL]
[22] Trained with Mixed Precision Nvidia: https://docs.nvidia.com/deeple....arning/performance/m
[23] DeepSpeed Doc: https://www.deepspeed.ai/training/
[24] https://paperswithcode.com/method/weight-decay
[25] https://towardsdatascience.com..../what-is-gradient-cl
[26] arXiv:2001.08361 [cs.LG]
[27] arXiv:1803.05457 [cs.AI]
[28] arXiv:1905.07830 [cs.CL]
[29] arXiv:2009.03300 [cs.CY]
[30] arXiv:2109.07958 [cs.CL]
[31] https://huggingface.co/blog/ev....aluating-mmlu-leader
[32] https://www.cs.toronto.edu/~hi....nton/absps/JMLRdropo

--
Homepage: https://shawhintalebi.com/
Book a call: https://calendly.com/shawhintalebi

Intro - 0:00
How much does it cost? - 1:30
4 Key Steps - 3:55
Step 1: Data Curation - 4:19
1.1: Data Sources - 5:31
1.2: Data Diversity - 7:45
1.3: Data Preparation - 9:06
Step 2: Model Architecture (Transformers) - 13:17
2.1: 3 Types of Transformers - 15:13
2.2: Other Design Choices - 18:27
2.3: How big do I make it? - 22:45
Step 3: Training at Scale - 24:20
3.1: Training Stability - 26:52
3.2: Hyperparameters - 28:06
Step 4: Evaluation - 29:14
4.1: Multiple-choice Tasks - 30:22
4.2: Open-ended Tasks - 32:59
What's next? - 34:31

Let's build GPT: from scratch, in code, spelled out. 121gamers 0 Views • 1 year ago

We build a Generatively Pretrained Transformer (GPT), following the paper "Attention is All You Need" and OpenAI's GPT-2 / GPT-3. We talk about connections to ChatGPT, which has taken the world by storm. We watch GitHub Copilot, itself a GPT, help us write a GPT (meta :D!) . I recommend people watch the earlier makemore videos to get comfortable with the autoregressive language modeling framework and basics of tensors and PyTorch nn, which we take for granted in this video.

Links:
- Google colab for the video: https://colab.research.google.....com/drive/1JMLa53HDu
- GitHub repo for the video: https://github.com/karpathy/ng-video-lecture
- Playlist of the whole Zero to Hero series so far: https://www.youtube.com/watch?v=VMj-3S1tku0&list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ
- nanoGPT repo: https://github.com/karpathy/nanoGPT
- my website: https://karpathy.ai
- my twitter: https://twitter.com/karpathy
- our Discord channel: https://discord.gg/3zy8kqD9Cp

Supplementary links:
- Attention is All You Need paper: https://arxiv.org/abs/1706.03762
- OpenAI GPT-3 paper: https://arxiv.org/abs/2005.14165
- OpenAI ChatGPT blog post: https://openai.com/blog/chatgpt/
- The GPU I'm training the model on is from Lambda GPU Cloud, I think the best and easiest way to spin up an on-demand GPU instance in the cloud that you can ssh to: https://lambdalabs.com . If you prefer to work in notebooks, I think the easiest path today is Google Colab.

Suggested exercises:
- EX1: The n-dimensional tensor mastery challenge: Combine the `Head` and `MultiHeadAttention` into one class that processes all the heads in parallel, treating the heads as another batch dimension (answer is in nanoGPT).
- EX2: Train the GPT on your own dataset of choice! What other data could be fun to blabber on about? (A fun advanced suggestion if you like: train a GPT to do addition of two numbers, i.e. a+b=c. You may find it helpful to predict the digits of c in reverse order, as the typical addition algorithm (that you're hoping it learns) would proceed right to left too. You may want to modify the data loader to simply serve random problems and skip the generation of train.bin, val.bin. You may want to mask out the loss at the input positions of a+b that just specify the problem using y=-1 in the targets (see CrossEntropyLoss ignore_index). Does your Transformer learn to add? Once you have this, swole doge project: build a calculator clone in GPT, for all of +-*/. Not an easy problem. You may need Chain of Thought traces.)
- EX3: Find a dataset that is very large, so large that you can't see a gap between train and val loss. Pretrain the transformer on this data, then initialize with that model and finetune it on tiny shakespeare with a smaller number of steps and lower learning rate. Can you obtain a lower validation loss by the use of pretraining?
- EX4: Read some transformer papers and implement one additional feature or change that people seem to use. Does it improve the performance of your GPT?

Chapters:
00:00:00 intro: ChatGPT, Transformers, nanoGPT, Shakespeare
baseline language modeling, code setup
00:07:52 reading and exploring the data
00:09:28 tokenization, train/val split
00:14:27 data loader: batches of chunks of data
00:22:11 simplest baseline: bigram language model, loss, generation
00:34:53 training the bigram model
00:38:00 port our code to a script
Building the "self-attention"
00:42:13 version 1: averaging past context with for loops, the weakest form of aggregation
00:47:11 the trick in self-attention: matrix multiply as weighted aggregation
00:51:54 version 2: using matrix multiply
00:54:42 version 3: adding softmax
00:58:26 minor code cleanup
01:00:18 positional encoding
01:02:00 THE CRUX OF THE VIDEO: version 4: self-attention
01:11:38 note 1: attention as communication
01:12:46 note 2: attention has no notion of space, operates over sets
01:13:40 note 3: there is no communication across batch dimension
01:14:14 note 4: encoder blocks vs. decoder blocks
01:15:39 note 5: attention vs. self-attention vs. cross-attention
01:16:56 note 6: "scaled" self-attention. why divide by sqrt(head_size)
Building the Transformer
01:19:11 inserting a single self-attention block to our network
01:21:59 multi-headed self-attention
01:24:25 feedforward layers of transformer block
01:26:48 residual connections
01:32:51 layernorm (and its relationship to our previous batchnorm)
01:37:49 scaling up the model! creating a few variables. adding dropout
Notes on Transformer
01:42:39 encoder vs. decoder vs. both (?) Transformers
01:46:22 super quick walkthrough of nanoGPT, batched multi-headed self-attention
01:48:53 back to ChatGPT, GPT-3, pretraining vs. finetuning, RLHF
01:54:32 conclusions

Corrections:
00:57:00 Oops "tokens from the _future_ cannot communicate", not "past". Sorry! :)
01:20:05 Oops I should be using the head_size for the normalization, not C