This Complete Machine Learning Course video will help you understand and learn Machine Learning in detail. This Machine Learning Tutorial is ideal for both beginners as well as professionals who want to master Machine Learning concepts & practice.

All presentation files for the Machine Learning course as PDF for as low as ₹200 (INR): https://forms.gle/VeD1hi5e2n6Ced236

Github repository link for Colab Notebooks: https://github.com/siddhardhan....23/Complete-Machine-

All Datasets Link: https://drive.google.com/drive..../folders/1NEs0rpFelf

Timestamp for the topics covered in this Machine Learning Course video:

00:00 Introduction
4:22 AI vs ML vs DL
9:40 Types of Machine Learning
16:05 Supervised Learning & its Types
21:34 Unsupervised Learning & its Types
27:55 Deep Learning
36:11 Google Colaboratory - basics
45:53 Python Basics
1:08:36 Basic Data types in Python
1:28:40 List, Tuple, Set, Dictionary
1:55:36 Operators in Python
2:14:50 If Else Statement in Python
2:28:42 Loops in Python
2:44:20 Functions in Python
2:59:16 Numpy Tutorial
3:44:03 Pandas Tutorial
4:33:06 Matplotlib Tutorial
5:01:25 Seaborn Tutorial
5:37:34 Data Collection for ML
5:50:46 Importing datasets through Kaggle API
6:05:15 Handling Missing Values
6:29:58 Data Standardization
6:49:30 Label Encoding
7:06:56 Train Test Split
7:19:16 Handling Imbalanced Dataset
7:38:24 Feature Extraction of Text data
7:52:03 Numerical dataset processing
8:11:42 Textual data Processing
8:47:12 ML Use case 1: Rock vs Mine Prediction
9:35:35 ML Use case 2: Diabetes Prediction
10:38:53 ML Use case 3: Spam Mail Prediction


Machine Learning Projects Playlist: https://youtube.com/playlist?l....ist=PLfFghEzKVmjvuSA

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In this Python tutorial, We'll see how to create an AI Text Generation Solution with GPT-Neo from Eleuther AI.
We'll learn
1. About GPT-Neo
2. How to install the latest Hugging Face Transformers Package
3. Load Text Generation pipeline and Download Pre-trained GPT-Neo Models
4. Text Generation

Eleuther AI - https://www.eleuther.ai/
GPT-Neo on Hugging Face Model Hub - https://huggingface.co/EleutherAI/gpt-neo-1.3B
GPT-Neo - https://github.com/EleutherAI/gpt-neo
GPU Focused GPT-NeoX - https://github.com/EleutherAI/gpt-neox/
Colab Code - https://colab.research.google.....com/drive/1UByjdT5l_

14 additional cool applications just built on top of OpenAI's GPT-3 (generative predictive transformer) API (currently in private beta).

Links:
https://twitter.com/siddkaramc....heti/status/12861686
https://twitter.com/minimaxir/....status/1286100406305
https://twitter.com/mckaywrigl....ey/status/1285827683
https://twitter.com/plotlygrap....hs/status/1286079929
https://twitter.com/michaeltef....ula/status/128550589
https://twitter.com/itsyashdan....i/status/12856958503
https://www.youtube.com/watch?v=7Y5KsN6ehvk
https://twitter.com/amasad/sta....tus/1285789362647478
https://twitter.com/IntuitMach....ine/status/128705025
https://twitter.com/mattshumer...._/status/12871250155
https://twitter.com/bemmu/stat....us/12852841316564459
https://twitter.com/ChinyaSuha....il/status/1287110006
https://twitter.com/JanelleCSh....ane/status/128632703
https://twitter.com/gulan_28/s....tatus/12862512197830

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in this video I go over how to generate snippets of python code using open AI's gpt-3 playground. It's surprisingly good at generating programs that you describe with natural language. This is perfect for rapid prototyping and testing new features. I've linked the playground below so you can try generating python for yourself!

https://beta.openai.com/playground

Song: Wiguez & EH!DE - The Path (Ft. Agassi) [NCS Release]
Music provided by NoCopyrightSounds
Free Download/Stream: http://NCS.io/ThePath
Watch: http://youtu.be/

In this quick tutorial we will download and install the Open AI GPT-2 Model and then generate a text based on some input. Basically we will use the Open AI model to build a basic AI Text generator. You can fine tune this for certain tasks like AI Generated Code, Music, Text and so on but in this example we will be generating text based on some input. Also keep in mind this is not some text from the training set or from a website, the AI generates it's own unique response.


In order to get Open AI GPT-2 up and running you need to download the model from the Github repository, install the requirements, download the models and run the sample. You can also fine tune the GPT-2 NLP for other categories in order to make it much more accurate.


If you want me to create an app around this or create a video where I am fine-tuning the model leave a comment down below. If enough people are interested I will create video about that!



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Disclaimer:
All videos are for educational purposes and use them wisely. Any video might have inaccurate or outdated information. I give my best to research every topic thoroughly but please be aware that videos can contain mistakes.

This video explores the GPT-2 paper "Language Models are Unsupervised Multitask Learners". The paper has this title because their experiments show how massive language models trained on massive datasets can perform tasks like Question Answering and Translation by carefully formatting them as language modeling inputs.

Paper Links
GPT-2 Paper: https://cdn.openai.com/better-....language-models/lang
AllenNLP GPT-2 Demo: https://demo.allennlp.org/next....-token-lm?text=Joel%
The Illustrated GPT-2: http://jalammar.github.io/illustrated-gpt2/
Combining GPT2 and BERT to make a fake person: https://www.bonkerfield.org/20....20/02/combining-gpt-

Thanks for watching! Please Subscribe!

고려대학교 산업경영공학과 일반대학원
Unstructured Data Analysis
08-4: GPT
Generative Pre-Trained Word Vectors, Transformer Decoder

https://github.com/pilsung-kang/text-analytics

How do you represent a word in AI? Rob Miles reveals how words can be formed from multi-dimensional vectors - with some unexpected results.

08:06 - Yes, it's a rubber egg :)

Unicorn AI:
EXTRA BITS: https://youtu.be/usthqKtw2LA
AI YouTube Comments: https://youtu.be/XyMdpcAPnZc

More from Rob Miles: http://bit.ly/Rob_Miles_YouTube

Thanks to Nottingham Hackspace for providing the filming location: http://bit.ly/notthack

https://www.facebook.com/computerphile
https://twitter.com/computer_phile

This video was filmed and edited by Sean Riley.

Computer Science at the University of Nottingham: https://bit.ly/nottscomputer

Computerphile is a sister project to Brady Haran's Numberphile. More at http://www.bradyharan.com

#ai #technology #switchtransformer

Scale is the next frontier for AI. Google Brain uses sparsity and hard routing to massively increase a model's parameters, while keeping the FLOPs per forward pass constant. The Switch Transformer compares favorably to its dense counterparts in terms of speed and sample efficiency and breaks the next magic number: One Trillion Parameters.

OUTLINE:
0:00 - Intro & Overview
4:30 - Performance Gains from Scale
8:30 - Switch Transformer Architecture
17:00 - Model-, Data- and Expert-Parallelism
25:30 - Experimental Results
29:00 - Stabilizing Training
32:20 - Distillation into Dense Models
33:30 - Final Comments

Paper: https://arxiv.org/abs/2101.03961
Codebase T5: https://github.com/google-rese....arch/text-to-text-tr

Abstract:
In deep learning, models typically reuse the same parameters for all inputs. Mixture of Experts (MoE) defies this and instead selects different parameters for each incoming example. The result is a sparsely-activated model -- with outrageous numbers of parameters -- but a constant computational cost. However, despite several notable successes of MoE, widespread adoption has been hindered by complexity, communication costs and training instability -- we address these with the Switch Transformer. We simplify the MoE routing algorithm and design intuitive improved models with reduced communication and computational costs. Our proposed training techniques help wrangle the instabilities and we show large sparse models may be trained, for the first time, with lower precision (bfloat16) formats. We design models based off T5-Base and T5-Large to obtain up to 7x increases in pre-training speed with the same computational resources. These improvements extend into multilingual settings where we measure gains over the mT5-Base version across all 101 languages. Finally, we advance the current scale of language models by pre-training up to trillion parameter models on the "Colossal Clean Crawled Corpus" and achieve a 4x speedup over the T5-XXL model.

Authors: William Fedus, Barret Zoph, Noam Shazeer

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Meta AI’s recently shared Open Pretrained Transformer (OPT-175B), a language model with 175 billion parameters trained on publicly available data sets.

For the first time for a language technology system of this size, the release includes both the pretrained models and the code needed to train and use them.

This video contains three parts:

00:00 Quick Intro about OPT-175B
03:51 OPT-175B Live Demo with Alpa
08:07 OPT1.3B Text Generation Hands-on Coding using Hugging Face Transformers

Colab OPT1.3B Text Generation Tutorial - https://colab.research.google.....com/drive/1ZGfFzL9au

OPT-175B Blog post from Meta AI - https://ai.facebook.com/blog/d....emocratizing-access-

OPT-175B Live Demo - https://opt.alpa.ai/

It's the Github Copilot that you have at home ;) GPyT is a GPT style model that is trained from publicly accessible code on Github. You can get the model from here: https://huggingface.co/Sentdex/GPyT

Code samples used in the video: https://pythonprogramming.net/....GPT-python-code-tran

Neural Networks from Scratch book: https://nnfs.io
Channel membership: https://www.youtube.com/channe....l/UCfzlCWGWYyIQ0aLC5
Discord: https://discord.gg/sentdex
Reddit: https://www.reddit.com/r/sentdex/
Support the content: https://pythonprogramming.net/support-donate/
Twitter: https://twitter.com/sentdex
Instagram: https://instagram.com/sentdex
Facebook: https://www.facebook.com/pythonprogramming.net/
Twitch: https://www.twitch.tv/sentdex

Fine-tuning larger models can be tricky on consumer hardware. In this video I go over why its better to use large models for fine-tuning vs smaller models, I go over the issue with the naive approach to fine-tuning, and finally, I go over how to use DeepSpeed to successfully fine-tune even the largest GPT Neo model.

Notebook Git repo: https://github.com/mallorbc/GP....T_Neo_fine-tuning_no
finetuning repo: https://github.com/Xirider/finetune-gpt2xl
DeepSpeed repo: https://github.com/microsoft/DeepSpeed
happy transformers: https://happytransformer.com/
GPT article with images: https://towardsdatascience.com..../gpt-3-the-new-might

Timestamps

00:00 - Intro
00:36 - Background on fine-tuning
02:41 - Setting up Jupyter
05:17 - Incorrect naive fine-tuning method
10:02 - Correctly fine-tuning with DeepSpeed
15:35 - Fine-tuning the 2.7B model
16:35 - Fine-tuning the 1.3B model
17:55 - Looking at the README
20:08 - Outro and future work

Lex Fridman Podcast full episode: https://www.youtube.com/watch?v=rIpUf-Vy2JA
Please support this podcast by checking out our sponsors:
- Coinbase: https://coinbase.com/lex to get $5 in free Bitcoin
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GUEST BIO:
Joscha Bach is a cognitive scientist, AI researcher, and philosopher.

PODCAST INFO:
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GPT is the first of the papers which proved the effectiveness of unsupervised pre-training for language processing tasks. This video is about GPT-1 which became quite an impactful work in the series of GPT papers that we now have (GPT-2 and GPT-3).


Paper: https://www.cs.ubc.ca/~amuham0....1/LING530/papers/rad
code: https://github.com/openai/finetune-transformer-l
Official OpenAI blog: https://openai.com/blog/language-unsupervised/


Paper Abstract:
Natural language understanding comprises a wide range of diverse tasks suchas textual entailment, question answering, semantic similarity assessment, anddocument classification. Although large unlabeled text corpora are abundant,labeled data for learning these specific tasks is scarce, making it challenging fordiscriminatively trained models to perform adequately. We demonstrate that largegains on these tasks can be realized bygenerative pre-trainingof a language modelon a diverse corpus of unlabeled text, followed bydiscriminative fine-tuningon eachspecific task. In contrast to previous approaches, we make use of task-aware inputtransformations during fine-tuning to achieve effective transfer while requiringminimal changes to the model architecture. We demonstrate the effectiveness ofour approach on a wide range of benchmarks for natural language understanding.Our general task-agnostic model outperforms discriminatively trained models thatuse architectures specifically crafted for each task, significantly improving upon thestate of the art in 9 out of the 12 tasks studied. For instance, we achieve absoluteimprovements of 8.9% on commonsense reasoning (Stories Cloze Test), 5.7% onquestion answering (RACE), and 1.5% on textual entailment (MultiNLI).


AI Bites
YouTube: https://www.youtube.com/c/AIBites
Twitter: https://twitter.com/ai_bites
Patron: https://www.patreon.com/ai_bites
github: https://github.com/ai-bites

OUTLINE:
0:00 - Intro
0:30 - Google RL creates next-gen TPUs
2:15 - Facebook launches NetHack challenge
3:50 - OpenAI mitigates bias by fine-tuning
9:05 - Google AI releases browseable reconstruction of human cortex
9:50 - GPT-J 6B Transformer in JAX
12:00 - Tensorflow launches Forum
13:50 - Text style transfer from a single word
15:45 - ALiEn artificial life simulator

My Video on Chip Placement: https://youtu.be/PDRtyrVskMU

References:
RL creates next-gen TPUs
https://www.nature.com/articles/s41586-021-03544-w
https://www.youtube.com/watch?v=PDRtyrVskMU
Facebook launches NetHack challenge
https://ai.facebook.com/blog/l....aunching-the-nethack
Mitigating bias by fine-tuning
https://openai.com/blog/improv....ing-language-model-b
Human Cortex 3D Reconstruction
https://ai.googleblog.com/2021..../06/a-browsable-peta
GPT-J: An open-source 6B transformer
https://arankomatsuzaki.wordpr....ess.com/2021/06/04/g
https://6b.eleuther.ai/
https://github.com/kingoflolz/....mesh-transformer-jax
Tensorflow launches "Forum"
https://discuss.tensorflow.org/
Text style transfer from single word
https://ai.facebook.com/blog/a....i-can-now-emulate-te
ALiEn Life Simulator
https://github.com/chrxh/alien

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