Learning

Machine Learning Full Course For Beginners | Machine Learning Tutorial | Machine Learning Course

Generative AI
3,373,989 Views · 4 years ago

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Machine Learning Full Course for Beginners | Machine Learning Tutorial | Machine Learning Course
Hello and welcome to the Machine Learning Full Course for Beginners using python. In this video, you will learn from basics to advanced machine learning concepts from Great Learning’s top faculties, including professor Mukesh Rao, Bharani Akella & many other leading industry experts. If you are an enthusiast who wants to start with machine learning from scratch, this machine learning beginner video is the best to start with.
#machinelearningfullcourse #machinelearning #machinelearningbasics

Agenda:
• Python for Machine Learning
• Role of Statistics in Machine Learning
• Introduction to Machine Learning and its types
• How does a Machine learning model learn?
• Supervised and Unsupervised learning algorithms
• Principal component analysis for dimensionality reduction
• Application of Machine Learning

Topics Covered:
00:01:09 – What Is Machine learning? (Introduction to Machine Learning)
00:03:00 – Why Machine Learning?
00:04:22 – Road Map to Machine Learning
00:01:09 – How to Use Kaggle (www.kaggle.com)
Machine Learning with Python (Python Libraries for Machine Learning)
00:11:25 - NumPy Python Tutorial (How to Create NumPy Array)
00:14:58 - How to Initialize NumPy Array
00:22:19 - How to check the shape of NumPy arrays
00:24:42 - How to Join NumPy Arrays
00:28:15 - NumPy Intersection & Difference
00:31:50 - NumPy Array Mathematics
00:39:15 - NumPy Matrix
00:42:28 - How to Transpose NumPy Matrix
00:43:21 - NumPy Matrix Multiplication
00:45:45 - NumPy Save & Load
00:47:44 - Python Pandas Tutorial
00:48:09 - Pandas Series Object
00:58:44 - Pandas Dataframe
01:12:00 - Matplotlib Python Tutorial
01:12:12 - Line plot
01:26:32 - Bar plot
01:32:37 - Scatter Plot
01:40:35 - Histogram
01:46:16 - Box Plot
01:51:03 - Violin Plot
01:51:57 - Pie Chart
01:56:39 - DoughNut Chart
01:59:04 - SeaBorn Line Plot
02:07:27 - SeaBorn Bar Plot
02:15:15 - SeaBorn ScatterPlot
02:20:25 - SeaBorn Histogram/Distplot
02:26:52 - SeaBorn JointPlot
02:30:23 - SeaBorn BoxPlot
02:38:59 – Role of Mathematics in Data Science
02:40:23 – What is data?
02:42:34 – What is Information?
02:43:21 – What is Statistics?
02:43:58 – What is Population?
02:46:48 – What is Sample?
02:47:33 – What are Parameters?
02:47:55 – Measures of Central Tendency
02:51:10 – Understanding Empirical Rule
02:53:16 – What is Mean, median, and mode?
02:57:04 – Measures of Spread (Understanding Range, Inter Quartile Range & Box-plot)
03:12:56 – Types of Machine Learning (Supervised, Unsupervised & Reinforcement Learning)
03:27:43 – How does a Machine Learning Model Learn?
03:35:31 – Supervised Machine Learning (Mukesh Rao)
04:34:51 – Python for Machine Learning
04:46:40 – Linear Regression Algorithm (Hands-on)
05:21:13 – What is Logistic Regression
05:29:39 – Linear Regression vs Logistic Regression
05:40:15 – Naïve Bayes Algorithm
05:49:32 – Diabetes Prediction using Naïve Bayes
06:15:18 – Decision Tree and Random Forest Algorithm
07:55:01 – Introduction to Support Vector Machines (SVMs)
08:07:08 – Kernel Functions
08:11:56 – Advantages & Disadvantages of SVMs
08:31:37 – K-NN Algorithm (K-Nearest Neighbour Algorithm)
08:40:13 – Introduction to Unsupervised Learning - Clustering
08:48:35 – Introduction to Principal Component Analysis
09:09:39 – PCA for Dimensionality Reduction
09:15:27 – Introduction to Hierarchical Clustering
09:28:38 – Types of Hierarchical Clustering
09:34:02 – How does Agglomerative hierarchical clustering work
09:42:32 – Euclidean Distance
09:45:10 – Manhattan Distance
09:48:01 – Minkowski Distance
09:50:02 – Jaccard Similarity Coefficient/Jaccard Index
09:54:02 – Cosine Similarity
09:58:18 – How to find an optimal number for clustering
10:03:02 – Applications Machine Learning

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Learning Machine Learning has never been easier #shorts #machinelearning #statistics #datascience

Generative AI
2,108,181 Views · 4 years ago

Introduction to Statistical Learning: https://statlearning.com

Roadmap to Become a Data Scientist / Machine Learning Engineer in 2022: https://youtu.be/lHKlCqx94OY

Roadmap to Become a Data Analyst in 2022: https://youtu.be/024KMhTKlVs

Roadmap to Become a Data Engineer in 2022: https://youtu.be/ztGB-__Yg7w

Here's my favourite resources:

Best Courses for Analytics:
---------------------------------------------------------------------------------------------------------
+ IBM Data Science (Python): https://bit.ly/3Rn00ZA
+ Google Analytics (R): https://bit.ly/3cPikLQ
+ SQL Basics: https://bit.ly/3Bd9nFu

Best Courses for Programming:
---------------------------------------------------------------------------------------------------------
+ Data Science in R: https://bit.ly/3RhvfFp
+ Python for Everybody: https://bit.ly/3ARQ1Ei
+ Data Structures & Algorithms: https://bit.ly/3CYR6wR

Best Courses for Machine Learning:
---------------------------------------------------------------------------------------------------------
+ Math Prerequisites: https://bit.ly/3ASUtTi
+ Machine Learning: https://bit.ly/3d1QATT
+ Deep Learning: https://bit.ly/3KPfint
+ ML Ops: https://bit.ly/3AWRrxE

Best Courses for Statistics:
---------------------------------------------------------------------------------------------------------
+ Introduction to Statistics: https://bit.ly/3QkEgvM
+ Statistics with Python: https://bit.ly/3BfwejF
+ Statistics with R: https://bit.ly/3QkicBJ

Best Courses for Big Data:
---------------------------------------------------------------------------------------------------------
+ Google Cloud Data Engineering: https://bit.ly/3RjHJw6
+ AWS Data Science: https://bit.ly/3TKnoBS
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More Courses:
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+ Excel: https://bit.ly/3RBxind

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+ Natural Language Processing: https://bit.ly/3edXAgW

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+ Object Oriented Programming (Java): https://bit.ly/3Bfjn0K

+ TensorFlow Advanced Techniques: https://bit.ly/3BePQV2
+ TensorFlow Data and Deployment: https://bit.ly/3BbC5Xb
+ Generative Adversarial Networks / GANs (PyTorch): https://bit.ly/3RHQiRj

Become a Member of the Channel! https://bit.ly/3oOMrVH
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#machinelearning #datascience #statistics #datascience

Complete Machine Learning Course in 60 Hours - Part 1 | Full Machine Learning Course with Python

Generative AI
2,377,014 Views · 4 years ago

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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AI Text Generation with GPT-3 OpenSource Alternative GPT-Neo Model using Hugging Face Hub

Generative AI
2,821,736 Views · 4 years ago

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 (More) Cool Apps Built Using GPT-3 Part II NEW

Generative AI
2,499,527 Views · 4 years ago

Convertir facilement un disque MBR en GPT | Installation de Windows

Generative AI
2,217,972 Views · 4 years ago

▬▬ Liens Utiles ▬▬

► Me suivre sur FB : https://www.facebook.com/TutoInfoFR/

Merci d'avoir regardé ce Tutoriel !
----------------------------------------------------
© Tutoriels Informatiques FR - 2020

Generating Python Code with GPT 3 #shorts

Generative AI
2,190,626 Views · 4 years ago

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/

Open AI GPT-2 Tutorial | AI Text Generator

Generative AI
2,548,293 Views · 4 years ago

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.

GPT2 Explained!

Generative AI
2,383,146 Views · 4 years ago

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!

08-4: GPT

Generative AI
2,114,635 Views · 4 years ago

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

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

Vectoring Words (Word Embeddings) - Computerphile

Generative AI
2,725,820 Views · 4 years ago

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

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Generative AI
2,828,316 Views · 4 years ago

#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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GPT-3 Alternative - OPT-175B Hugging Face Language Model Tutorial

Generative AI
2,465,781 Views · 4 years ago

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/

GPyT - Generative Python Transformer Model released (the off-brand Github Copilot)

Generative AI
2,825,949 Views · 4 years ago

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
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Fine-tuning GPT Neo 2.7B and 1.3B

Generative AI
2,629,423 Views · 4 years ago

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

GPT-4 is listening to us now | Joscha Bach and Lex Fridman

Generative AI
2,527,871 Views · 4 years ago

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

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