🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐂𝐨𝐮𝐫𝐬𝐞 - 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 𝐭𝐨 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝: https://www.edureka.co/openai-....chatgpt-training-cou
This 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥 is intended as a Crash Course on 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 for Beginners. 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 has been growing in popularity exponentially. But, 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 is still not known to many people. In this video, I aim to show you the different ways in which you can use 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 for yourself.

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AI robots, with Elon Musk, Boston Dynamics. To learn AI, visit: https://brilliant.org/digitalengine where you'll also find loads of fun courses on maths, science and computer science.

Sources:

Future of Life Institute AI discussion with Elon Musk:
https://www.youtube.com/watch?v=h0962biiZa4&t=1452s

AI Alignment study, OpenAI, Oxford and UC Berkeley:
https://twitter.com/RichardMCN....go/status/1603862969

Atlas gets a grip - Boston Dynamics:
https://youtu.be/-e1_QhJ1EhQ

OpenAI's ChatGPT:
https://openai.com/blog/chatgpt/

David Chalmers on Machine Learning Street Talk (great channel):
https://www.youtube.com/watch?v=T7aIxncLuWk

Volcanic lightning is making the world safer:
https://www.youtube.com/watch?v=Yc-2lfNNmWk

Ball lightning:
https://www.youtube.com/watch?v=_gOlQCI9Tgg

A message to artists about AI art, ChrissaBug:
https://www.youtube.com/watch?v=zx3ROK9nOYE

Study on the health cost of sitting down too much:
https://www.acpjournals.org/doi/10.7326/M17-0212

AI improves stroke recovery:
https://www.euronews.com/next/....2022/12/27/ai-techno

AI encodes clinical knowledge:
https://arxiv.org/abs/2212.13138?utm_source=substack&utm_medium=email

Elon Musks tweet on ChatGPT, with Altman’s response.
https://twitter.com/elonmusk/s....tatus/15991285770686

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This Logistic Regression video will help you understand how a Logistic Regression algorithm works in Machine Learning. You will learn what is Supervised Learning, what is a classification problem, and the maths behind Logistic Regression. In the end, you will see a demo on how to predict the number present in an image using Logistic Regression in Python.

Below topics are covered in this Logistic Regression Tutorial:
0:00 - 01:52 Introduction
01:52 - 05:35 What is Supervised Learning?
05:35 - 06:10 What is Classification?
06:10 - 15:49 What is Logistic Regression?
15:49 - 16:58 Comparing Linear and Logistic regression
16:58 - 18:40 Logistic regression applications
18:40 - 38:17 Use case - Predicting the number in an image


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What is Logistics Regression?
Logistic regression is a classification algorithm that is used to predict a binary outcome based on a set of independent variables. Logistic regression uses a logistic function to estimate the probability of a target variable belonging to a particular class or category. It assumes that the dependent variable is binary or dichotomous and there should be no or very little multicollinearity between the predictor variables. Logistic regression

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🔥Edureka*** Machine Learning Certification Training - https://www.edureka.co/machine....-learning-certificat ***

00:00 Introduction
01:10 Agenda
01:28 Why Mathematics in Machine learning
03:38 Linear Algebra
05:05 Linear ALgebra - Scalars
06:20 Linear Algebra - Vectors
11:27 Linear Algebra - Matrices
20:24 Linear Algebra - Vector as Matrix
26:11 Linear Algebra - Eigen Vectors
27:41 Linear Algebra - Applications
30:18 Multivariate Calculus

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This Edureka video on "Clustering Algorithms" will help you understand the various aspects of clustering using K Means in Python.

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- - - - - - - - - - - - - - - - -
About the Course

Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.

- - - - - - - - - - - - - -

Why Learn Data Science?

Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.

After the completion of the Data Science course, you should be able to:
1. Gain insight into the 'Roles' played by a Data Scientist
2. Analyse Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyse data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R

- - - - - - - - - - - - - -

Who should go for this course?

The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:

1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies.

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This Live session on 'Data Science For Non Programmers' will help you explore the basics of Data Science and Machine Learning and in the process also tell you how to use different Python Libraries that let you implement Machine Learning Deep Learning, Data Visualization for Data Science and other purposes Non Programmatically.

🔷About Speaker: Mr. Krishna Prasad P is
1. Director Consulting at CGI.
2. Mentor for W2RT cohort as part of Nasscom initiative.
3. Recipient of prestigious “CGI Builder Award” at CGI level & “Sirius Award” at India level.
4. Submitted various white papers on Data related topics in DCAL (IIMB & IISC).
5. Architected and implemented comprehensive Data platforms for leading Banking firms.
6. Excellent experience in managing programs globally across delivery locations involving multiple time-zones.
7. Driving Pre-sales as Solution Architect & Solution Lead involving large deals.
8. Overall 22+ Years of experience with a strong focus on leading high performing teams & managing delivery excellence.
9. An Alumnus of the Indian School of Business.

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About the Master's Program

This program follows a set structure with 6 core courses and 8 electives spread across 26 weeks. It makes you an expert in key technologies related to Data Science. At the end of each core course, you will be working on a real-time project to gain hands on expertise. By the end of the program you will be ready for seasoned Data Science job roles.

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Topics Covered in the curriculum:

Topics covered but not limited to will be : Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Spark SQL, Random Forest, Naïve Bayes, Time Series, Text Mining, Web Scraping, PySpark, Python Scripting, Neural Networks, Keras, TFlearn, SoftMax, Autoencoder, Restricted Boltzmann Machine, LOD Expressions, Tableau Desktop, Tableau Public, Data Visualization, Integration with R, Probability, Bayesian Inference, Regression Modelling etc.

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Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.

For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free).

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐂𝐨𝐮𝐫𝐬𝐞 - 𝐁𝐞𝐠𝐢𝐧𝐧𝐞𝐫𝐬 𝐭𝐨 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝: https://www.edureka.co/openai-....chatgpt-training-cou
This 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐓𝐮𝐭𝐨𝐫𝐢𝐚𝐥 is intended as a Crash Course on 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 for Beginners. 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 has been growing in popularity exponentially. But, 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 is still not known to many people. In this video, I aim to show you the different ways in which you can use 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 for yourself.



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🌕 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

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80s Music Hits 🍃 Olivia Newton-John, Prince, Lionel Richie, Cyndi Lauper, Madonna, Janet Jackson

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[00:03:51] - 02. S̲a̲̲cri̲̲fi̲̲ce̲̲ - E̲̲̲lto̲̲n J̲o̲̲hn
[00:08:31] - 03. G̲i̲̲rls J̲u̲̲st W̲a̲̲nt T̲o̲̲ H̲a̲̲ve̲̲ F̲u̲̲n - C̲yndi̲̲ L̲a̲̲u̲̲pe̲̲r
[00:12:57] - 04. B̲la̲̲ck O̲̲̲r W̲hi̲̲te̲̲ - M̲i̲̲cha̲̲e̲̲l J̲a̲̲ckso̲̲n
[00:16:48] - 05. P̲i̲̲a̲̲no̲̲ M̲a̲̲n
[00:19:24] - 06. G̲i̲̲rls J̲u̲̲st W̲a̲̲nt T̲o̲̲ H̲a̲̲ve̲̲ F̲u̲̲n - C̲yndi̲̲ L̲a̲̲u̲̲pe̲̲r

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Episode Summary: MIT professor Andrew W. Lo tackles AI-assisted financial advising, healthcare, and the effect of human behavior on financial markets.

Episode Description: In this the first of two pilot episodes of Chalk Radio with VIDEO, Professor Andrew Lo, who teaches finance at MIT’s Sloan School of Management, knows that many people find financial matters perplexing and scary. Lots of us don’t have a good head for numbers, and besides, how can one get advice and make sound decisions when it’s taboo to discuss one’s finances at all? That’s where a financial advisor is useful–someone who understands the concepts, can crunch the numbers, and has a fiduciary responsibility to look out for your best interests. For many people, hiring a financial advisor might be a financial impossibility, but Prof. Lo and his colleagues are working to develop an AI financial advisor that not only gives ordinary people access to sound financial advice, but acts with real fiduciary responsibility. Large language models can’t do this yet, he says, but the technology is developing fast. Other topics he touches on in this episode include the outsized influence of finance on drug development and global decarbonization and the equally outsized influence of teachers on their students–he names many who changed his own life, from his third-grade teacher in Queens to his professors at college and graduate school.

Subscribe to Chalk Radio on podcast platforms ➜ https://chalk-radio.simplecast.com

Relevant Resources:
MIT OpenCourseWare (https://ocw.mit.edu)
The OCW Educator portal (https://ocw.mit.edu/educator)
Professor Lo’s faculty page (https://mitsloan.mit.edu/facul....ty/directory/andrew-
15.401 Finance Theory I on MIT OpenCourseWare (https://ocw.mit.edu/courses/15....-401-finance-theory-
15.481x Adaptive Markets: Financial Market Dynamics and Human Behavior on MIT Open Learning Library (https://openlearninglibrary.mi....t.edu/courses/course
15.482x Healthcare Finance on MIT Open Learning Library (https://openlearninglibrary.mi....t.edu/courses/course
Music in this episode by Blue Dot Sessions (https://www.sessions.blue/)

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0:01 Intro
0:51 Why Finance Matters
3:02 Inflation, and practical finance applications to mitigate rising costs
5:19 Can ChatGPT reliably plan someone's retirement?
7:54 How to deal with AI hallucinations
9:14 Financial planning - why you need to start early!
9:58 Finances - a taboo topic?
11:07 AI Finance tools and ethics
12:31 Will AI take people's jobs?
13:18 Finance for positive impact on people & healthcare - Andrew's origin story
16:47 How Finance could help Climate
18:48 'It all comes down to money'
19:22 How human behavior affects Finance
19:47 How humans react to a market crash
21:12 Andrew's Adaptive Markets Hypothesis
22:02 How can we counteract irrational human tendencies?
24:00 How Andrew makes finance accessible through his teaching
24:55 Andrew's education and identifying different types of intelligence
28:31 Andrew's learning disorder and how teachers helped him manage it
35:08 Andrew's meaningful memento
38:17 Conclusion


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Credits:
Sarah Hansen, host and producer
Brett Paci, producer
Jackson Maher, producer
Dave Lishansky, producer
Peter Chipman, show notes

MIT 3.020 Thermodynamics of Materials, Spring 2021
Instructor: Rafael Jaramillo

View the complete course: https://ocw.mit.edu/courses/3-....020-thermodynamics-o
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

This first class session introduces entropy and spontaneous processes, molecular interactions, and enthalpy, as well as the scope and use of thermodynamics.

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In this tutorial we show how to do transfer learning and fine tuning in Pytorch!

People often ask what courses are great for getting into ML/DL and the two I started with is ML and DL specialization both by Andrew Ng. Below you'll find both affiliate and non-affiliate links if you want to check it out. The pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
ML Course (affiliate): https://bit.ly/3qq20Sx
DL Specialization (affiliate): https://bit.ly/30npNrw
ML Course (no affiliate): https://bit.ly/3t8JqA9
DL Specialization (no affiliate): https://bit.ly/3t8JqA9

GitHub Repository:
https://github.com/aladdinpers....son/Machine-Learning

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PyTorch Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf

In this video I show you how to to load different file formats (json, csv, tsv) in Pytorch Torchtext using Fields, TabularDataset, BucketIterator to do all the heavy preprocessing for NLP tasks, such as numericalizing, padding, building vocabulary, which saves us a lot of time to focus on actually training the models! In this example I show a toy example dataset for sentiment analysis but the things we go through are general and can be adapted for any dataset.

Resources I used to learn about torchtext:
https://torchtext.readthedocs.io/en/latest/
https://anie.me/On-Torchtext/
https://github.com/bentrevett
https://towardsdatascience.com..../how-to-use-torchtex
https://mlexplained.com/2018/0....2/08/a-comprehensive

People often ask what courses are great for getting into ML/DL and the two I started with is ML and DL specialization both by Andrew Ng. Below you'll find both affiliate and non-affiliate links if you want to check it out. The pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
ML Course (affiliate): https://bit.ly/3qq20Sx
DL Specialization (affiliate): https://bit.ly/30npNrw
ML Course (no affiliate): https://bit.ly/3t8JqA9
DL Specialization (no affiliate): https://bit.ly/3t8JqA9

GitHub Repository:
https://github.com/aladdinpers....son/Machine-Learning

✅ Equipment I use and recommend:
https://www.amazon.com/shop/aladdinpersson

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GitHub - https://github.com/aladdinpersson

PyTorch Playlist:
https://www.youtube.com/playli....st?list=PLhhyoLH6Ijf

This video explains the Transformer architecture in a very detailed way, including most math formulas in the paper, and the neural network operations behind it. The Transformer is the foundation of many powerful language models like BERT, GPT3, RoBERTa, XLNET, ELECTRA, T5. Understanding how it works in detail might help you modify, optimize, or improve it in the way you want.


Connect
Linkedin https://www.linkedin.com/in/xue-yong-fu-955723a6/
Twitter https://twitter.com/home
Email [email protected]

0:00 - Intro
1:10 - Architecture overview
1:56 - Encoder
3:58 - Residual connection & layer normalization
6:59 - Decoder
11:14 - Attention mechanism
14:30 - Scaled dot-product attention
20:22 - Learned projection layers
26:32 - Multi-head attention
28-39 - Encoder-decoder attention
31:18 - Encoder self-attention
31:34 - Decoder self-attention
33:58 - Position-wise feedforward network
36:41 - Word embedding
39:34 - Positional encoding
47:37 - Why self-attention

What Is GPT-3 Series
https://www.youtube.com/playli....st?list=PLoS8jSwcU-c

Paper: Attention Is All You Need
https://arxiv.org/abs/1706.03762

Abstract
The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best
performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English- to-German translation task, improving over the existing best results, including ensembles, by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.0 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

🔥 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐀𝐧𝐠𝐮𝐥𝐚𝐫 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎) : https://www.edureka.co/angular-training
This Edureka video on Top 30 Angular Interview Question and Answers will help you to prepare yourself for Angular Interviews. It covers questions for beginners, intermediate and experienced professionals. Following topics are covered in this Angular Interview Questions and Answers video:

Beginners level Angular Interview questions
Intermediate level Angular Interview questions
Advanced level Angular Interview questions
🔴 Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV

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🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh

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⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

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📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
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