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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Learn how to earn a free C# certification! freeCodeCamp has teamed up with Microsoft up to bring you a new free professional certification: the Foundational C# Certification. The course is a text based course, not a video course. This professional certification includes 35 hours of text-based training and interactive coding challenges all from Microsoft, and also an online certification exam by freeCodeCamp. In this video, freeCodeCamp team member @GavinLon will preview the course, along with telling you everything you need to do to take this free course.

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01:47:32 - What are Gen AI Agents
02:24:34 - Agentic AI
03:21:06 - Machine Learning Tutorial
05:26:29 - Reinforcement Learning
05:42:34 - Deepseek r1
05:53:47 - Install Deepseek
06:44:30 - Hugging Face and its tutorial
06:52:47 - Search GPT
06:55:02 - Langchain
07:51:37 - Generative Adversarial Tutorial
09:21:57 - Introduction to agentic workflow
09:31:19 - What are Gans
09:32:20 - Transformers in AI
09:44:49 - LSTM
10:04:40 - CHatgpt analyse
10:15:36 - Openai sora
10:24:29 - LLM Benchmarkeing
10:39:53 - Open ai chatgpt o1 model
10:48:16 - Google Quantam AI
10:53:49 - Majorana
10:58:58 - Gen ai tools for job interview
12:59:19 - Deep learning Interview questions
13:05:13 - Claude 3 sonnet

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⏩ Check out the Artificial Intelligence training videos: https://youtube.com/playlist?l....ist=PLEiEAq2VkUULa5a

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Reinforcement Learning with Human Feedback (RLHF) is a method used for training Large Language Models (LLMs). In the heart of RLHF lies a very powerful reinforcement learning method called Proximal Policy Optimization. Learn about it in this simple video!

This is the first one in a series of 3 videos dedicated to the reinforcement learning methods used for training LLMs.

Full Playlist: https://www.youtube.com/playli....st?list=PLs8w1Cdi-zv

Video 0 (Optional): Introduction to deep reinforcement learning https://www.youtube.com/watch?v=SgC6AZss478
Video 1 (This one): Proximal Policy Optimization
Video 2: Reinforcement Learning with Human Feedback https://www.youtube.com/watch?v=Z_JUqJBpVOk
Video 3 (Coming soon!): Deterministic Policy Optimization

00:00 Introduction
01:25 Gridworld
03:10 States and Action
04:01 Values
07:30 Policy
09:39 Neural Networks
16:14 Training the value neural network (Gain)
22:50 Training the policy neural network (Surrogate Objective Function)
33:38 Clipping the surrogate objective function
36:49 Summary

Get the Grokking Machine Learning book!
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Reinforcement Learning with Human Feedback (RLHF) is a method used for training Large Language Models (LLMs). In the heart of RLHF lies a very powerful reinforcement learning method called Proximal Policy Optimization. Learn about it in this simple video!

This is the first one in a series of 3 videos dedicated to the reinforcement learning methods used for training LLMs.

Full Playlist: https://www.youtube.com/playli....st?list=PLs8w1Cdi-zv

Video 0 (Optional): Introduction to deep reinforcement learning https://www.youtube.com/watch?v=SgC6AZss478
Video 1: Proximal Policy Optimization https://www.youtube.com/watch?v=TjHH_--7l8g
Video 2 (This one): Reinforcement Learning with Human Feedback
Video 3 (Coming soon!): Deterministic Policy Optimization

00:00 Introduction
00:48 Intro to Reinforcement Learning (RL)
02:47 Intro to Proximal Policy Optimization (PPO)
4:17 Intro to Large Language Models (LLMs)
6:50 Reinforcement Learning with Human Feedback (RLHF)
13:08 Interpretation of the Neural Networks
14:36 Conclusion

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Discount code (40%): serranoyt
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🚀 Small Language Models (SLMs): The Future of AI? 🤖

Most people think AI—like ChatGPT—needs huge servers and an internet connection. While that’s true for Large Language Models (LLMs), AI doesn’t have to be massive!

Introducing Small Language Models (SLMs)—AI that’s lightweight, efficient, and runs entirely offline. 📱💡

🔍 What You’ll Learn in This Video:
✅ SLMs vs. LLMs – What’s the difference?
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Unlike LLMs that require cloud computing, SLMs can run directly on your phone, laptop, or even a Raspberry Pi! No internet needed, no privacy concerns, and perfect for businesses, remote areas, and industries with strict data security requirements.

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• Instant AI-powered customer support 🏢
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• Energy-efficient AI for sustainability 🌱

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If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.

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AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.

We will help you master Deep Learning, understand how to apply it, and build a career in AI.

#deeplearning#learningmonkey

In this class we discuss multilayered Perceptron in neural network.
The discussion about single Perceptron is done in our previous class.
In multilayered network first one is considered as input layer.
Next layer we take neurons and this layer we call it first hidden layer.
Input is send to each neuron in the first hidden layer .
We randomly select weights and the input is multiplied with weights and the summation value is taken as output to the neuron.
These outputs are taken as input to second hidden layer.
Here in each neuron we apply activation function. Whats the need of activation function we understand in next classes.
The last layer we call it as output layer.
The output got from last layer is considered as predicted output and loss function applied on the output.


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How is a beginner supposed to get started learning machine learning? I'm going to describe a free 3 month curriculum to help you go from beginner to well-versed in modern machine learning. Transformers, Diffusers, Large Language Models, and dozens of new ML Ops tools have been released the past few years! The field of ML has changed so much and there are so many new tools to learn. Using the help of AI services, we can learn a lot faster than usual. This learning plan is something I'd create for myself if I were to get started today, but I'm going to open source it for you guys. This curriculum will cover all the math concepts, machine learning, deep learning, ML Ops, and tons of cool projects to get you up to speed with the field as fast as possible. If anyone asks how to best get started with machine learning, direct them to this video!

Curriculum from this video:
https://github.com/llSourcell/LearnML

Subscribe! And like. And comment. That's what keeps me going.

Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
Instagram: https://www.instagram.com/sirajraval
Facebook: https://www.facebook.com/sirajology

Curriculum Links
-----------------------------------------------------
Month 1: Machine Learning

Week 1: Python Fundamentals - https://allendowney.github.io/DSIRP/index.html

Week 2: Math of Machine Learning -
http://www.xaktly.com/XMathMain.html

Week 3: Data Analysis -
https://www.kaggle.com/learn

Week 4: Machine Learning Techniques -
https://github.com/ipython-books/cookbook-2nd

Month 2: Deep Learning

Week 1: Neural Networks
http://d2l.ai/

Week 2: Transformers
https://huggingface.co/course/chapter1/1

Week 3: Diffusers
https://www.fast.ai/posts/part2-2022-preview.html

Week 4: Deep Reinforcement Learning
https://simoninithomas.github.io/deep-rl-course/

Month 3: Machine Learning Operations

Week 1: Design -
https://madewithml.com/

Week 2: Development -
https://fullstackdeeplearning.com/course/2022/

Week 3: Production -
https://github.com/DataTalksClub/mlops-zoomcamp

Week 4: Data Engineering -
https://github.com/DataTalksCl....ub/data-engineering-

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📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 2023 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
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📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/



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🔥 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

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

🔵 DevOps Online Training: http://bit.ly/3VkBRUT
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🌕 Professional Certificate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

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

🌕 Artificial and Machine Learning PGD with E&ICT Academy
NIT Warangal: http://bit.ly/3OuZ3xs

📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 2023 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
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📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
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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.

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

Watch the original Lecture here: https://www.youtube.com/watch?v=UzxYlbK2c7E

Lecture by Professor Andrew Ng for Machine Learning (CS 229) in the Stanford Computer Science department. Professor Ng provides an overview of the course in this introductory meeting.

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include supervised learning, unsupervised learning, learning theory, reinforcement learning and adaptive control. Recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing are also discussed.

Complete Playlist for the Course:
http://www.youtube.com/view_play_list...

CS 229 Course Website:
http://www.stanford.edu/class/cs229/

Stanford University:
http://www.stanford.edu/

Stanford University Channel on YouTube:
http://www.youtube.com/stanford

❤️ Check out Anyscale and try it for free here: https://www.anyscale.com/papers

📝 The paper "#GPT-4 Technical Report" is available here:
https://cdn.openai.com/papers/gpt-4.pdf

More here:
https://openai.com/product/gpt-4

App development: https://twitter.com/zaid/statu....s/163736539552613171
The most beautiful sentence: https://reddit.com/r/ChatGPT/c....omments/121t9b4/the_
Doggy: https://twitter.com/peakcooper..../status/163971682268
Safe code: https://twitter.com/moyix/stat....us/16425874103692656
Finds security holes: https://twitter.com/feross/sta....tus/1641548124366987
App design: https://www.usegalileo.ai

📝 Paper with caustics:
https://users.cg.tuwien.ac.at/....zsolnai/gfx/adaptive

My latest paper on simulations that look almost like reality is available for free here:
https://rdcu.be/cWPfD

Or this is the orig. Nature Physics link with clickable citations:
https://www.nature.com/articles/s41567-022-01788-5

🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
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🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚'𝐬 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 𝐏𝐨𝐰𝐞𝐫 𝐁𝐈 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞 (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎): https://www.edureka.co/power-b....i-certification-trai
In this Edureka video on “Power BI Interview Questions”, you will get insights on some of the latest Power BI Interview Questions for both Beginners and Experienced. Power BI has continued to gain popularity due to its simplicity, data connectivity, and rich visualization, making it a compelling choice for a wide range of applications and industries. What do you think about the Power BI ? Leave us a comment and let us know what you think of this video. We want to hear your thoughts. Below are the topics covered in this Power BI Interview Questions video:
00:00 Intro
00:34 Agenda
00:55 Generic Power BI Interview Questions
07:25 DAX in Power BI Interview Questions
10:30 Power Pivot Interview Questions
12:24 Power Query Interview Questions

🔵 Check our complete Power BI Tutorial for Beginners playlist: https://goo.gl/Ju8JLG

Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV

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🔵 DevOps Engineer Masters Program: http://bit.ly/3Oud9PC
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🔵 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

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

🔵 Advanced Certificate Program in Cloud Computing with E&ICT Academy, IIT Guwahati: https://bit.ly/43vmME8

📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 2023 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩ NEW Top 10 Technologies To Learn In 2023 - • Top 10 Technologies To Learn In 2023 ...


How does it work?


1. This is a 4 Week Instructor led Online Course, 20 hours of assignment and 20 hours of project work.
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to work on a project, based on which we will provide you a Grade and a Verifiable Certificate!

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

About the Power BI Course


Power BI is a powerful cloud-based suite of business analytics tools that make it easy to combine data from multiple sources, analyze and visualize information and share insights.

Edureka's Power BI course features easy drag and drop report authoring for advanced insights , and online web dashboards that update automatically and can be shared within seconds.

You begin by learning about architecture and components of Power BI along with Power Query and Power Pivot. You will learn about Data Visualization with Power BI Desktop, Power View and Power Map. Along with the process of visualization, You will learn about Power BI Q&A and how the users can discover, explore and visualize their own enterprise. You will be introduced to the Power BI Administration for deployment of files and building dashboards in the Power BI Website.

After the completion of Edureka's Power BI Certification Course, you will be able to:

Discuss Power BI, Power Pivot, Power view, Power Map
Discuss Power Query
Describe Power BI Desktop
Describe DAX
Explain Data Visualization
State Power BI Q&A and Data insights
Develop Custom Visualizations
Discuss Power BI Embedded
Explain Power BI with SSRS Reports
Discuss the concepts around Power BI Administration

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

Who should go for this Power BI Course?

The Power BI training at edureka is for all the professionals who are passionate about business intelligence, data visualization, and data analytics.

For example:
Business Analysts
Business Intelligence Manager
Statisticians and Analysts
Data Scientists
Project Managers
- - - - - - - - - - - - - -


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

In this comprehensive exploration of the field of deep learning with Professor Simon Prince who has just authored an entire text book on Deep Learning, we investigate the technical underpinnings that contribute to the field's unexpected success and confront the enduring conundrums that still perplex AI researchers.

Understanding Deep Learning - Prof. SIMON PRINCE [STAFF FAVOURITE]

Watch behind the scenes, get early access and join private Discord by supporting us on Patreon:
https://patreon.com/mlst
https://discord.gg/aNPkGUQtc5
https://twitter.com/MLStreetTalk

Key points discussed include the surprising efficiency of deep learning models, where high-dimensional loss functions are optimized in ways which defy traditional statistical expectations. Professor Prince provides an exposition on the choice of activation functions, architecture design considerations, and overparameterization. We scrutinize the generalization capabilities of neural networks, addressing the seeming paradox of well-performing overparameterized models. Professor Prince challenges popular misconceptions, shedding light on the manifold hypothesis and the role of data geometry in informing the training process. Professor Prince speaks about how layers within neural networks collaborate, recursively reconfiguring instance representations that contribute to both the stability of learning and the emergence of hierarchical feature representations. In addition to the primary discussion on technical elements and learning dynamics, the conversation briefly diverts to audit the implications of AI advancements with ethical concerns.

Pod version (with no music or sound effects): https://podcasters.spotify.com..../pod/show/machinelea

Follow Prof. Prince:
https://twitter.com/SimonPrinceAI
https://www.linkedin.com/in/si....mon-prince-615bb9165

Get the book now!
https://mitpress.mit.edu/97802....62048644/understandi
https://udlbook.github.io/udlbook/

Panel: Dr. Tim Scarfe -
https://www.linkedin.com/in/ecsquizor/
https://twitter.com/ecsquendor

TOC:
[00:00:00] Introduction
[00:11:03] General Book Discussion
[00:15:30] The Neural Metaphor
[00:17:56] Back to Book Discussion
[00:18:33] Emergence and the Mind
[00:29:10] Computation in Transformers
[00:31:12] Studio Interview with Prof. Simon Prince
[00:31:46] Why Deep Neural Networks Work: Spline Theory
[00:40:29] Overparameterization in Deep Learning
[00:43:42] Inductive Priors and the Manifold Hypothesis
[00:49:31] Universal Function Approximation and Deep Networks
[00:59:25] Training vs Inference: Model Bias
[01:03:43] Model Generalization Challenges
[01:11:47] Purple Segment: Unknown Topic
[01:12:45] Visualizations in Deep Learning
[01:18:03] Deep Learning Theories Overview
[01:24:29] Tricks in Neural Networks
[01:30:37] Critiques of ChatGPT
[01:42:45] Ethical Considerations in AI

References:

#61: Prof. YANN LECUN: Interpolation, Extrapolation and Linearisation (w/ Dr. Randall Balestriero)
https://youtube.com/watch?v=86ib0sfdFtw

Scaling down Deep Learning [Sam Greydanus]
https://arxiv.org/abs/2011.14439

"Broken Code" a book about Facebook's internal engineering and algorithmic governance [Jeff Horwitz]
https://www.penguinrandomhouse.....com/books/712678/br

Literature on neural tangent kernels as a lens into the training dynamics of neural networks.
https://en.wikipedia.org/wiki/....Neural_tangent_kerne

Zhang, C. et al. "Understanding deep learning requires rethinking generalization." ICLR, 2017.
https://arxiv.org/abs/1611.03530

Computer Vision: Models, Learning, and Inference, by Simon J.D. Prince
https://www.amazon.co.uk/Compu....ter-Vision-Models-Le

Deep Learning Book, by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
https://www.deeplearningbook.org/

Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network
https://arxiv.org/abs/2210.00881

Computer Vision: Algorithms and Applications, 2nd ed. [Szeliski]
https://szeliski.org/Book/

A Spline Theory of Deep Networks [Randall Balestriero]
https://proceedings.mlr.press/....v80/balestriero18b/b

DEEP NEURAL NETWORKS AS GAUSSIAN PROCESSES [Jaehoon Lee]
https://arxiv.org/abs/1711.00165

Do Transformer Modifications Transfer Across Implementations and Applications [Narang]
https://arxiv.org/abs/2102.11972

ConvNets Match Vision Transformers at Scale [Smith]
https://arxiv.org/abs/2310.16764

Dr Travis LaCroix (Wrote Ethics chapter with Simon)
https://travislacroix.github.io/

CIFAR DLRL 2020 Summer School