🚀 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?
✅ How SLMs work – Train them on your data for specific tasks.
✅ Real-world examples – AI for customer support, healthcare, and education.
✅ Why SLMs are the future – Privacy-friendly, low-energy, and cost-effective!

🌎 Why This Matters:
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.

💡 SLMs Are Changing AI Forever!
• Instant AI-powered customer support 🏢
• Medical assistance in remote areas 🏥
• Personalized education tools for students 📚
• Energy-efficient AI for sustainability 🌱

n8n Tutorial for Beginners 2026: How to Build AI Agents
✅ self-host n8n and save tons of money with Hostinger: http://hostinger.com/youri10

In this video, I show you how to build your own AI agent in n8n from scratch, self-host it on Hostinger for unlimited executions, and automate tasks that normally waste hours every week. You’ll learn how to set up a VPS with one-click n8n installation, connect OpenAI models and memory, add real-world tools like Google Search, and turn the output into clean newsletters automatically sent to your inbox.

for inquiries: youri (at) mediaflow.group

All rights owned by Paramount Pictures
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In this video we take a professional, 8K cinema camera and use it as a webcam. This is definitely one way to improve your video calls. With the help of an $80 adapter from Atomos, you can connect nearly any type of camera to your computer.

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Enroll today at https://www.coursera.org/speci....alizations/deep-lear to get access to the specialization!


About this Specialization:

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.

In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.

You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.

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.

この動画では、ニューラルネットワークの歴史を追いながらDeep Learningの基礎についてやさしく解説します。

前回の動画（今Deep Learningに取り組むべき理由）はこちらです。
https://www.youtube.com/watch?v=-Dl8s4iufxI

再生リスト「Deep Learning入門」
https://www.youtube.com/playli....st?list=PLg1wtJlhfh2

Neural Network Console
https://dl.sony.com/ja/

Neural Network Libraries
https://nnabla.org/ja/

Self-attention and the transformer architecture have broken many benchmarks and enabled widespread progress in NLP. However, at this point neither researchers nor companies in industry (with a few exceptions) have leveraged them in a time series context. This talk will explore both the barriers and promise of self-attention models and transfer learning in a time series context. The talk will also look into why time series tasks (forecasting/prediction) have not had their BERT/Imagenet moment and what can be done to enable transfer learning on temporal data. The extremely limited COVID-19 time series forecasting dataset will be used as an example for the need to address these limited data scenarios and enable effective few-shot/transfer learning more generally.

Isaac Godfried is a machine learning engineer at Monster focusing on the data and machine learning platform. Prior to his current position Isaac worked on machine learning problems in both retail and healthcare. He also has participated in many Kaggle competitions. Isaac’s main focus is to remove barriers related to the use of deep learning in industry. Specifically, this involves researching techniques like transfer and meta learning for data constrained scenarios, designing tools to effectively track and manage experiments, and creating frameworks to deploy models at scale. In his spare time Isaac also conducts research in AI for good causes like medicine and climate.

👩🏼‍🚀Weights and Biases:
We’re always free for academics and open source projects. Email [email protected] with any questions or feature suggestions.

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This Edureka "Simple DevOps Projects for Beginners - Part 1" video will help you in developing 3 simple projects. Here you will be working on the concepts of Continuous Integration, Continuous Delivery & Deployment. Two technologies in particular that the session surrounds itself around are Jenkins and Docker.

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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

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📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

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This video on "What is Deep Learning" provides a fun and simple introduction to its concepts. We learn about where Deep Learning is implemented and move on to how it is different from machine learning and artificial intelligence. We will also look at what neural networks are and how they are trained to recognize digits written by hand. We further look at some popular applications of Deep Learning. So, let’s dive into the world of Deep Learning with this video.

Don't forget to take the quiz at 04:26!

To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/S....implilearn?sub_confi

Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip

#DeepLearning #WhatIsDeepLearning #DeepLearningTutorial #DeepLearningCourse #DeepLearningExplained #Simplilearn

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Simplilearn’s Deep Learning course will transform you into an expert in Deep Learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our Deep Learning course, you'll master Deep Learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as Deep Learning scientist.

Why Deep Learning?

It is one of the most popular software platforms used for Deep Learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in Deep Learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in Deep Learning models, learn to operate TensorFlow to manage neural networks and interpret the results. According to payscale.com, the median salary for engineers with Deep Learning skills tops $120,000 per year.

You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement Deep Learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build Deep Learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve Deep Learning models
7. Build your own Deep Learning project
8. Differentiate between machine learning, Deep Learning and artificial intelligence

There is booming demand for skilled Deep Learning engineers across a wide range of industries, making this Deep Learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this Deep Learning online course particularly for the following professionals:

1. Software engineers
2. Data scientists
3. Data analysts
4. Statisticians with an interest in Deep Learning

Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=DeepLearningScribein5min&utm_medium=Description&utm_source=youtube

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This Cyber Security full course helps you learn cybersecurity in 12 Hours. In this cybersecurity tutorial for beginners, you will learn the importance of cybersecurity, cybersecurity skills, what cybersecurity means, and the types of cyberattacks with a hands-on demo for each. This video also covers cryptography, ethical hacking, and the top cybersecurity certifications.

Below are the topics covered in this Cyber Security full course video:
Introduction to Cybersecurity
Top Cyber Security Skills
Types of Cyberattacks
What is Cyber Security
Ethical Hacking
Cryptography
CompTIA Security+Cerificatios
CEH Certification
CISSP Certification
Cyber Security Interview Questions and answers

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#CyberSecurity #CyberSecurityFullCourse #CyberSecurityCourse #LearnCyberSecurity #CyberSecurityTrainingForBeginners #CyberSecurityExplained #CyberSecurityTutorial #Simplilearn

What Is Cyber Security?
Cyber Security involves techniques that help secure various digital components, networks, data, and computer systems, from unauthorized digital access. There are several ways to implement cybersecurity, depending on the kind of network you are connected to and the type of cyberattacks you are prone to. A few ways to implement cybersecurity are defining clear boundaries, using network security control devices like Firewalls and IDS, and carrying out security testing.

About Simplilearn Cyber Security Master's Program:
The Cyber Security Expert Master’s Program will equip you with the skills needed to become an expert in this rapidly growing domain. With this best-in-class program, you will learn comprehensive approaches to protecting your infrastructure, including securing data and information, running risk analysis and mitigation, architecting cloud-based security, achieving compliance, and much more.

Simplilearn Cyber Security Expert Master’s Program provides cybersecurity professionals with foundational, intermediate, and advanced security skills through industry-leading certification courses, including CompTIA Security+, CEH, CISM, CISSP, and CCSP. The program begins with introductory-level cybersecurity skills training, then progresses to advanced cybersecurity technologies such as reverse engineering, penetration testing techniques, and many more. This training program will enable you to:

Implement technical strategies, tools, and techniques to secure data and information for your organization
1. Adhere to ethical security behavior for risk analysis and mitigation
2. Understand security in cloud computing architecture in depth
3. Comprehend legal requirements, privacy issues, and audit process methodologies within the cloud environment
4. Focus on IT compliance and the integrity of enterprise systems to establish a more secure enterprise IT framework

At the end of this Master’s Program, you will be equipped with the following skillsets:
1. Install, configure and deploy public key infrastructure and network components while assessing and troubleshooting issues to support organizational security
2. Master advanced hacking concepts to manage information security efficiently
3. Design security architecture and framework for a secure IT operation
4. Frame cloud data storage architectures and security strategies, and utilize them to analyze risks
5. Protect data movement, perform disaster recovery, access CSP security, and manage client databases

Learn more at: https://www.simplilearn.com/cyber-security-expert-master-program-training-course?utm_campaign=ReUpLoad25Sep22&utm_medium=Description&utm_source=youtube

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In this talk, we will cover the basics of Reinforcement Learning from Human Feedback (RLHF) and how this technology is being used to enable state-of-the-art ML tools like ChatGPT. Most of the talk will be an overview of the interconnected ML models and cover the basics of Natural Language Processing and RL that one needs to understand how RLHF is used on large language models. It will conclude with open question in RLHF.

RLHF Blogpost: https://huggingface.co/blog/rlhf
The Deep RL Course: https://hf.co/deep-rl-course
Slides from this talk: https://docs.google.com/presen....tation/d/1eI9PqRJTCF
Nathan Twitter: https://twitter.com/natolambert
Thomas Twitter: https://twitter.com/thomassimonini

Nathan Lambert is a Research Scientist at HuggingFace. He received his PhD from the University of California, Berkeley working at the intersection of machine learning and robotics. He was advised by Professor Kristofer Pister in the Berkeley Autonomous Microsystems Lab and Roberto Calandra at Meta AI Research. He was lucky to intern at Facebook AI and DeepMind during his Ph.D. Nathan was was awarded the UC Berkeley EECS Demetri Angelakos Memorial Achievement Award for Altruism for his efforts to better community norms.