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In this video, we see two math problems that require the exact same logic to solve. However, one is very easy and one is very hard. Why?
For information on my book "Grokking Machine Learning":
https://www.manning.com/books/....grokking-machine-lea
Discount code (40%): serranoyt
CORRECTION: at 13:41, the probability is 6.1e-5 and not 4.8e-4 (however, the entropy is 1.75, which is correct). Thank you @dlyChimi!
Learn Shannon entropy and information gain by playing a game consisting in picking colored balls from buckets.
Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
40% discount code: serranoyt
Accompanying blog post: https://medium.com/p/5810d35d54b4/
0:00 Shannon Entropy and Information Gain
2:22 What ball will we pick?
4:33 Quiz
5:06 Question
5:14 Game
7:17 Probability of Winning
7:45 Products
11:00 What if there are more classes?
12:34 Sequence 2
13:44 Sequence 3
14:57 Naive Approach
15:34 Sequence 1
19:44 General Formula
For a code implementation, check out these repos:
https://github.com/luisguiserr....ano/manning/tree/mas
https://github.com/luisguiserr....ano/manning/tree/mas
Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
40% discount code: serranoyt
An introduction to logistic regression and the perceptron algorithm that requires very little math (no calculus or linear algebra), only a visual mind.
0:00 Introduction
0:08 Series of 3 videos
0:41 E-mail spam classifier
7:19 Classification goal: split data
11:36 How to move a line
12:21 Rotating and translating
18:47 Perceptron Trick
23:20 Correctly and incorrectly classified points
24:20 Positive and negative regions
27:18 Perceptron Error
29:40 Gradient Descent
34:36 A friendly introduction to deep learning and neural networks
37:48 Activation function (sigmoid)
38:31 Log-Loss Error
41:37 Perceptron Algorithm
42:45 Logistic regression algorithm
44:48 Thank you!
For a code implementation, check out this repo:
https://github.com/luisguiserr....ano/manning/tree/mas
Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
40% discount code: serranoyt
An introduction to support vector machines (SVMs) that requires very little math (no calculus or linear algebra), only a visual mind.
This is the third of a series of three videos.
- Linear Regression: https://www.youtube.com/watch?v=wYPUhge9w5c
- Logistic Regression: https://www.youtube.com/watch?v=jbluHIgBmBo
0:00 Introduction
1:42 Classification goal: split data
3:14 Perceptron algorithm
6:00 Split data - separate lines
7:05 How to separate lines?
12:01 Expanding rate
18:19 Perceptron Error
19:26 SVM Classification Error
20:34 Margin Error
25:13 Challenge - Gradient Descent
27:25 Which line is better?
28:24 The C parameter
30:16 Series of 3 videos
30:30 Thank you!
Check out Kimi here! https://www.kimi.com/?utm_campaign=TR_grOjaW6m&utm_content=&utm_medium=Youtube&utm_source=CH_kEMBez3l&utm_term=
Timestamps ⏰
00:00 AI agents course!
01:13 Basics of AI Agents - What is an AI Agent?
02:58 What tasks are agents good for?
05:47 Spectrum of Autonomy
06:45 Context Engineering
07:13 Task Decomposition
07:59 Demo: No-Code Agent System
09:22 Intermediate AI Agents - Measuring Performance
10:39 Memory
11:42 Guardrails
12:50 Reflection
13:56 Tool Use
15:51 Designing Good Tools
16:56 Planning
19:05 Multi-Agent Collaboration
20:38 Multi-Agent System Design - Roles
21:25 Multi-Agent System Design - Communication Patterns
23:09 Multi-Agent System Design - Communication Pitfalls
23:44 Multi-Agent System Design - Best Practices
25:02 Demo: Multi-Agent System in Python
26:14 Advanced AI Agents - Advanced Task Decomposition
29:02 Improving Performance
30:26 Reducing Latency
31:16 Reducing Cost
32:39 Observability and Monitoring
35:10 Security
37:17 Bonus Resource!
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Agentic System Design:
https://gerred.github.io/build....ing-an-agentic-syste
The slides and examples were adapted from a few different courses, including:
https://www.deeplearning.ai/co....urses/design-develop
https://learn.deeplearning.ai/....courses/agentic-ai/i
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Want to become an AI Engineer? Download my AI Engineering Skills Checklist here: https://www.gratitudedriven.co....m/c/ai-engineering-c
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☕ If you'd like to support my work, you can buy me a coffee (thank you!): https://ko-fi.com/marinawyss
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🎥 Other videos you might like:
AI Engineering in 76 Minutes (Complete Course/Speedrun!)
https://www.youtube.com/watch?v=JV3pL1_mn2M&t=2s
AI Engineering: A *Realistic* Roadmap for Beginners
https://www.youtube.com/watch?v=dbUIjFXIpis
4 *Real* Machine Learning Projects That Get You Hired - No More Tutorials!
https://www.youtube.com/watch?v=MFSFcPsMsuE
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🦫 About me
I am a Senior Applied Scientist (basically, a blend of Data Scientist/Machine Learning Engineer) at Twitch/Amazon. Outside of my full-time job I'm a 1:1 career coach for people looking to break into the field, with a focus on those from non-traditional backgrounds.
I’m also a Certified Personal Trainer, always busy with too many interests, and really, deeply happy with my life. I hope to be able to help others achieve these things, too.
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✉️ Contact
Instagram: https://www.instagram.com/marina.wyss/
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TikTok: https://www.tiktok.com/@gratitudedriven
Leave me a comment here on YouTube!
Business email: [email protected]
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⚖️ Disclaimer
The views and opinions expressed in this video are my own and do not reflect the official policy or position of Twitch/Amazon or any other company I have worked for. All advice and insights shared here are based on my personal experiences and should be considered as such.
Thank you to Kimi for sponsoring this video!
This description may contain affiliate links. If you make a purchase I may make a small commission at no cost to you.
#AI #AIEngineering #AIAgents
Try Notion for free → https://ntn.so/techwithtim
👉 Check out PyCharm, the only Python IDE you need. Built for web, data, and AI/ML professionals. Download now. Free forever, plus one month of Pro included: https://jb.gg/check_out_pycharm_ide
Today I'm going to show you how to build an AI agent in Python in less than ten minutes.
Want to make real money with coding? I share high-signal insights on careers, monetization, and leverage in my free newsletter. Join here and get my guide How to Make Money With Coding instantly: https://techwithtim.net/newsletter
🎞 Video Resources 🎞
UV Video: https://www.youtube.com/watch?v=6pttmsBSi8M
Code in this video: https://github.com/techwithtim..../PythonAIAgentin10Mi
⏳ Timestamps ⏳
00:00 | Install & Setup
01:23 | OpenAI API Key
03:26 | Imports
04:42 | Tools
06:39 | LLM & Agent
07:43 | Driver Code
09:45 | Testing
Hashtags
#Python #AIAgents #Notion
A look at what Agentic AI is and then how to create an agentic AI agent using Copilot Studio then Semantic Kernel.
🔎 Looking for content on a particular topic? Search the channel. If I have something it will be there!
🤔 Due to the channel growth and number of people wanting help I no longer can answer or even read questions and they will just stay in the moderation queue never to be seen so please post questions to other sites like Reddit, Microsoft Community Hub etc.
▬▬▬▬▬▬ C H A P T E R S ⏰ ▬▬▬▬▬▬
00:00 - Introduction
00:26 - Types of AI agent
05:27 - Agentic AI
09:35 - Self-improving?
11:26 - Agentic agents ARE AI agents
11:40 - Many expert agents
13:58 - Quality testing
14:56 - Creating Agentic agents
15:15 - Low code with Copilot Studio
17:43 - Using generative orchestration
20:19 - Adding triggers
22:55 - Pro code with Semantic Kernel
24:48 - Types of semantic kernel agent
26:28 - Multi-agent
28:53 - Multi-agent example code
32:25 - Viewing multi-agent interaction
34:18 - Governance
35:59 - Summary
▬▬▬▬▬▬ K E Y L I N K S 🔗 ▬▬▬▬▬▬
► Video whiteboard:
🔗 https://github.com/johnthebrit..../RandomStuff/raw/mas
► Semantic kernel agent sample repo:
🔗 https://github.com/microsoft/s....emantic-kernel/tree/
▬▬▬▬▬▬ Want to learn more? 🚀 ▬▬▬▬▬▬
📖 Recommended Learning Path for Azure
🔗 https://learn.onboardtoazure.com
🥇 Certification Content Repository
🔗 https://github.com/johnthebrit..../CertificationMateri
📅 Weekly Azure Update
🔗 https://youtube.com/playlist?l....ist=PLlVtbbG169nEv7j
☁ Azure Master Class
🔗 https://youtube.com/playlist?l....ist=PLlVtbbG169nGccb
⚙ DevOps Master Class
🔗 https://youtube.com/playlist?l....ist=PLlVtbbG169nFr8R
💻 PowerShell Master Class
🔗 https://youtube.com/playlist?l....ist=PLlVtbbG169nFq_h
🎓 Certification Cram Videos
🔗 https://youtube.com/playlist?l....ist=PLlVtbbG169nHz2q
🧠 Mentoring Content
🔗 https://youtube.com/playlist?l....ist=PLlVtbbG169nGHxN
❔ Questions? Maybe I answered it in my FAQ
🔗 https://savilltech.com/faq
👕 Cure Childhood Cancer Charity T-Shirt Channel Store
🔗 https://johns-t-shirts-store.creator-spring.com/
👂 Enable the subtitles and from there you can translate to your native language via the auto-translate feature in settings! https://youtu.be/v5b53-PgEmI for a demo of using this feature.
SUBSCRIBE ✅ https://www.youtube.com/channe....l/UCpIn7ox7j7bH_OFj7
#aiagents #AI #agenticai #copilotstudio #johnsavillstechnicaltraining #onboardtoazure
MIT 14.02 Principles of Macroeconomics, Spring 2023
Instructor: Ricardo J. Caballero
View the complete course: https://ocw.mit.edu/courses/14....-02-principles-of-ma
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6
This lecture covers an introduction to the topic of macroeconomics, which is the study of the behavior of an economy as a whole. The instructor also talks about the objectives of the course.
License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
Support OCW at http://ow.ly/a1If50zVRlQ
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.
Lecture 2: Science and Research
Instructor: John Gabrieli
View the complete course: http://ocw.mit.edu/9-00SCS11
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
MIT 14.02 Principles of Macroeconomics, Spring 2023
Instructor: Ricardo J. Caballero
View the complete course: https://ocw.mit.edu/courses/14....-02-principles-of-ma
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6
In this lecture, Prof. Caballero discusses basic macroeconomic concepts such as aggregate output, the unemployment rate, and the inflation rate.
License: Creative Commons BY-NC-SA
More information at https://ocw.mit.edu/terms
More courses at https://ocw.mit.edu
Support OCW at http://ow.ly/a1If50zVRlQ
We encourage constructive comments and discussion on OCW’s YouTube and other social media channels. Personal attacks, hate speech, trolling, and inappropriate comments are not allowed and may be removed. More details at https://ocw.mit.edu/comments.
MIT 15.S21 Nuts and Bolts of Business Plans, IAP 2014
View the complete course: http://ocw.mit.edu/15-S21IAP14
Instructor: Bob Jones
This session will discuss these issues and provide guidance on how to approach the marketing section of your business plan.
License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai
October 14, 2025
This lecture covers adversarial robustness and generative models.
To learn more about enrolling in this course, visit: https://online.stanford.edu/co....urses/cs230-deep-lea
To follow along with the course schedule and syllabus, visit: https://cs230.stanford.edu/syllabus/
More lectures will be published regularly.
View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r
Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department
Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing
Trevor Hastie, Professor of Statistics and Biomedical Data Sciences at Stanford University - https://statistics.stanford.ed....u/people/trevor-j-ha
Robert Tibshirani, Professor of Statistics and Biomedical Data Sciences at Stanford University - https://statistics.stanford.ed....u/people/robert-tibs
Jonathan Taylor, Professor Statistics at Stanford University - https://statistics.stanford.ed....u/people/jonathan-ta
You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion. You can choose to take the course in R (https://www.edx.org/course/statistica) or in Python (https://www.edx.org/learn/data....-analysis-statistics
For more information about courses on Statistics, you can browse our Stanford Online Catalog: https://stanford.io/3QHRi72
Lets Make a Question Answering chatbot using the bleeding edge in deep learning (Dynamic Memory Network). We'll go over different chatbot methodologies, then dive into how memory networks work, with accompanying code in Keras.
Code + Challenge for this video:
https://github.com/llSourcell/....How_to_make_a_chatbo
Nemanja's Winning Code:
https://github.com/Nemzy/langu....age-translation/blob
Vishal's Runner up code:
https://github.com/erilyth/Dee....pLearning-Challenges
Web app to run the code yourself:
https://ethancaballero.pythonanywhere.com
Please subscribe! And like. And comment. That's what keeps me going.
More Learning resources:
https://www.youtube.com/watch?v=FCtpHt6JEI8&t=643s
https://www.youtube.com/watch?v=Qf0BqEk5n3o&t=637s
https://yerevann.github.io/201....6/02/05/implementing
https://www.youtube.com/watch?v=2A5DKPA5lAw
http://www.wildml.com/2016/01/....attention-and-memory
https://github.com/domluna/memn2n
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An overview of Deep Learning, including representation learning, families of neural networks and their applications, a first look inside a deep neural network, and many code examples and concepts from TensorFlow. This talk is part of a ML speaker series we recorded at home. You can find all the links from this video below. I hope this was helpful, and I'm looking forward to seeing you when we can get back to doing events in person. Thanks everyone!
Chapters:
0:00 - Intro and outline
1:42 - TensorFlow.js demos + discussion
3:58 - AI vs ML vs DL
7:55 - What’s representation learning?
8:40 - A cartoon neural network (more on this later)
9:20 - What features does a network see?
10:47 - The “deep” in “deep learning”
12:48 - Why tree-based models are still important
13:38 - How your workflow changes with DL
14:02 - A couple illustrative code examples
17:59 - What’s a hyperparameter?
19:44 - The skills that are important in ML
20:48 - An example of applied work in healthcare
21:58 - Families of neural networks + applications
28:55 - Encoder-decoders + more on representation learning
32:45 - Families of neural networks continued
35:50 - Are neural networks opaque?
38:29 - Building up from a neuron to a neural network
49:11 - A demo of representation learning in TF Playground
53:24 - Importance of activation functions
54:36 - What’s a neural network library?
58:43 - Overfitting and underfitting
1:02:38 - Autoencoders (and anomaly detection) screencast and demo
1:12:13 - Book recommendations
Here are three helpful classes you can check out to learn more:
Intro to Deep Learning from MIT → http://goo.gle/3sPj8To
MIT Deep Learning and Artificial Intelligence Lectures → https://goo.gle/3qh7H54
Convolutional Neural Networks for Visual Recognition from Stanford → http://goo.gle/3bbC34I
And here are all the links to demos and code from the video, in the order they appeared:
Face and hand tracking demos → http://goo.gle/2WTCwSc
Teachable machine demo → https://goo.gle/3bSCzCi
What features does a network see? → http://goo.gle/3e2zpA5
DeepDream tutorials → http://goo.gle/3bYIBTp and http://goo.gle/384B6JC
Hyperparameter tuning with Keras Tuner → http://goo.gle/2InBK7J
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs → http://goo.gle/309pMY5
Linear (and deep) regression tutorial → http://goo.gle/3sKxkN7
Image classification with a CNN tutorial → http://goo.gle/3qdD2Wb
Audio recognition tutorial → http://goo.gle/3kFpl1j
Transfer learning tutorial → http://goo.gle/3bV7D60
RNN tutorial (sentiment analysis / text classification) → http://goo.gle/3bVM1X7
RNN tutorial (text generation with Shakespeare) → http://goo.gle/3qmnrnz
Timeseries forecasting tutorial (weather) → http://goo.gle/3ecdYg9
Sketch RNN demo (draw together with a neural network) → http://goo.gle/3bbHTTy
Machine translation tutorial (English to Spanish) → http://goo.gle/3e7IJme
Image captioning tutorial → http://goo.gle/3sKFNQz
Autoencoders and anomaly detection tutorial → http://goo.gle/30aD0UA
GANs tutorial (Pix2Pix) → http://goo.gle/3kI1ZrB
A Deep Learning Approach to Antibiotic Discovery → https://goo.gle/3e7ivQD
Integrated gradients tutorial → http://goo.gle/2PxfRtq and http://goo.gle/3sE0bmq
TensorFlow Playground demos → http://goo.gle/2Px6rhB
Introduction to gradients and automatic differentiation → http://goo.gle/3sFVybo
Basic image classification tutorial → http://goo.gle/3c2AF3o
Overfitting and underfitting tutorial → http://goo.gle/3cdA9Qv
Keras early stopping callback → http://goo.gle/308XQUj
Interactive autoencoders demo (anomaly detection) → http://goo.gle/3kPfW7q
Deep Learning with Python, Second Edition → http://goo.gle/3qcQ5Y5
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition → http://goo.gle/386DKP4
Deep Learning book → http://goo.gle/3c2VQmd
Find Josh on Twitter → https://goo.gle/308Ve8P
Subscribe to TensorFlow → https://goo.gle/TensorFlow
