How does AI learn? Is AI conscious & sentient? Can AI break encryption? How does GPT & image generation work? What's a neural network?
#ai #agi #qstar #singularity #gpt #imagegeneration #stablediffusion #humanoid #neuralnetworks #deeplearning

I used this to create neural nets:
https://alexlenail.me/NN-SVG/index.html

More info on neural networks
https://youtu.be/aircAruvnKk?si=Go9XAXR8TqmhX-5m

How stable diffusion works
https://youtu.be/sFztPP9qPRc?si=aF-doepEiaiDrG6z

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MIT Introduction to Deep Learning 6.S191: Lecture 4
Deep Generative Modeling
Lecturer: Ava Amini
*New 2024 Edition*

For all lectures, slides, and lab materials: http://introtodeeplearning.com​

Lecture Outline
0:00​ - Introduction
6:10- Why care about generative models?
8:16​ - Latent variable models
10:50​ - Autoencoders
17:02​ - Variational autoencoders
23:25 - Priors on the latent distribution
32:31​ - Reparameterization trick
34:36​ - Latent perturbation and disentanglement
37:40 - Debiasing with VAEs
39:37​ - Generative adversarial networks
42:09​ - Intuitions behind GANs
44:57 - Training GANs
48:28 - GANs: Recent advances
50:57 - CycleGAN of unpaired translation
55:03 - Diffusion Model sneak peak

Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

MIT Introduction to Deep Learning 6.S191: Lecture 2
Recurrent Neural Networks
Lecturer: Ava Amini
** New 2024 Edition **

For all lectures, slides, and lab materials: http://introtodeeplearning.com

Lecture Outline
0:00​ - Introduction
3:42​ - Sequence modeling
5:30​ - Neurons with recurrence
12:20 - Recurrent neural networks
14:08 - RNN intuition
17:14​ - Unfolding RNNs
19:54 - RNNs from scratch
22:41 - Design criteria for sequential modeling
24:24 - Word prediction example
31:50​ - Backpropagation through time
33:40 - Gradient issues
37:15​ - Long short term memory (LSTM)
40:00​ - RNN applications
44:00- Attention fundamentals
46:46 - Intuition of attention
49:13 - Attention and search relationship
51:22 - Learning attention with neural networks
57:45 - Scaling attention and applications
1:00:08 - Summary
Subscribe to stay up to date with new deep learning lectures at MIT, or follow us @MITDeepLearning on Twitter and Instagram to stay fully-connected!!

Shortform link:
https://shortform.com/artem

In this video we will talk about backpropagation – an algorithm powering the entire field of machine learning and try to derive it from first principles.

OUTLINE:
00:00 Introduction
01:28 Historical background
02:50 Curve Fitting problem
06:26 Random vs guided adjustments
09:43 Derivatives
14:34 Gradient Descent
16:23 Higher dimensions
21:36 Chain Rule Intuition
27:01 Computational Graph and Autodiff
36:24 Summary
38:16 Shortform
39:20 Outro

USEFUL RESOURCES:
Andrej Karpathy's playlist: https://youtube.com/playlist?list=PLAqhIrjkxbuWI23v9cThsA9GvCAUhRvKZ&si=zBUZW5kufVPLVy9E

Jürgen Schmidhuber's blog on the history of backprop:
https://people.idsia.ch/~juerg....en/who-invented-back


CREDITS:
Icons by https://www.freepik.com/

El Machine Learning y el Deep Learning son clave para la evolución de la Inteligencia Artificial porque permiten que las máquinas puedan aprender y mejorar por sí mismas, logrando que estas máquinas puedan hacer tareas complejas y resolver problemas de manera autónoma. Pero, ¿qué es Machine Learning y Deep Learning exactamente? Te lo explicaremos en este nuevo video de EDteam.

Y si no lo sabías #LoAprendisteEnEDteam

🎉 ¡CURSOS NUEVOS DE LA SEMANA!
1. Curso: Android desde cero https://edy.to/android-yt
2. Curso: Desarrollo de apps con Vue 3 y TypeScript (Pre-venta) https://edy.to/vue-typescript-yt

Si quieres que EDteam de una conferencia gratis en tu institución, contáctanos aquí 👉 https://ed.team/conferencias

💻 Cursos recomendados:
- Carrera: Machine Learning e Inteligencia Artificial https://ed.team/especialidades/machine-learning
- Curso: Introducción al Machine Learning https://edy.to/ML-yt
- Curso: Introducción a las redes neuronales artificiales https://edy.to/redes-neuronales-yt
- Curso: Redes neuronales con TensorFlow https://edy.to/tensorflow-yt


📺 Videos recomendados
- ¿Qué es la Inteligencia Artificial y cómo está cambiando al mundo? https://www.youtube.com/watch?v=98-SzelNMzU
- ¿Qué es el Big Data? https://www.youtube.com/watch?v=M26iIqmqWkI&t=6s
- ¡Los peores errores de programación de la historia! https://www.youtube.com/watch?v=qi9nFs2caj4

⌚ Timeline:
00:00 - Introducción
00:59 - Que es Machine Learning
04:50 - Tipos de MAchine Learning
08:00 - Las Redes Neuronales
11:40 - El Deep Learning


- 🧑‍🎓👩‍🎓 ¿Eres estudiante? Postula a las becas de EDteam: https://edy.to/estudiantes-yt
- 🎁 ¡Accede a 9 cursos GRATIS de tecnología! https://edy.to/cursos-gratis-yt
- 🧑‍🏫 Dicta un curso en EDteam: https://edy.to/profesores-yt
- ⭐ Sube a premium y accede a cientos de cursos: https://edy.to/premium-yt
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This deep learning course is designed to take you from beginner to proficient in deep learning. You will learn the fundamental concepts, architectures, and applications of deep learning in a clear and practical way. So get ready to build, train, and deploy models that can tackle real-world problems across various industries.

Course created by @AyushSinghSh

GitHub: https://github.com/ayush714/co....re-deep-learning-cou

⭐️ Contents ⭐️
0:00:00 Intro
0:03:07 Getting started
0:05:07 Vectors
0:21:51 Operation on vectors
0:38:52 Matrices
0:52:02 Operation on Matrices
0:52:27 Matrix Scalar Multiplication
0:55:47 Addition of Matrices
0:59:27 Properties of Matrix addition
1:03:07 Matrix Multiplication
1:08:02 Properties of Matrix Multiplication
1:18:32 Linear Combination Concept
1:36:20 Span
1:50:57 Linear Transformation
2:05:30 Transpose
2:14:02 Properties of Transpose
2:19:52 Dot Product
2:25:22 Geometric Meaning of Dot Product
2:34:32 Types of Matrices
3:04:22 Determinant
3:11:17 Geometric Meaning of Determinant
3:15:42 Calculating Determinant
3:23:37 Properties of Determinant
3:27:22 Rule of Sarus
3:48:42 Minor
3:56:49 Cofactor of a Matrix
4:00:42 Steps to calculate Cofactor of a Matrix
4:03:17 Adjoint of a Matrix
4:18:47 Trace of a Matrix
4:17:22 Properties of Trace
4:38:17 System of Equations
5:03:07 Example
5:17:42 Determinant
5:57:47 Single Variable Calculus
6:02:48 What is Calculus?
6:11:07 Ideas in Calculus
6:11:33 Differentiation
6:18:38 Integration
6:22:07 Precalculus Functions
6:43:52 Single Variable Calculus (Trigonometry Review)
6:45:02 Trigonometry functions
7:12:02 Unit Circle
7:24:32 Limit Concept
7:51:47 Definition of a limit
7:53:27 Continuity
8:00:17 Evaluating Limits
8:17:12 Sandwich Theorem
8:21:12 Differentiation
8:45:42 Differentiation as rate of Change
8:52:37 Differentiation in terms of Limit
9:04:51 Example
9:09:54 Important Differentiation Rules
9:53:12 Rule Chain Rule
10:17:27 What is Deep Learning
10:18:27 What is Machine Learning
10:36:37 Definition of Deep Learning
10:43:07 Applications
10:47:19 Introduction to Neural Networks
10:51:17 Artificial Neural Networks
11:08:31 The Perceptron
11:19:57 Linear Neural Network
11:21:32 Intuition Behind Activation function and Backpropagation Algorithm



🎉 Thanks to our Champion and Sponsor supporters:
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All Machine Learning algorithms intuitively explained in 17 min

In this video I will go through all machine learning algorithms in less than 17 minutes to get you an intuitive understanding of how they work and how they relate to each other as well as help you decide how to pick the right one for your problem. Going all the way from Linear Regression to Neural Networks / Deep Learning and Unsupervised Learning.

Also Watch: How to Learn Machine Learning in 2024 (7 step roadmap) https://youtu.be/jwTaBztqTZ0

Chapters:

00:00 - Intro: What is Machine Learning?
00:59 - Supervised Learning
01:37 - Unsupervised Learning
02:20 - Linear Regression
04:04 - Logistic Regression
04:53 - K Nearest Neighbors (KNN)
06:10 - Support Vector Machine (SVM)
07:51 - Naive Bayes Classifier
08:37 - Decision Trees
09:11 - Ensemble Algorithms
09:24 - Bagging & Random Forests
09:53 - Boosting & Strong Learners
10:26 - Neural Networks / Deep Learning
12:42 - Unsupervised Learning (again)
12:57 - Clustering / K-means
14:35 - Dimensionality Reduction
15:15 - Principal Component Analysis (PCA)

Qual a diferença entre Inteligência Artificial, Machine Learning e Deep Learning?

Neste vídeo trato das diferenças que existem entre as tecnologias de Inteligência Artificial, Aprendizado de Máquina e Aprendizado Profundo, além de abordar as aplicações de cada tecnologia.

Leia também: O que é Machine Learning? http://www.bosontreinamentos.c....om.br/inteligencia-a

Ajude o canal adquirindo meus cursos na Udemy:
Bancos de Dados com MySQL Básico: https://bit.ly/35QdWE4
Lógica de Programação com Português Estruturado: https://bit.ly/3QKPn22
Programação em Python do Zero: https://bit.ly/python-boson

Adquira também livros e outros itens na loja da Bóson Treinamentos na Amazon e ajude o canal a se manter e crescer:
https://www.amazon.com.br/shop/bosontreinamentos

Seja membro deste canal e ganhe benefícios:
https://www.youtube.com/channe....l/UCzOGJclZQvPVgYZIw

Contribua com a Bóson Treinamentos!:
http://www.bosontreinamentos.com.br/contribuir/

Por Fábio dos Reis
Bóson Treinamentos: http://www.bosontreinamentos.com.br
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Outros projetos do autor:
Diário do Naturalista: http://www.diariodonaturalista.com.br
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#bosontreinamentos #inteligenciaartificial #machinelearning

🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py
This Edureka "Convolutional Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you in understanding what is Convolutional Neural Network and how it works. It also includes a use-case, in which we will be creating a classifier using TensorFlow.

📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 𝟐𝟎𝟐𝟒 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩ NEW Top 10 Technologies To Learn In 2024 - https://www.youtube.com/watch?v=vaLXPv0ewHU

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

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Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: +18338555775 (toll-free) for more information.

Go from zero to a machine learning engineer in 12 months. This step-by-step roadmap covers the essential skills you must learn to become a machine learning engineer in 2024.

Download the FREE roadmap PDF here: https://mosh.link/machine-learning-roadmap

✋ Stay connected

- Complete courses: https://codewithmosh.com
- Twitter: https://twitter.com/moshhamedani
- Facebook: https://www.facebook.com/programmingwithmosh/
- Instagram: https://www.instagram.com/codewithmosh.official/
- LinkedIn: https://www.linkedin.com/school/codewithmosh/

🔗 Other roadmaps

https://youtu.be/Tef1e9FiSR0?si=QpVnZ_o9-DAXzT71
https://youtu.be/OeEHJgzqS1k?si=qd0ZIqAzUpZQn6BX

📚 Tutorials

https://youtu.be/_uQrJ0TkZlc?si=ZhlCrQs1SkaPNVa8
https://youtu.be/8JJ101D3knE?si=OGTuS35LQqSunuhh
https://youtu.be/BBpAmxU_NQo?si=dm-ZCPxVBYWS1Qhn
https://youtu.be/7S_tz1z_5bA?si=QL7s_M2Ao90RDwG8

📖 Chapters

00:00 - Introduction
00:20 - Programming Languages
00:42 - Version Control
01:03 - Data Structures & Algorithms
01:35 - SQL
01:55 - The Complete Roadmap PDF
02:19 - Mathematics & Statistics
02:40 - Data Handling
03:15 - Machine Learning Fundamentals
03:57 - Advanced Topics
04:28 - Model Deployment

#machinelearning #ai #datascience #coding #programming

TensorFlow is a tool for machine learning capable of building deep neural networks with high-level Python code. It provides developer-friendly APIs that help software engineers train, analyze, and deploy ML models.

#programming #deeplearning #100secondsofcode

🔗 Resources

TensorFlow Docs https://www.tensorflow.org/
Fashion MNIST Tutorial https://www.tensorflow.org/tutorials/keras/classification
Neural Networks Overview for Data Scientists https://www.ibm.com/cloud/learn/neural-networks
Machine Learning in 100 Seconds https://youtu.be/PeMlggyqz0Y

🔥 Get More Content - Upgrade to PRO

Upgrade to Fireship PRO at https://fireship.io/pro
Use code lORhwXd2 for 25% off your first payment.

🎨 My Editor Settings

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🔖 Topics Covered

- What is TensorFlow?
- How to build a neural network with TensorFlow
- What is TensorFlow used for?
- Who created TensorFlow?
- How neural networks work
- Easy neural network tutorial
- What is a mathematical Tensor?

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/

Deep Learning powers most of the technologies we rely on every day. From Machine Translation that can translate web pages in seconds to recommendations of shows to watch or even face recognition that lets us log in to our phones many times a day.

Even though we use it every day, it might still not be clear to you how deep learning works. Let's dive into the behind-the-scenes of deep learning in this video.

Get your free speech-to-text API token 👇
https://www.assemblyai.com/?utm_source=youtube&utm_medium=referral&utm_campaign=yt_mis_1

In this video, we will take a closer look at deep learning. We will learn what deep learning is, where in the world of Artificial Intelligence it stands, why it has been very successful in becoming part of our lives, and how it compares to more traditional machine learning algorithms.

Become better at machine learning in 5 min/ week 👉🏻 https://borismeinardus.substack.com/

In this video, I share how I would learn Machine Learning in 2024 if I could start over.
For the past 3 years, I have been studying machine learning (and 2 years before that basic computer science), which has now led me to work with an amazing ex-Meta professor, collaborate with Google DeepMind researchers, and have interviews at amazing companies.
Having learned from all of my failures and successes, this video breaks down how I would learn machine learning all over again, focusing on the essentials and learning from the best resources.
Enjoy!

👉🏻 Book a one-on-one call
https://calendly.com/boris-meinardus/consulting

👇🏻 Links to resources 👇🏻
==== Maths ====
https://www.edx.org/learn/prob....ability/harvard-univ
https://www.edx.org/learn/line....ar-algebra/the-unive
https://www.coursera.org/learn/matrix-algebra-engineers?irclickid=x-U2gpTSJxyLTxPwUx0Mo3EoUkDXeNXFjUFXWo0&irgwc=1
==== ML/ DL ====
https://www.coursera.org/speci....alizations/machine-l
https://www.youtube.com/playli....st?list=PLAqhIrjkxbu
https://www.coursera.org/specializations/deep-learning?irclickid=x-U2gpTSJxyLTxPwUx0Mo3EoUkDXeu01jUFXWo0&irgwc=1#courses
https://huggingface.co/learn/nlp-course/chapter1/1

⬇️ Follow me on my other socials and feel free to DM questions! ⬇️
⚫⚪ Medium: https://medium.com/@boris.meinardus
🐦 Twitter: https://twitter.com/BorisMeinardus

================== Timestamps ================
00:00 - Intro
00:40 - Python
01:29 - Maths
02:47 - ML Developer Stack
04:00 - Learn Machine Learning
06:06 - How To Really Get Good
=============================================

#ai #learning #machinelearning

Learn more about watsonx: https://ibm.biz/BdvxDS

What is really the difference between Artificial intelligence (AI) and machine learning (ML)? Are they actually the same thing? In this video, Jeff Crume explains the differences and relationship between AI & ML, as well as how related topics like Deep Learning (DL) and other types and properties of each.

#ai #ml #dl #artificialintelligence #machinelearning #deeplearning #watsonx

Demystifying attention, the key mechanism inside transformers and LLMs.
Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support
Special thanks to these supporters: https://www.3blue1brown.com/le....ssons/attention#than
An equally valuable form of support is to simply share the videos.

Demystifying self-attention, multiple heads, and cross-attention.
Instead of sponsored ad reads, these lessons are funded directly by viewers: https://3b1b.co/support

The first pass for the translated subtitles here is machine-generated, and therefore notably imperfect. To contribute edits or fixes, visit https://translate.3blue1brown.com/

And yes, at 22:00 (and elsewhere), "breaks" is a typo.

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

Here are a few other relevant resources

Build a GPT from scratch, by Andrej Karpathy
https://youtu.be/kCc8FmEb1nY

If you want a conceptual understanding of language models from the ground up, @vcubingx just started a short series of videos on the topic:
https://youtu.be/1il-s4mgNdI?si=XaVxj6bsdy3VkgEX

If you're interested in the herculean task of interpreting what these large networks might actually be doing, the Transformer Circuits posts by Anthropic are great. In particular, it was only after reading one of these that I started thinking of the combination of the value and output matrices as being a combined low-rank map from the embedding space to itself, which, at least in my mind, made things much clearer than other sources.
https://transformer-circuits.p....ub/2021/framework/in

Site with exercises related to ML programming and GPTs
https://www.gptandchill.ai/codingproblems

History of language models by Brit Cruise,  @ArtOfTheProblem 
https://youtu.be/OFS90-FX6pg

An early paper on how directions in embedding spaces have meaning:
https://arxiv.org/pdf/1301.3781.pdf

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

Timestamps:
0:00 - Recap on embeddings
1:39 - Motivating examples
4:29 - The attention pattern
11:08 - Masking
12:42 - Context size
13:10 - Values
15:44 - Counting parameters
18:21 - Cross-attention
19:19 - Multiple heads
22:16 - The output matrix
23:19 - Going deeper
24:54 - Ending

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

These animations are largely made using a custom Python library, manim. See the FAQ comments here:
https://3b1b.co/faq#manim
https://github.com/3b1b/manim
https://github.com/ManimCommunity/manim/

All code for specific videos is visible here:
https://github.com/3b1b/videos/

The music is by Vincent Rubinetti.
https://www.vincentrubinetti.com
https://vincerubinetti.bandcam....p.com/album/the-musi
https://open.spotify.com/album..../1dVyjwS8FBqXhRunaG5

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

3blue1brown is a channel about animating math, in all senses of the word animate. If you're reading the bottom of a video description, I'm guessing you're more interested than the average viewer in lessons here. It would mean a lot to me if you chose to stay up to date on new ones, either by subscribing here on YouTube or otherwise following on whichever platform below you check most regularly.

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