Video wird geladen...

Video konnte nicht geladen werden

Beim Laden dieses Videos ist ein Problem aufgetreten. Dies könnte an einem vorübergehenden Netzwerkproblem liegen oder das Video ist möglicherweise nicht verfügbar.

Neural networks explained from first principles: how simple linear pieces combine into richer models, and why layered representations make machine learning more powerful. MIT OpenCourseWare, 6.036 Introduction to Machine Learning, Fall 2020.

tetsuo

233,130 subscribers

10,720 Aufrufe • vor 14 Tagen •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

if you're struggling on where to start learning ML, here’s a playlist of 30 youtube videos to learn machine learning fundamentals from scratch "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

ℏεsam

108,861 Aufrufe • vor 1 Jahr

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

a playlist of 30 youtube videos to learn machine learning fundamentals from scratch if you're struggling on where to start learning ML, this list goes this "Machine Learning: Teach by Doing" is a solid choice to learn both theory and code. (1) Introduction to Machine Learning Teach by Doing: (2) What is Machine Learning? History of Machine Learning: (3) Types of ML Models: (4) 6 steps of any ML project: (5) Install Python and VSCode and run your first code: (6) Linear Classifiers Part 1: (7) Linear Classifiers Part 2: (8) Jupyter Notebook, Numpy and Scikit-Learn: (9) Running the Random Linear Classifier Algorithm in Python: (10) The oldest ML model - Perceptron: (11) Coding the Perceptron: (12) Perceptron Convergence Theorem: (13) Magic of features in Machine Learning: (14) One hot encoding: (15) Logistic Regression Part 1: (16) Cross Entropy Loss: (17) How gradient descent works: (18) Logistic Regression from scratch in Python: (19) Introduction to Regularization: (20) Implementing Regularization in Python: (21) Linear Regression Introduction: (22) Ordinary Least Squares step by step implementation: (23) Ridge regression fundamentals and intuition: (24) Regression recap for interviews: (25) Neural network architecture in 30 minutes: (26) Backpropagation intuition: (27) Neural network activation functions: (28) Momentum in gradient descent: (29) Hands on neural network training in Python: (30) Introduction to Convolutional Neural Networks (CNNs):

ℏεsam

117,570 Aufrufe • vor 1 Jahr

Security, scalability, and interoperability are the core foundations of Oasis technology Dawn Song explains how cyber-attacks are becoming more sophisticated — using machine learning models themselves — and why we need to build resilient AI machine learning systems

Security, scalability, and interoperability are the core foundations of Oasis technology Dawn Song explains how cyber-attacks are becoming more sophisticated — using machine learning models themselves — and why we need to build resilient AI machine learning systems

Oasis Labs

24,279 Aufrufe • vor 3 Jahren

The 2024 Nobel Prize in Physics has been awarded to Geoffrey Hinton and John Hopfield for machine learning with artificial neural networks

The 2024 Nobel Prize in Physics has been awarded to Geoffrey Hinton and John Hopfield for machine learning with artificial neural networks

Tsarathustra

106,654 Aufrufe • vor 1 Jahr

Neural Networks Learning Visualization

Neural Networks Learning Visualization

ₕₐₘₚₜₒₙ

47,392 Aufrufe • vor 1 Jahr

As AI advances towards mainstream adoption, security and privacy in machine learning models must be prioritized Our founder Dawn Song shares why security in machine learning systems is crucial as we begin to use AI for everyday purposes

As AI advances towards mainstream adoption, security and privacy in machine learning models must be prioritized Our founder Dawn Song shares why security in machine learning systems is crucial as we begin to use AI for everyday purposes

Oasis Labs

20,264 Aufrufe • vor 3 Jahren

'Introduction to Poker Theory' From Poker Theory & Analytics (MIT OpenCourseWare):

'Introduction to Poker Theory' From Poker Theory & Analytics (MIT OpenCourseWare):

Alex Bilzerian

58,287 Aufrufe • vor 1 Jahr

Security is a must for the development of AI and machine learning — and vice versa Dawn Song, Founder of Oasis Labs, explains how AI models can benefit security research View the full interview — where Dawn shares her journey into AI, Machine Learning, and web3 — below:

Security is a must for the development of AI and machine learning — and vice versa Dawn Song, Founder of Oasis Labs, explains how AI models can benefit security research View the full interview — where Dawn shares her journey into AI, Machine Learning, and web3 — below:

Oasis Labs

33,121 Aufrufe • vor 3 Jahren

Course on Matrix Methods in Data Analysis & Signal Processing Machine Learning in Finance represents one of AI's fastest growing applications, leveraging data driven models and algorithms to make financial predictions, manage risks, and automate trading decisions. At the forefront is algorithmic trading (quant trading), where ML models predict price movements and execute high speed trades by learning from historical market data including prices, volume, and news sentiment using sophisticated techniques like reinforcement learning, regression analysis, decision trees, and LSTM neural networks to gain competitive advantages in increasingly complex financial markets

Course on Matrix Methods in Data Analysis & Signal Processing Machine Learning in Finance represents one of AI's fastest growing applications, leveraging data driven models and algorithms to make financial predictions, manage risks, and automate trading decisions. At the forefront is algorithmic trading (quant trading), where ML models predict price movements and execute high speed trades by learning from historical market data including prices, volume, and news sentiment using sophisticated techniques like reinforcement learning, regression analysis, decision trees, and LSTM neural networks to gain competitive advantages in increasingly complex financial markets

D4rsh🦅

82,499 Aufrufe • vor 11 Monaten

Data preparation! It's crucial for machine learning, and we all hate it. Tools and techniques to reduce this burden? A quick summary of 10 years of R&D on this, from cheap tricks to LLMs and graph neural networks 1/9

Data preparation! It's crucial for machine learning, and we all hate it. Tools and techniques to reduce this burden? A quick summary of 10 years of R&D on this, from cheap tricks to LLMs and graph neural networks 1/9

Gael Varoquaux 🦋

13,752 Aufrufe • vor 1 Jahr

Famous YouTuber Zara Dar has quit her PhD and become an OnlyFans Content Creator Full Time. She used to make videos about Neural Networks, Machine Learning and other Tech Stuff, now she will be making content of a different genre. Thoughts?

Sensitive content

Famous YouTuber Zara Dar has quit her PhD and become an OnlyFans Content Creator Full Time. She used to make videos about Neural Networks, Machine Learning and other Tech Stuff, now she will be making content of a different genre. Thoughts?

Sensei Kraken Zero

19,913,784 Aufrufe • vor 1 Jahr

We've released 10+ Linear Algebra concepts for Machine Learning! Here's one of the visualization how PCA identifies direction of maximum variance.

We've released 10+ Linear Algebra concepts for Machine Learning! Here's one of the visualization how PCA identifies direction of maximum variance.

TensorTonic

48,303 Aufrufe • vor 5 Monaten

Famous YouTuber Zara Dar has quit her PhD and become an OnlyFans content creator full time She used to make videos about Neural Networks, Machine Learning and other Tech Stuff, now she will be making content of a different genre Thoughts on this

Sensitive content

Famous YouTuber Zara Dar has quit her PhD and become an OnlyFans content creator full time She used to make videos about Neural Networks, Machine Learning and other Tech Stuff, now she will be making content of a different genre Thoughts on this

I Post Forbidden Videos

53,981 Aufrufe • vor 9 Monaten

Machine learning

Machine learning

Tony Babel

643,273 Aufrufe • vor 1 Jahr

how to learn something from the first principles (with a live demo on reinforcement learning)

how to learn something from the first principles (with a live demo on reinforcement learning)

Ajeet ( opensox.ai )

229,634 Aufrufe • vor 1 Monat

This lecture was a really simple & practical introduction to how machines learn Bayesian Learning, Bayes Theorem, Naive Bayes from Kimia Lab by Professor H.R.Tizhoosh. The lecture also walks through - MAP - Maximum Likelihood - Bayes Optimal Classifier - Naive Bayes model A really beginner-friendly lecture if you want to understand how probability thinking shaped modern machine learning.

This lecture was a really simple & practical introduction to how machines learn Bayesian Learning, Bayes Theorem, Naive Bayes from Kimia Lab by Professor H.R.Tizhoosh. The lecture also walks through - MAP - Maximum Likelihood - Bayes Optimal Classifier - Naive Bayes model A really beginner-friendly lecture if you want to understand how probability thinking shaped modern machine learning.

𝗿𝗮𝗺𝗮𝗸𝗿𝘂𝘀𝗵𝗻𝗮— 𝗲/𝗮𝗰𝗰

14,816 Aufrufe • vor 29 Tagen

📚 Building a Museum and Learning Academy powered by AI and machine learning to transform onboarding. Bringing Web2 players into Web3 with knowledge, fun, and purpose.

📚 Building a Museum and Learning Academy powered by AI and machine learning to transform onboarding. Bringing Web2 players into Web3 with knowledge, fun, and purpose.

World of Dypians

34,345 Aufrufe • vor 1 Jahr

An Introduction to the Theory of Machine Learning by Avrim Blum of TTIC. Lecture notes: (audio generated by notebookLM)

An Introduction to the Theory of Machine Learning by Avrim Blum of TTIC. Lecture notes: (audio generated by notebookLM)

Math Cafe

12,964 Aufrufe • vor 1 Jahr

Holy shit… someone just made machine learning click. Not static diagrams. Not math-heavy PDFs. Not black-box training. Real algorithms — training step-by-step — visually. It’s called Machine Learning Visualized and it lets you watch models learn in real time. Here’s why this is different: Instead of dumping theory first, it shows optimization happening live: • gradients moving • weights updating • decision boundaries shifting • loss decreasing • models converging You literally see learning happen. Everything is built from first principles: • Gradient Descent • Logistic Regression • Perceptron • PCA • K-Means • Neural Networks • Backpropagation No magic. Just math → code → visualization. Each chapter is a Jupyter notebook that derives the math then implements it then animates training. So you can watch: • neural nets shape decision surfaces • PCA rotate feature space • K-means clusters form live • gradient descent find minima • sigmoid reshape boundaries • backprop update weights step-by-step This solves a huge problem: Most ML resources teach: math → code → ??? → trained model This shows: math → code → learning process → result Which means you finally understand: • why gradients matter • how weights evolve • what loss landscapes look like • how convergence actually happens • why deep nets learn non-linear functions Even better: You can open any notebook modify parameters and watch behavior change instantly. Learning ML becomes interactive. Not passive. Not abstract. Not confusing. Just… visible. Perfect for: • beginners learning ML • devs moving into AI • interview prep • teaching concepts • understanding backprop • visual learners • building intuition This is the kind of resource that makes neural networks finally “click”. Link: We’re moving from: reading about ML → watching ML learn That’s a big shift. Because once you can see training, you stop memorizing… and start understanding. AI education just got visual.

Holy shit… someone just made machine learning click. Not static diagrams. Not math-heavy PDFs. Not black-box training. Real algorithms — training step-by-step — visually. It’s called Machine Learning Visualized and it lets you watch models learn in real time. Here’s why this is different: Instead of dumping theory first, it shows optimization happening live: • gradients moving • weights updating • decision boundaries shifting • loss decreasing • models converging You literally see learning happen. Everything is built from first principles: • Gradient Descent • Logistic Regression • Perceptron • PCA • K-Means • Neural Networks • Backpropagation No magic. Just math → code → visualization. Each chapter is a Jupyter notebook that derives the math then implements it then animates training. So you can watch: • neural nets shape decision surfaces • PCA rotate feature space • K-means clusters form live • gradient descent find minima • sigmoid reshape boundaries • backprop update weights step-by-step This solves a huge problem: Most ML resources teach: math → code → ??? → trained model This shows: math → code → learning process → result Which means you finally understand: • why gradients matter • how weights evolve • what loss landscapes look like • how convergence actually happens • why deep nets learn non-linear functions Even better: You can open any notebook modify parameters and watch behavior change instantly. Learning ML becomes interactive. Not passive. Not abstract. Not confusing. Just… visible. Perfect for: • beginners learning ML • devs moving into AI • interview prep • teaching concepts • understanding backprop • visual learners • building intuition This is the kind of resource that makes neural networks finally “click”. Link: We’re moving from: reading about ML → watching ML learn That’s a big shift. Because once you can see training, you stop memorizing… and start understanding. AI education just got visual.

Suryansh Tiwari

132,193 Aufrufe • vor 2 Monaten