Video yükleniyor...

Video Yüklenemedi

Bu video yüklenirken bir sorun oluştu. Bu geçici bir ağ sorunundan kaynaklanıyor olabilir veya video kullanılamıyor olabilir.

Ana Sayfaya Dön

Machine Learning from Scratch, part 1. Part 2 adds three lines to this exact program and the line turns into a neuron. #machinelearning #python #gradientdescent #linearregression #neuralnetworks

MachineLearningToGo

27,900 subscribers

357,300 görüntüleme • 1 ay önce •via TikTok

Bilim & Teknoloji #machinelearning #python #gradientdescent #linearregression #neuralnetworks

Anya Rossi• Live Now

Private livecam show

0 Yorum

Yorum bulunmuyor

Orijinal gönderinin yorumları burada görünecek

Benzer Videolar

➡️ Machine Learning from Scratch, part 47: the Hopfield network. In 1982 the physicist John Hopfield described a network that works as a memory, and 42 years later this work was part of the Nobel Prize in Physics. Each neuron is one pixel that is either on or off, and every neuron is connected to every other neuron, so 100 pixels need 4,950 connections. There are no layers: signals go back and forth until the network settles. Each neuron takes a weighted sum of its inputs and switches on if the sum is positive, off if it is negative. Storing a picture is simple: for every pair of pixels with the same colour the connection gets stronger, otherwise weaker. Every stored picture becomes a valley in the energy landscape, and no switch can ever raise the energy, so a noisy picture rolls downhill into the nearest stored memory. The code (hopfield.py, 16 lines of numpy, pictures in pics.txt) stores three 10 x 10 pictures, a heart, a house and a smiley, and flips 30 of the 100 pixels in each. After a single pass over all neurons all three come back exactly; the energy of the heart falls from -6.90 to -48.90, the energy of the stored heart. With 100 neurons and random patterns (10 flipped pixels, 20 runs) it recalls 5 patterns every time, 10 patterns in 94.5 % of the cases, 14 in 68 %, 20 in about one in four (24 %) and 30 in 1 %. Honest notes: the weights are kept as whole numbers (100 times the Hebb weight), so a sum of exactly zero switches the neuron on, as the rule says. The landscape in the video is a picture of the idea; the valley floors and the height of the rolling ball are the real energies from the run. #machinelearning #ai #coding #neuralnetworks #nobelprize

➡️ Machine Learning from Scratch, part 47: the Hopfield network. In 1982 the physicist John Hopfield described a network that works as a memory, and 42 years later this work was part of the Nobel Prize in Physics. Each neuron is one pixel that is either on or off, and every neuron is connected to every other neuron, so 100 pixels need 4,950 connections. There are no layers: signals go back and forth until the network settles. Each neuron takes a weighted sum of its inputs and switches on if the sum is positive, off if it is negative. Storing a picture is simple: for every pair of pixels with the same colour the connection gets stronger, otherwise weaker. Every stored picture becomes a valley in the energy landscape, and no switch can ever raise the energy, so a noisy picture rolls downhill into the nearest stored memory. The code (hopfield.py, 16 lines of numpy, pictures in pics.txt) stores three 10 x 10 pictures, a heart, a house and a smiley, and flips 30 of the 100 pixels in each. After a single pass over all neurons all three come back exactly; the energy of the heart falls from -6.90 to -48.90, the energy of the stored heart. With 100 neurons and random patterns (10 flipped pixels, 20 runs) it recalls 5 patterns every time, 10 patterns in 94.5 % of the cases, 14 in 68 %, 20 in about one in four (24 %) and 30 in 1 %. Honest notes: the weights are kept as whole numbers (100 times the Hebb weight), so a sum of exactly zero switches the neuron on, as the rule says. The landscape in the video is a picture of the idea; the valley floors and the height of the rolling ball are the real energies from the run. #machinelearning #ai #coding #neuralnetworks #nobelprize

MachineLearningToGo

11,900 görüntüleme • 12 gün önce

➡️ Machine Learning from Scratch, part 40: is linear regression machine learning? Linear regression is machine learning, because it learns a weight and a bias from examples with known answers, which makes it supervised learning in its simplest form. What makes it special is that the best weight and bias can also be computed in one step with the normal equation, w = (X^T X)^-1 X^T y, so standard tools usually solve it directly with a least squares routine without any training loop. Gradient descent comes back when the data gets really huge, and it trains neural networks, where there is generally no such formula. We ran everything ourselves and downloaded nothing. linreg_ml.py (14 lines of numpy) takes the same eight points as part 1. Gradient descent with the same settings as part 1 (start at 0, learning rate 0.01) still has a bias of 1.995 after 1,000 steps, while the formula gives 2.029 (weight 0.796), and it needs 2,052 steps to come within 0.0005 of the formula. The least squares routine gives the same numbers as the formula. Honest notes: on eight points the formula is instant, and on millions of rows with many features the picture changes, which is when step by step training pays off. #machinelearning #ai #linearregression #datascience #coding

➡️ Machine Learning from Scratch, part 40: is linear regression machine learning? Linear regression is machine learning, because it learns a weight and a bias from examples with known answers, which makes it supervised learning in its simplest form. What makes it special is that the best weight and bias can also be computed in one step with the normal equation, w = (X^T X)^-1 X^T y, so standard tools usually solve it directly with a least squares routine without any training loop. Gradient descent comes back when the data gets really huge, and it trains neural networks, where there is generally no such formula. We ran everything ourselves and downloaded nothing. linreg_ml.py (14 lines of numpy) takes the same eight points as part 1. Gradient descent with the same settings as part 1 (start at 0, learning rate 0.01) still has a bias of 1.995 after 1,000 steps, while the formula gives 2.029 (weight 0.796), and it needs 2,052 steps to come within 0.0005 of the formula. The least squares routine gives the same numbers as the formula. Honest notes: on eight points the formula is instant, and on millions of rows with many features the picture changes, which is when step by step training pays off. #machinelearning #ai #linearregression #datascience #coding

MachineLearningToGo

22,700 görüntüleme • 23 gün önce

➡️Machine Learning from Scratch, part 51: dropout. At every training step each hidden neuron flips its own coin and about half of them are switched off, a new random half every step. No neuron can lean on its neighbours, so the knowledge spreads over many neurons, and only patterns that survive these small tests get stronger. After training, dropout is switched off and all neurons work together, like the average of many smaller networks that share the same weights. The code (dropout_reel.py, 16 lines of numpy) trains the same 1-64-1 tanh network twice on the same 12 noisy points of sin(3x), 60,000 steps of plain gradient descent, once the plain way and once with inverted dropout (chance one half, survivors multiplied by two). On 99 new points of the same curve the plain network ends at an error of 0.135 (its training error is almost zero, and its test error grows from 0.026 at step 100), the dropout network at 0.045: three times smaller, with a training error of 0.069. Sources: Hinton et al. 2012, arXiv:1207.0580; Srivastava et al. 2014, JMLR 15. #machinelearning #ai #coding #neuralnetworks #deeplearning

➡️Machine Learning from Scratch, part 51: dropout. At every training step each hidden neuron flips its own coin and about half of them are switched off, a new random half every step. No neuron can lean on its neighbours, so the knowledge spreads over many neurons, and only patterns that survive these small tests get stronger. After training, dropout is switched off and all neurons work together, like the average of many smaller networks that share the same weights. The code (dropout_reel.py, 16 lines of numpy) trains the same 1-64-1 tanh network twice on the same 12 noisy points of sin(3x), 60,000 steps of plain gradient descent, once the plain way and once with inverted dropout (chance one half, survivors multiplied by two). On 99 new points of the same curve the plain network ends at an error of 0.135 (its training error is almost zero, and its test error grows from 0.026 at step 100), the dropout network at 0.045: three times smaller, with a training error of 0.069. Sources: Hinton et al. 2012, arXiv:1207.0580; Srivastava et al. 2014, JMLR 15. #machinelearning #ai #coding #neuralnetworks #deeplearning

MachineLearningToGo

14,500 görüntüleme • 4 gün önce

How to learn Machine Learning from scratch in 30 days, completely for free. #ml #machinelearning #ai #coding #codingforbeginners

How to learn Machine Learning from scratch in 30 days, completely for free. #ml #machinelearning #ai #coding #codingforbeginners

Erik Cupsa

13,100 görüntüleme • 5 gün önce

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

117,748 görüntüleme • 1 yıl önce

➡️ Part 9 of learning ML code from scratch Six weights, one wrong answer, and not one of them knows it was at fault. Backpropagation is how they find out. Two questions guide the video: what even is backpropagation, and why do models need it? Step 1, the forward pass. Two numbers go into a tiny network, run through a hidden layer, and one number comes out. Ours says 0.700. We wanted 1.0, so square the difference and the loss is 0.09. That single number is how wrong the model is. Step 2, the question. Six weights produced that answer together. Which of them is to blame, and by how much? Step 3, the one idea. Change a single weight by a tiny amount and watch the error move. How much the error changes per unit of weight is the gradient of that weight. In the video you can see it happen: the weight wobbles and the error bar answers. Step 4, the shortcut. Every weight running into the same neuron starts from one shared number, the blame of that neuron. Multiply that blame by the value each weight carried and you have its gradient. One blame, computed once, and every weight into that neuron gets its own gradient from it. That is why backpropagation is fast enough to train anything at all. Step 5, backwards. The blame starts at the output and every layer hands its share to the layer in front of it, back through the same network the data came forward through. It goes layer by layer, and inside one layer every neuron is done together. Step 6, the update. The optimizer moves every weight against its own gradient, the whole weight matrix in one go, so no weight waits for its turn. All six gradients here are negative, so all six weights go up. After one step the answer reads 0.769. After sixty it reads 0.968 and the error is down to 0.001. The honest part: every gradient in the video was checked against the measured change of the error. Move that one weight, see what the error does, divide. Backprop and the measurement agree to nine decimal places, otherwise nothing would have rendered. #machinelearning #backpropagation #neuralnetworks #python #coding

➡️ Part 9 of learning ML code from scratch Six weights, one wrong answer, and not one of them knows it was at fault. Backpropagation is how they find out. Two questions guide the video: what even is backpropagation, and why do models need it? Step 1, the forward pass. Two numbers go into a tiny network, run through a hidden layer, and one number comes out. Ours says 0.700. We wanted 1.0, so square the difference and the loss is 0.09. That single number is how wrong the model is. Step 2, the question. Six weights produced that answer together. Which of them is to blame, and by how much? Step 3, the one idea. Change a single weight by a tiny amount and watch the error move. How much the error changes per unit of weight is the gradient of that weight. In the video you can see it happen: the weight wobbles and the error bar answers. Step 4, the shortcut. Every weight running into the same neuron starts from one shared number, the blame of that neuron. Multiply that blame by the value each weight carried and you have its gradient. One blame, computed once, and every weight into that neuron gets its own gradient from it. That is why backpropagation is fast enough to train anything at all. Step 5, backwards. The blame starts at the output and every layer hands its share to the layer in front of it, back through the same network the data came forward through. It goes layer by layer, and inside one layer every neuron is done together. Step 6, the update. The optimizer moves every weight against its own gradient, the whole weight matrix in one go, so no weight waits for its turn. All six gradients here are negative, so all six weights go up. After one step the answer reads 0.769. After sixty it reads 0.968 and the error is down to 0.001. The honest part: every gradient in the video was checked against the measured change of the error. Move that one weight, see what the error does, divide. Backprop and the measurement agree to nine decimal places, otherwise nothing would have rendered. #machinelearning #backpropagation #neuralnetworks #python #coding

MachineLearningToGo

210,300 görüntüleme • 1 ay önce

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

109,764 görüntüleme • 1 yıl önce

Let's learn how to build an AI Agent from scratch in Python. Today, we’ll run a language model on our computer, connect it to Python through localhost, send it a message, and print the response. This gives us the first piece of our agent: a Python program that can communicate with the model. Follow along for part 2! #datascientist #ai #agenticai #python #STEM

Let's learn how to build an AI Agent from scratch in Python. Today, we’ll run a language model on our computer, connect it to Python through localhost, send it a message, and print the response. This gives us the first piece of our agent: a Python program that can communicate with the model. Follow along for part 2! #datascientist #ai #agenticai #python #STEM

Lera Andronova

199,500 görüntüleme • 2 ay önce

➡️ Machine Learning from Scratch, part 31: the transformer. A transformer turns every token into a list of numbers, adds its position, and then lets every token look at the others. That step is called attention: each token has a query (what am I looking for), a key (what do I contain) and a value (what I pass on). The better a query matches a key, the more of that value flows in. Real transformers repeat this in many layers with several attention heads each. The design comes from the 2017 Google paper "Attention Is All You Need" by Ashish Vaswani and colleagues, first used for translation. A large language model is a transformer trained to predict the next token, again and again, on a huge amount of text. Our own run, nothing downloaded. attention.py is one self-attention layer in 16 lines of numpy. Every word gets a query, a key and a value of 8 numbers, starting random, trained from scratch with hand-written backpropagation on 192 made-up sentences like "the dog ate the carrot because it was hungry". The task: find out which pet "it" means. On 64 new test sentences "it" puts 99.95 percent of its attention on the right word on average (the lowest single sentence gets 99.9 percent; the program prints this as 100%). Before training it was 10 percent, spread evenly. The cat sentence from the video is one of the test sentences. Honest notes: our task is easy on purpose. Every sentence has exactly one pet, so "it" only has to learn to look for pets, and it learns that within the first pass. Real sentences with two candidates need more context, which is what many layers are for. Our small version has no positions, no scaling, no mask, one head, and only "it" asks a question. Every word of the test sentences also appears in training; the combinations are new. The 3D space in the video shows the real keys of our model, projected to three axes (two of them stretched so you can see them). The stacked layers are a sketch. #machinelearning #ai #transformer #python #llm

➡️ Machine Learning from Scratch, part 31: the transformer. A transformer turns every token into a list of numbers, adds its position, and then lets every token look at the others. That step is called attention: each token has a query (what am I looking for), a key (what do I contain) and a value (what I pass on). The better a query matches a key, the more of that value flows in. Real transformers repeat this in many layers with several attention heads each. The design comes from the 2017 Google paper "Attention Is All You Need" by Ashish Vaswani and colleagues, first used for translation. A large language model is a transformer trained to predict the next token, again and again, on a huge amount of text. Our own run, nothing downloaded. attention.py is one self-attention layer in 16 lines of numpy. Every word gets a query, a key and a value of 8 numbers, starting random, trained from scratch with hand-written backpropagation on 192 made-up sentences like "the dog ate the carrot because it was hungry". The task: find out which pet "it" means. On 64 new test sentences "it" puts 99.95 percent of its attention on the right word on average (the lowest single sentence gets 99.9 percent; the program prints this as 100%). Before training it was 10 percent, spread evenly. The cat sentence from the video is one of the test sentences. Honest notes: our task is easy on purpose. Every sentence has exactly one pet, so "it" only has to learn to look for pets, and it learns that within the first pass. Real sentences with two candidates need more context, which is what many layers are for. Our small version has no positions, no scaling, no mask, one head, and only "it" asks a question. Every word of the test sentences also appears in training; the combinations are new. The 3D space in the video shows the real keys of our model, projected to three axes (two of them stretched so you can see them). The stacked layers are a sketch. #machinelearning #ai #transformer #python #llm

MachineLearningToGo

79,900 görüntüleme • 1 ay önce

➡️ ML news part 1 An AI just fixed another AI by rewriting its weights directly, without any training. Machine Learning News, part 1: weight space programming. What is new, and why it matters. Until now, the weights of a neural network were set by training, and reading them took special tools built for that one job. A new report from researchers at MIT CSAIL shows that a general AI model, here GPT-6 Astra used as it is, can read and write those weights directly as plain lists of numbers. Test 1, reading. The model was given only the 17,253 weights of four small game playing networks (Breakout, Pong, Pac-Man, Freeway) and named the right game for all four. Test 2, writing. Without any data, loss or gradients, it wrote networks for three MinAtar games, and all three beat networks trained with PPO for 100 million steps. In Breakout it scored 517.4 points, against 25.3 after 500 million training steps. Test 3, repairing. The small open language model Qwen3.5-0.8B ran into its length limit on 93 percent of MATH-500 answers because it got stuck in loops. The model traced the loop to one direction written by the MLPs of layers 15 and 19 and removed it. That changed 131,072 weights, which is 0.016 percent of the model. MATH-500 went from 7.9 to 48.1 percent, IFEval from 21.0 to 62.3 percent, GSM8K from 13.8 to 67.1 percent, all with no training. The edit was chosen with a budget of 50 trial runs on practice problems, and the test sets were never seen. To be fair, the authors say the fix removed an obstacle and added no new ability, and on another small model it brought no improvement. This is an early report and not yet peer reviewed. Source: Takehi, Teoh, Zhang, Khattab (MIT CSAIL), "Sparks of Weight-Space Programming", blog post, November 2, 2026, (early report, not peer reviewed). Screenshots from the report are shown as quotes. Model names are given only to name the source. #machinelearning #ai #claude #neuralnetworks #llm

➡️ ML news part 1 An AI just fixed another AI by rewriting its weights directly, without any training. Machine Learning News, part 1: weight space programming. What is new, and why it matters. Until now, the weights of a neural network were set by training, and reading them took special tools built for that one job. A new report from researchers at MIT CSAIL shows that a general AI model, here GPT-6 Astra used as it is, can read and write those weights directly as plain lists of numbers. Test 1, reading. The model was given only the 17,253 weights of four small game playing networks (Breakout, Pong, Pac-Man, Freeway) and named the right game for all four. Test 2, writing. Without any data, loss or gradients, it wrote networks for three MinAtar games, and all three beat networks trained with PPO for 100 million steps. In Breakout it scored 517.4 points, against 25.3 after 500 million training steps. Test 3, repairing. The small open language model Qwen3.5-0.8B ran into its length limit on 93 percent of MATH-500 answers because it got stuck in loops. The model traced the loop to one direction written by the MLPs of layers 15 and 19 and removed it. That changed 131,072 weights, which is 0.016 percent of the model. MATH-500 went from 7.9 to 48.1 percent, IFEval from 21.0 to 62.3 percent, GSM8K from 13.8 to 67.1 percent, all with no training. The edit was chosen with a budget of 50 trial runs on practice problems, and the test sets were never seen. To be fair, the authors say the fix removed an obstacle and added no new ability, and on another small model it brought no improvement. This is an early report and not yet peer reviewed. Source: Takehi, Teoh, Zhang, Khattab (MIT CSAIL), "Sparks of Weight-Space Programming", blog post, November 2, 2026, (early report, not peer reviewed). Screenshots from the report are shown as quotes. Model names are given only to name the source. #machinelearning #ai #claude #neuralnetworks #llm

MachineLearningToGo

55,800 görüntüleme • 9 gün önce

Curious how #AI predicts the future? 🤔 Watch my quick tutorial on estimating Vancouver housing prices with beginner-friendly #LinearRegression. #DataScience #MachineLearning #Statistics #Python

Curious how #AI predicts the future? 🤔 Watch my quick tutorial on estimating Vancouver housing prices with beginner-friendly #LinearRegression. #DataScience #MachineLearning #Statistics #Python

Mariya Sha

20,806 görüntüleme • 1 yıl önce

The Body Swap Workout Program by Three Degrees Off Center Productions (Mark and his trainer Michelle body swap) Source Part 1: Source: Part 2:

The Body Swap Workout Program by Three Degrees Off Center Productions (Mark and his trainer Michelle body swap) Source Part 1: Source: Part 2:

Raw Swap Moments

11,920 görüntüleme • 3 yıl önce

Introduction to #MachineLearning, Part 1 🧠 #MachineLearningFundamentals

Introduction to #MachineLearning, Part 1 🧠 #MachineLearningFundamentals

MATLAB

10,076 görüntüleme • 3 yıl önce

Traffic Stop Turns Into Complete Chaos. Part (1/2)

Traffic Stop Turns Into Complete Chaos. Part (1/2)

GFY TV

11,243 görüntüleme • 7 ay önce

IA, Machine learning, Deep learning : comprendre chaque terme. Tu veux créer ton premier modèle de deep learning ? J’organise ce samedi 13 juin un atelier en ligne gratuit de 2h. Inscription : #ia #machinelearning #deeplearning #python

IA, Machine learning, Deep learning : comprendre chaque terme. Tu veux créer ton premier modèle de deep learning ? J’organise ce samedi 13 juin un atelier en ligne gratuit de 2h. Inscription : #ia #machinelearning #deeplearning #python

Clémence Audrey Emaha

14,000 görüntüleme • 5 ay önce

Part 1 of 2 of my shots from the RWBY x JL pt 2 movie! It was definitely a learning experience and a lot of fun!

Part 1 of 2 of my shots from the RWBY x JL pt 2 movie! It was definitely a learning experience and a lot of fun!

Arryn Troche (They/Them)

14,042 görüntüleme • 3 yıl önce

Part 1 of 12: building a tiny SQLite-style database in C from scratch. Today is the REPL skeleton: the db > command line, getline, .exit, EOF handling, and the loop every future parser, row store, pager, and B-tree feature plugs into. This tiny command shell becomes a database.

Part 1 of 12: building a tiny SQLite-style database in C from scratch. Today is the REPL skeleton: the db > command line, getline, .exit, EOF handling, and the loop every future parser, row store, pager, and B-tree feature plugs into. This tiny command shell becomes a database.

tetsuo

16,367 görüntüleme • 5 ay önce

AI and Machine Learning is NOT what you expect #ai #machinelearning #chatgpt #softwareengineer #fyp

AI and Machine Learning is NOT what you expect #ai #machinelearning #chatgpt #softwareengineer #fyp

Sajjaad Khader

235,500 görüntüleme • 2 yıl önce