Serrano.AcademyAnnouncement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
A friendly description of K-means clustering and hierarchical clustering with simple examples. No math is needed, only a visual mind and a will to learn.
0:00 Introduction 0:24 Customer Segmentation 2:43 Clustering goal: group data 7:22 K-Means Clustering 7:47 Elbow method 13:17 Dendrogram 15:58 Applications
Clustering: K-means and HierarchicalSerrano.Academy2019-01-28 | Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
A friendly description of K-means clustering and hierarchical clustering with simple examples. No math is needed, only a visual mind and a will to learn.
0:00 Introduction 0:24 Customer Segmentation 2:43 Clustering goal: group data 7:22 K-Means Clustering 7:47 Elbow method 13:17 Dendrogram 15:58 ApplicationsReinforcement Learning with Human Feedback - How to train and fine-tune Transformer ModelsSerrano.Academy2024-02-12 | Reinforcement Learning with Human Feedback (RLHF) is a method used for training Large Language Models (LLMs). In the heart of RLHF lies a very powerful reinforcement learning method called Proximal Policy Optimization. Learn about it in this simple video!
This is the first one in a series of 3 videos dedicated to the reinforcement learning methods used for training LLMs.
Video 0 (Optional): Introduction to deep reinforcement learning youtube.com/watch?v=SgC6AZss478 Video 1: Proximal Policy Optimization youtube.com/watch?v=TjHH_--7l8g Video 2 (This one): Reinforcement Learning with Human Feedback Video 3 (Coming soon!): Deterministic Policy Optimization
00:00 Introduction 00:48 Intro to Reinforcement Learning (RL) 02:47 Intro to Proximal Policy Optimization (PPO) 4:17 Intro to Large Language Models (LLMs) 6:50 Reinforcement Learning with Human Feedback (RLHF) 13:08 Interpretation of the Neural Networks 14:36 Conclusion
Get the Grokking Machine Learning book! manning.com/books/grokking-machine-learning Discount code (40%): serranoyt (Use the discount code on checkout)Proximal Policy Optimization (PPO) - How to train Large Language ModelsSerrano.Academy2024-01-24 | Reinforcement Learning with Human Feedback (RLHF) is a method used for training Large Language Models (LLMs). In the heart of RLHF lies a very powerful reinforcement learning method called Proximal Policy Optimization. Learn about it in this simple video!
This is the first one in a series of 3 videos dedicated to the reinforcement learning methods used for training LLMs.
Video 0 (Optional): Introduction to deep reinforcement learning youtube.com/watch?v=SgC6AZss478 Video 1 (This one): Proximal Policy Optimization Video 2: Reinforcement Learning with Human Feedback youtube.com/watch?v=Z_JUqJBpVOk Video 3 (Coming soon!): Deterministic Policy Optimization
00:00 Introduction 01:25 Gridworld 03:10 States and Action 04:01 Values 07:30 Policy 09:39 Neural Networks 16:14 Training the value neural network (Gain) 22:50 Training the policy neural network (Surrogate Objective Function) 33:38 Clipping the surrogate objective function 36:49 Summary
Get the Grokking Machine Learning book! manning.com/books/grokking-machine-learning Discount code (40%): serranoyt (Use the discount code on checkout)The Attention Mechanism for Large Language Models #AI #llm #attentionSerrano.Academy2023-12-19 | This is how the attention mechanism works for large language models. For more information, follow me, and check out https://llm.universityStable Diffusion - How to build amazing images with AISerrano.Academy2023-12-12 | This video is about Stable Diffusion, the AI method to build amazing images from a prompt.
If you like this material, check out LLM University from Cohere! https://llm.university
Get the Grokking Machine Learning book! https://manning.com/books/grokking-ma... Discount code (40%): serranoyt (Use the discount code on checkout)
0:00 Introduction 1:27 How does Stable Diffusion work? 2:55 Embeddings 12:55 Diffusion Model 15:00 Numerical Example 17:39 Embedding Example 19:37 Image Generator Example 28:37 The Sigmoid Function 34:39 Diffusion Model Example 41:03 SummaryHow Large Language Models are Shaping the FutureSerrano.Academy2023-11-13 | Livestream with FourthBrain and Cohere fourthbrain.aiWhat are Transformer Models and how do they work?Serrano.Academy2023-11-02 | This is the last of a series of 3 videos where we demystify Transformer models and explain them with visuals and friendly examples.
00:00 Introduction 01:50 What is a transformer? 04:35 Generating one word at a time 08:59 Sentiment Analysis 13:05 Neural Networks 18:18 Tokenization 19:12 Embeddings 25:06 Positional encoding 27:54 Attention 32:29 Softmax 35:48 Architecture of a Transformer 39:00 Fine-tuning 42:20 ConclusionThe math behind Attention: Keys, Queries, and Values matricesSerrano.Academy2023-08-31 | This is the second of a series of 3 videos where we demystify Transformer models and explain them with visuals and friendly examples.
If you like this material, check out LLM University from Cohere! https://llm.university
00:00 Introduction 01:18 Recap: Embeddings and Context 04:46 Similarity 11:09 Attention 20:46 The Keys and Queries Matrices 25:02 The Values Matrix 28:41 Self and Multi-head attention 33:54: ConclusionThe Attention Mechanism in Large Language ModelsSerrano.Academy2023-07-25 | Attention mechanisms are crucial to the huge boom LLMs have recently had. In this video you'll see a friendly pictorial explanation of how attention mechanisms work in Large Language Models. This is the first of a series of three videos on Transformer models.
Learn more in LLM University! https://llm.universityThe Binomial and Poisson DistributionsSerrano.Academy2022-11-08 | If on average, 3 people enter a store every hour, what is the probability that over the next hour, 5 people will enter the store? The answer lies in the Poisson distribution. In this video you'll learn this distribution, starting from a much simpler one, the Binomial distribution.
Grokking Machine Learning book: bit.ly/grokkingML 40% discount code: serranoytEulers number, derivatives, and the bank at the end of the universeSerrano.Academy2022-10-14 | Euler's number, e, is defined as a limit. The function e to the x is (up to multiplying by a constant) the only function that is its own derivative. How are these two related? In this video you'll find an explanation for this phenomenon using banking interest rates, and a very particular bank, located at the end of the universe.Decision trees - A friendly introductionSerrano.Academy2022-09-30 | A video about decision trees, and how to train them on a simple example.
Grokking Machine Learning book: www.manning.com/books/grokking-machine-learning 40% discount code: serranoytThank you for 100K subscribers! I’m planning tons of new content coming soon, so excited!Serrano.Academy2022-09-20 | ...How do you minimize a function when you cant take derivatives? CMA-ES and PSOSerrano.Academy2022-09-18 | What happens when you want to minimize a function, say, the error function in order to train a machine learning model, but the function has no derivatives, or they are very hard to calculate? You can use Gradient-Free optimizers. In this video, I show you two of them: - CMA-ES (Covariance matrix adaptation strategy) - PSO (Particle swarm optimization)
Introduction: (0:00) CMA-ES: (1:23) PSO (9:17) Conclusion: (14:00)What is Quantum Machine Learning?Serrano.Academy2022-09-07 | This video is a friendly introduction to quantum computing and machine learning. No knowledge required.
Video 2: Non-gradient optimizers, CMA-ES and PSO youtube.com/watch?v=oi5GQvJzy5I Video 3: The mathematics behind qubits (coming soon!)
Introduction: (0:00) Quantum and classical machine learning: (1.46) Probability: (5:12) The qubit: (8:23) Quantum measurement: (12:08) Qubits as generative models: (13:42) Measuring with different bases: (14:15) Quantum gates: (22:27) Quantum entanglement: (25:23) Entanglement gates: (35:31) Quantum machine learning (36:04) Training models: (39:50) Loss functions and KL divergence: (47:55) Labs, code, etc: (49:59)Denoising and Variational AutoencodersSerrano.Academy2022-01-15 | A video about autoencoders, a very powerful generative model. The video includes: Intro: (0:25) Dimensionality reduction (3:35) Denoising autoencoders (10:50) Variational autoencoders (18:15) Training autoencoders (23:36)
Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
0:00 Introduction 0:13 Generative models 3:03 Variational autoencoders 3:45 Dataset of images 10:16 Denoising autoencoders 10:27 Linear methods 10:53 A friendly introduction to deep learning and neural networks 11:58 Mapping the real numbers to the interval (0,1) 12:23 Sigmoid function 12:41 Perceptron 15:02 Correct noise 18:20 Autoencoders as generators 20:16 Latent space 23:41 Training a neural network - loss function 25:18 Training an autoencoder 25:32 Training autoencoders 25:46 Reconstruction loss (Mean squared error) 26:31 Reconstruction loss (log-loss) 27:11 Training a variational auto encoder
Correction: At 30:05, the number in the middle of the red graph should be 0.4, not 0.3.Eigenvectors and Generalized EigenspacesSerrano.Academy2021-10-17 | A video about the nice geometric intuitions behind eigenvectors and eigenvalues, and their generalized counterparts, generalized eigenvectors and generalized eigenvalues.
Announcement: Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoytThompson sampling, one armed bandits, and the Beta distributionSerrano.Academy2021-07-06 | Thompson sampling is a strategy to explore a space while exploiting the wins. In this video we see an application to winning at a game of one-armed bandits.
Announcement: Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoytThe Beta distribution in 12 minutes!Serrano.Academy2021-06-13 | This video is about the Beta distribution, a very important distribution in probability, statistics, and machine learning. It is explained using a simple example involving flipping coins.
Machine Learning Testing and Error Metrics https://www.youtube.com/watch?v=aDW44...
Announcement: Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoytA friendly introduction to deep reinforcement learning, Q-networks and policy gradientsSerrano.Academy2021-05-24 | A video about reinforcement learning, Q-networks, and policy gradients, explained in a friendly tone with examples and figures.
Introduction: (0:00) Markov decision processes (MDP): (1:09) Rewards: (5:39) Discount factor: (8:51) Bellman equation: (10:48) Solving the Bellman equation: (12:43) Deterministic vs stochastic processes: (16:29) Neural networks: (19:15) Value neural networks: (21:44) Policy neural networks: (25:44) Training the policy neural network: (30:46) Conclusion: (34:53)
Announcement: Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoytThe Gini Impurity Index explained in 8 minutes!Serrano.Academy2021-02-28 | The Gini Impurity Index is a measure of the diversity in a dataset. In this short video you'll learn a very simple way to calculate it using probabilities.
Announcement: Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoytThe covariance matrixSerrano.Academy2020-12-29 | CORRECTION: At 10:56 we shouldn't divide by 4 to get the covariance, we should divide by 1+1+1+1/3, which is 10/3. That means the covariances are the following: Var(x) = 1.056 Var(y) = 0.864 Cov(x,y) = 0.768 (Thank you Shivkumar Pippal!)
Mean, variance, covariance, and the covariance matrix for a dataset and a weighted dataset.
Announcement: Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
0:00 Introduction 0:09 The covariance matrix 2:22 Average 3:23 X-variance 5:06 Problem: Same variances 7:59 Formulas 10:30 Center pointsGaussian Mixture ModelsSerrano.Academy2020-12-28 | Covariance matrix video: youtu.be/WBlnwvjfMtQ Clustering video: youtu.be/QXOkPvFM6NU
A friendly description of Gaussian mixture models, a very useful soft clustering method.
Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
0:00 Introduction 0:13 Clustering applications 1:56 Hard clustering - soft clustering 3:36 Step 1: Colouring points 6:10 Step 2: Fitting a Gaussian 10:33 Gaussian Mixture Models (GMM)Singular Value Decomposition (SVD) and Image CompressionSerrano.Academy2020-09-08 | Github repo: http://www.github.com/luisguiserrano/singular_value_decomposition
In this video, we learn a very useful matrix trick called singular value decomposition (SVD), in which we express a matrix as a product of two rotation matrices and one scaling matrix. We also show a very interesting application to image compression.
Introduction: (0:00) Transformations: (0:50) A puzzle: (1:27) A harder puzzle: (2:21) Linear transformations: (3:50) Dimensionality reduction: (10:50) Image compression: (23:57)ROC (Receiver Operating Characteristic) Curve in 10 minutes!Serrano.Academy2020-07-15 | The ROC curve is a very effective way to make decisions on your machine learning model based on how important is it to not allow false positives or false negatives. In this video we introduce the ROC curve with a simple example.
Machine Learning Testing and Error Metrics youtube.com/watch?v=aDW44NPhNw0Restricted Boltzmann Machines (RBM) - A friendly introductionSerrano.Academy2020-07-07 | CORRECTION: The score for BE is 6 and for BD is -1.
A simple introduction to Restricted Boltzmann Machines (RBM) and their training process, using a real-life example with people and pets.
Introduction: (0:00) Mystery: (0:17) Scores: (4:39) Probabilities: (7:30) Training (11:09) Contrastive Divergence: (13:37) Small Problem: (15:33) Gibbs Sampling: (16:33) Updating Weights: (20:56) Sampling Problems: (22:58) Independent Sampling: (24:27) Picking Random Samples with Conditions: (28:30) Picking Completely Random Samples: (31:05) Summary: (35:03) Conclusion: (35:57)A Friendly Introduction to Generative Adversarial Networks (GANs)Serrano.Academy2020-05-05 | Code: http://www.github.com/luisguiserrano/gans What is the simplest pair of GANs one can build? In this video (with code included) we build a pair of ONE-layer GANs which will generate some simple 2x2 images (faces).
Live stream celebrating 50K subscribers and 3M views. Friday, March 27, 11am ESTTraining Latent Dirichlet Allocation: Gibbs Sampling (Part 2 of 2)Serrano.Academy2020-03-22 | This is the second of a series of two videos on Latent Dirichlet Allocation (LDA), a powerful technique to sort documents into topics. In this video, we learn to train an LDA model using Gibbs sampling. The first video is here: youtube.com/watch?v=T05t-SqKArYLatent Dirichlet Allocation (Part 1 of 2)Serrano.Academy2020-03-19 | Latent Dirichlet Allocation is a powerful machine learning technique used to sort documents by topic. Learn all about it in this video!
0:00 Introduction 0:11 The problem 3:47 Machine that generates documents 5:13 Blueprint for the LDA machine 5:49 Probability of a document 9:22 Quiz: Which one for topics? 11:06 A distribution of distributions 11:30 More topics? More dimensions 12:20 In More dimensions 12:55 Quiz: Where to put the topics? 14:05 Two Dirichlet distributions 14:22 Latent Dirichlet Allocation 22:05 Best settings on the machine 22:37 The winning arrangements 25:28 Series of two videos 25:48 Acknowledgements 26:30 Thank you!Book by Luis Serrano - Grokking Machine Learning (40% off promo code)Serrano.Academy2019-07-31 | You can find the book here: manning.com/books/grokking-machine-learning 40% discount promo code: serranoyt Grokking Machine Learning will give you the tools to learn and apply machine learning, with easy to follow examples and exercises. Check it out!Serrano.Academy - The art of understandingSerrano.Academy2019-02-24 | Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% Discount code: serranoyt
Welcome! I believe that math concepts can be learned through simple explanations, analogies and easy-to-understand visualizations. I am passionate about teaching math concepts in relatable, friendly and simple ways. My videos are designed so that beginners can clearly learn new concepts while experts can see them under a new light. I hope you enjoy the channel and please drop me a line if you have any comments or suggestions. Twitter: @luis_likes_math.Naive Bayes classifier: A friendly approachSerrano.Academy2019-02-11 | Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
A visual description of Bayes' Theorem and the Naive Bayes algorithm, and an application to spam detection. No previous knowledge is needed, aside from knowing how to multiply and divide, a visual mind and a desire to learn.
0:00 Introduction 0:39 Spam Detector 4:59 Problem 10:34 Naive Bayes Classifier 17:00 Bayes TheoremMath and OCD - My story with the Thue-Morse sequenceSerrano.Academy2019-02-10 | Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
The story of how I once stumbled upon a famous sequence, the Thue-Morse sequence, while walking on the sidewalks as a child,, and the subsequent tale of how this sequence satisfies amazing properties regarding sums of powers of integers.Principal Component Analysis (PCA)Serrano.Academy2019-02-10 | Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
A conceptual description of principal component analysis, including: - variance and covariance - eigenvectors and eigenvalues - applications As usual, very little formulas, lots and lots of pictures!
0:00 Introduction 0:46 Taking a picture 1:13 Dimensionality Reduction 2:02 Housing Data 5:09 Mean 7:46 Variance? 12:47 Covariance matrix 13:58 Linear Transformations 18:12 Eigenstuff 19:16 Eigenvalues 19:53 Eigenvectors 20:51 Principal Component Analysis (PCA) 26:05 Thank you!Support Vector Machines (SVMs): A friendly introductionSerrano.Academy2019-01-27 | For a code implementation, check out this repo: github.com/luisguiserrano/manning/tree/master/Chapter_11_Support_Vector_Machines
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: youtube.com/watch?v=wYPUhge9w5c - Logistic Regression: 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!Logistic Regression and the Perceptron Algorithm: A friendly introductionSerrano.Academy2019-01-01 | For a code implementation, check out these repos: github.com/luisguiserrano/manning/tree/master/Chapter_5_Perceptron_Algorithm github.com/luisguiserrano/manning/tree/master/Chapter_6_Logistic_Regression
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!Linear Regression: A friendly introductionSerrano.Academy2018-12-23 | For a code implementation, check out this repo: github.com/luisguiserrano/manning/tree/master/Chapter_3_Linear_Regression
Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
An introduction to linear regression that requires very little math (no calculus or linear algebra), only a visual mind.
0:00 Introduction 0:44 Housing Prices 6:08 Changing the slope - Rotation 6:34 Changing the y-intercept - Translation 7:01 How to move a line 9:16 Moving a line 11:18 Linear regression algorithm 14:27 Positive and negative distance 19:34 One rule to rule them all 26:35 Square error 29:02 Absolute error 29:52 Absolute trick 30:26 Series of 3 videosHow does Netflix recommend movies? Matrix FactorizationSerrano.Academy2018-09-08 | Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
What is Machine Learning: (0:05) How do recommendations work - Netflix example (0:40) How to figure out dependencies - Matrix Factorization (7:35) Matrix Factorization Benefits 20:38 How to find the right factorization (16:03) Error Function for factorization (26:35) How to use the factors to predict ratings - Inference (30:14)
(Thanks for the timings, Samarth Piano Posts!)Deep Neural Networks - USF Data Science Seminar by Luis SerranoSerrano.Academy2018-04-17 | Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
A Friendly Introduction to Neural Networks Thanks to the University of San Francisco Data Science Seminar Series for the video, and to Professor Yannet Interian for the invitation.
⭐ Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates with all the top editors and IDEs to give you smart completions and documentation while you’re typing. I've been using Kite for 6 months and I love it! kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=luisserrano&utm_content=description-onlyA friendly introduction to Bayes Theorem and Hidden Markov ModelsSerrano.Academy2018-03-27 | Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
A friendly introduction to Bayes Theorem and Hidden Markov Models, with simple examples. No background knowledge needed, except basic probability. Accompanying notebook: github.com/luisguiserrano/hmmShannon Entropy and Information GainSerrano.Academy2017-11-04 | Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
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 FormulaA friendly introduction to Recurrent Neural NetworksSerrano.Academy2017-08-18 | Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
A friendly explanation of how computers predict and generate sequences, based on Recurrent Neural Networks. For a brush up on Neural Networks, check out this video: youtube.com/watch?v=BR9h47Jtqyw
0:00 A friendly introduction to Recurrent Neural Networks 1:38 A friendly introduction to Deep Learning and Neural Networks 2:11 Vectors 5:22 Perfect Roommate 7:13 Simple Neural Network 7:54 Simple (Recurrent) Neural Network 10:03 Cooking Schedule 11:47 More Complicated RNN 12:06 Food 13:31 Weather 14:38 Add 16:02 Merge 20:53 Start with random weights 21:05 Use Gradient Descent 21:41 New Error FunctionA friendly introduction to Convolutional Neural Networks and Image RecognitionSerrano.Academy2017-03-21 | Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
A friendly explanation of how computer recognize images, based on Convolutional Neural Networks. All the math required is knowing how to add and subtract 1's. (Bonus if you know calculus, but not needed.) For a brush up on Neural Networks, check out this video: youtube.com/watch?v=BR9h47Jtqyw
0:00 Introduction 0:22 Simple World 1:05 Keyboard 1:33 Image recognition software 4:39 Image Recognition Classifier 6:12 Artificial Intelligence 8:47 Gradient Descent 10:26 Slightly More Complex World 11:47 Previous Knowledge 24:27 Convolutional Neural Network 28:27 Advanced WorldMachine Learning: Testing and Error MetricsSerrano.Academy2017-03-16 | Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
A friendly journey into the process of evaluating and improving machine learning models. - Training, Testing - Evaluation Metrics: Accuracy, Precision, Recall, F1 Score - Types of Errors: Overfitting and Underfitting - Cross Validation and K-fold Cross Validation - Model Evaluation Graphs - Grid Search
0:00 Introduction 0:37 Which model is better 1:31 Why Testing? 3:27 Golden Rule # 1 4:21 How do we not 'lose' the training data? 4:38 K-Fold Cross Validation 5:20 Randomizing in Cross Validation 5:38 Evaluation Metrics 7:53 Medical Model 8:05 Spam Classifier Model 9:25 Confusion Matrix Diagnosis 11:50 Accuracy 19:47 Precision and Recall 20:54 Credit Card Fraud 22:36 Harmonic mean 24:08 F1 Score 27:16 Types of Errors 27:56 Classification 30:03 Error due to variance (overfitting) 30:18 Error due to bias (underfitting) 31:45 Tradeoff 37:55 Solution: Cross Validation Testing 39:16 Training a Logistic Regression Model 40:04 Training a Decision Tree 40:49 Training a Support Vector Machine 41:14 Grid Search Cross Validation 41:59 Parameters and Hyperparameters 42:56 How to solve a problem 43:20 How to use machine learning 44:04 Thank you!A friendly introduction to Deep Learning and Neural NetworksSerrano.Academy2016-12-27 | A friendly introduction to neural networks and deep learning.
Note: In this tutorial I use natural logarithms. If you used logarithms base 10, you may get different answers that I got, although at the end it doesn't matter, since using a different base for the logarithm just scales all the logarithms by a constant.
Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML 40% discount code: serranoyt
00:00 What is machine learning? 2:22 Gradient descent 5:07 Neural network 10:11 logistic regression 12:28 Probability 14:57 Activation Function 19:56 Error function 22:34 Node(Neuron) 24:07 Non-linear regions 31:22 Deep neural networkA Friendly Introduction to Machine LearningSerrano.Academy2016-09-09 | Grokking Machine Learning Book: manning.com/books/grokking-machine-learning 40% discount promo code: serranoyt
A friendly introduction to the main algorithms of Machine Learning with examples. No previous knowledge required.
What is Machine Learning: (0:05) Linear Regression: (2:25) Gradient Descent: (4:10) Naive Bayes: (6:20) Decision Trees: (10:35) Logistic Regression: (13:20) Neural networks: (17:00) Support Vector Machines: (18:50) Kernel trick: (20:05) K-Means clustering: (26:00) Hierarchical Clustering: (28:30) Summary: (29:40)
(Thanks to Nick Kartha for breaking down the topics!)