Serrano.Academy
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.
updated 1 year ago
This is the first one in a series of 3 videos dedicated to the reinforcement learning methods used for training LLMs.
Full Playlist: youtube.com/playlist?list=PLs8w1Cdi-zvYviYYw_V3qe6SINReGF5M-
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
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This is the first one in a series of 3 videos dedicated to the reinforcement learning methods used for training LLMs.
Full Playlist: youtube.com/playlist?list=PLs8w1Cdi-zvYviYYw_V3qe6SINReGF5M-
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
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For more information, follow me, and check out https://llm.university
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...
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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 Summary
fourthbrain.ai
Video 1: The attention mechanism in high level youtube.com/watch?v=OxCpWwDCDFQ
Video 2: The attention mechanism with math youtube.com/watch?v=UPtG_38Oq8o
Video 3 (This one): Transformer models
If you like this material, check out LLM University from Cohere!
https://llm.university
Get the Grokking Machine Learning book!
manning.com/books/grokking-machine-learning
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(Use the discount code on checkout)
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 Conclusion
Video 1: The attention mechanism in high level youtu.be/OxCpWwDCDFQ
Video 2: The attention mechanism with math (this one)
Video 3: Transformer models youtube.com/watch?v=qaWMOYf4ri8
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: Conclusion
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.
Video 1: The attention mechanism in high level (this one)
Video 2: The attention mechanism with math: youtube.com/watch?v=UPtG_38Oq8o
Video 3: Transformer models youtube.com/watch?v=qaWMOYf4ri8
Learn more in LLM University! https://llm.university
Euler number video: youtube.com/watch?v=oikl9FCISqU
Grokking Machine Learning book:
bit.ly/grokkingML
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Accompanying blog post: medium.com/@luis.serrano/splitting-data-by-asking-questions-decision-trees-74afed9cd849
For a code implementation, check out this repo:
github.com/luisguiserrano/manning/tree/master/Chapter_9_Decision_Trees
Helper videos:
- Gini index: youtube.com/watch?v=u4IxOk2ijSs
- Entropy and information gain: youtube.com/watch?v=9r7FIXEAGvs
- Machine learning error and metrics: youtube.com/watch?v=aDW44NPhNw0
Grokking Machine Learning book:
www.manning.com/books/grokking-machine-learning
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- CMA-ES (Covariance matrix adaptation strategy)
- PSO (Particle swarm optimization)
This video is a sequel to "What is Quantum Machine Learning"
youtube.com/watch?v=j0DV_75LkFo
and also part of the blog post:
zapatacomputing.com/why-generative-modeling-is-leading-the-race-to-practical-quantum-advantage
Introduction: (0:00)
CMA-ES: (1:23)
PSO (9:17)
Conclusion: (14:00)
This video is part of a blog post with Zapata computing:
zapatacomputing.com/why-generative-modeling-is-leading-the-race-to-practical-quantum-advantage
QCBM Paper: nature.com/articles/s41534-019-0157-8
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)
Intro: (0:25)
Dimensionality reduction (3:35)
Denoising autoencoders (10:50)
Variational autoencoders (18:15)
Training autoencoders (23:36)
Github repo: www.github.com/luisguiserrano/autoencoders
Recommended videos:
Generative adversarial networks: youtube.com/watch?v=8L11aMN5KY8
Restricted Boltzmann machines: youtube.com/watch?v=Fkw0_aAtwIw
Matrix factorization: youtube.com/watch?v=ZspR5PZemcs
Singular value decomposition: youtube.com/watch?v=DG7YTlGnCEo
Neural networks: youtube.com/watch?v=BR9h47Jtqyw
Convolutional neural networks: youtube.com/watch?v=2-Ol7ZB0MmU
Recurrent neural networks: youtube.com/watch?v=2-Ol7ZB0MmU
Logistic regression: youtube.com/watch?v=jbluHIgBmBo
Shannon entropy: youtube.com/watch?v=9r7FIXEAGvs
Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
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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.
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Beta distributions video: youtube.com/watch?v=juF3r12nM5A
Tom Denton blog: inventingsituations.net
Icons made by Freepik from flaticon.com
Announcement: Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
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One armed bandits video: youtube.com/watch?v=nkyDGGQ5h60
Useful links:
Bayes theorem video: youtube.com/watch?v=Q8l0Vip5YUw
Beta distribution (3blue1brown)
youtube.com/watch?v=ZA4JkHKZM50
http://youtube.com/watch?v=8idr1WZ1A7Q
Grokking Machine Learning Book: manning.com/books/grokking-machine-learning
40% discount promo code: serranoyt
Machine Learning Testing and Error Metrics
https://www.youtube.com/watch?v=aDW44...
Announcement: Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
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Introduction to neural networks: youtube.com/watch?v=BR9h47Jtqyw
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
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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
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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 points
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
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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)
Grokking Machine Learning Book:
manning.com/books/grokking-machine-learning
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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.
Similar videos:
Principal component analysis (PCA): youtube.com/watch?v=g-Hb26agBFg
Matrix factorization and Netflix recommendations: youtube.com/watch?v=ZspR5PZemcs
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)
Grokking Machine Learning Book: manning.com/books/grokking-machine-learning
40% discount promo code: serranoyt
Machine Learning Testing and Error Metrics
youtube.com/watch?v=aDW44NPhNw0
A simple introduction to Restricted Boltzmann Machines (RBM) and their training process, using a real-life example with people and pets.
Grokking Machine Learning Book: manning.com/books/grokking-machine-learning
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Images downloaded from www.freepik.com
freepik.com/free-photos-vectors/banner
freepik.com/free-photos-vectors/dog
freepik.com/free-photos-vectors/people
freepik.com/free-photos-vectors/background
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)
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).
Grokking Machine Learning Book: manning.com/books/grokking-machine-learning
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GANs from Scratch 1: A deep introduction. With code in PyTorch and TensorFlow: medium.com/ai-society/gans-from-scratch-1-a-deep-introduction-with-code-in-pytorch-and-tensorflow-cb03cdcdba0f
For information on my book "Grokking Machine Learning":
manning.com/books/grokking-machine-learning
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Live stream celebrating 50K subscribers and 3M views.
Friday, March 27, 11am EST
The first video is here: youtube.com/watch?v=T05t-SqKArY
This is part 1 of a 2 video series.
Video 2: youtube.com/watch?v=BaM1uiCpj_E
For information on my book "Grokking Machine Learning": manning.com/books/grokking-machine-learning
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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!
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Grokking Machine Learning will give you the tools to learn and apply machine learning, with easy to follow examples and exercises. Check it out!
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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.
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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.
For a code implementation, check out this repo:
github.com/luisguiserrano/manning/tree/master/Chapter_8_Naive_Bayes
0:00 Introduction
0:39 Spam Detector
4:59 Problem
10:34 Naive Bayes Classifier
17:00 Bayes Theorem
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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.
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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!
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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
github.com/luisguiserrano/manning/tree/master/Chapter_11_Support_Vector_Machines
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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!
github.com/luisguiserrano/manning/tree/master/Chapter_5_Perceptron_Algorithm
github.com/luisguiserrano/manning/tree/master/Chapter_6_Logistic_Regression
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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!
github.com/luisguiserrano/manning/tree/master/Chapter_3_Linear_Regression
Announcement: New Book by Luis Serrano! Grokking Machine Learning. bit.ly/grokkingML
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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 videos
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A friendly introduction to recommender systems with matrix factorization and how it's used to recommend movies in Netflix.
Accompanying Notebook by Yannet Interian: github.com/yanneta/pytorch-tutorials/blob/master/collaborative-filtering-nn.ipynb
Gradient descent video mentioned at the end: youtube.com/watch?v=BR9h47Jtqyw
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!)
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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.
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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/hmm
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Accompanying blog post: medium.com/p/5810d35d54b4
0:00 Shannon Entropy and Information Gain
2:22 What ball will we pick?
4:33 Quiz
5:06 Question
5:14 Game
7:17 Probability of Winning
7:45 Products
11:00 What if there are more classes?
12:34 Sequence 2
13:44 Sequence 3
14:57 Naive Approach
15:34 Sequence 1
19:44 General Formula
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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 Function
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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
For a code implementation, check out this repo:
github.com/luisguiserrano/manning/tree/master/Chapter_10_Neural_Networks
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 World
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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
For a code implementation, check out this repo:
github.com/luisguiserrano/manning/tree/master/Chapter_4_Testing_Overfitting_Underfitting
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!
For a code implementation, check out this repo
github.com/luisguiserrano/manning/tree/master/Chapter_10_Neural_Networks
This is a follow up to the Introduction to Machine Learning video.
youtube.com/watch?v=IpGxLWOIZy4
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
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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 network
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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!)
If you like this, there's an extended version in this playlist:
youtube.com/playlist?list=PLAwxTw4SYaPknYBrOQx6UCyq67kprqXe3
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An interesting application of Bayes' Theorem.
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A fun introduction to geometric series.
⭐ 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-only