English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 6h 21m | 1.11 GB
Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance.
Fully understanding how machine learning algorithms function is essential for any serious ML engineer. In Machine Learning Algorithms in Depth you’ll explore practical implementations of dozens of ML algorithms including:
- Monte Carlo Stock Price Simulation
- Image Denoising using Mean-Field Variational Inference
- EM algorithm for Hidden Markov Models
- Imbalanced Learning, Active Learning and Ensemble Learning
- Bayesian Optimization for Hyperparameter Tuning
- Dirichlet Process K-Means for Clustering Applications
- Stock Clusters based on Inverse Covariance Estimation
- Energy Minimization using Simulated Annealing
- Image Search based on ResNet Convolutional Neural Network
- Anomaly Detection in Time-Series using Variational Autoencoders
Machine Learning Algorithms in Depth dives into the design and underlying principles of some of the most exciting machine learning (ML) algorithms in the world today. With a particular emphasis on probabilistic algorithms, you’ll learn the fundamentals of Bayesian inference and deep learning. You’ll also explore the core data structures and algorithmic paradigms for machine learning. Each algorithm is fully explored with both math and practical implementations so you can see how they work and how they’re put into action.
Learn how machine learning algorithms work from the ground up so you can effectively troubleshoot your models and improve their performance. This book guides you from the core mathematical foundations of the most important ML algorithms to their Python implementations, with a particular focus on probability-based methods.
Machine Learning Algorithms in Depth dissects and explains dozens of algorithms across a variety of applications, including finance, computer vision, and NLP. Each algorithm is mathematically derived, followed by its hands-on Python implementation along with insightful code annotations and informative graphics. You’ll especially appreciate author Vadim Smolyakov’s clear interpretations of Bayesian algorithms for Monte Carlo and Markov models.
What’s Inside
- Monte Carlo stock price simulation
- EM algorithm for hidden Markov models
- Imbalanced learning, active learning, and ensemble learning
- Bayesian optimization for hyperparameter tuning
- Anomaly detection in time-series
Table of Contents
1 Part 1. Introducing ML algorithms
2 Chapter 1. Machine learning algorithms
3 Chapter 1. Why learn algorithms from scratch
4 Chapter 1. Mathematical background
5 Chapter 1. Bayesian inference and deep learning
6 Chapter 1. Implementing algorithms
7 Chapter 1. Summary
8 Chapter 2. Markov chain Monte Carlo
9 Chapter 2. Estimating pi
10 Chapter 2. Binomial tree model
11 Chapter 2. Self-avoiding random walk
12 Chapter 2. Gibbs sampling
13 Chapter 2. Metropolis-Hastings sampling
14 Chapter 2. Importance sampling
15 Chapter 2. Exercises
16 Chapter 2. Summary
17 Chapter 3. Variational inference
18 Chapter 3. Mean-field approximation
19 Chapter 3. Image denoising in an Ising model
20 Chapter 3. MI maximization
21 Chapter 3. Exercises
22 Chapter 3. Summary
23 Chapter 4. Software implementation
24 Chapter 4. Problem-solving paradigms
25 Chapter 4. ML research Sampling methods and variational inference
26 Chapter 4. Exercises
27 Chapter 4. Summary
28 Part 2. Supervised learning
29 Chapter 5. Classification algorithms
30 Chapter 5. Perceptron
31 Chapter 5. Support vector machine
32 Chapter 5. Logistic regression
33 Chapter 5. Naive Bayes
34 Chapter 5. Decision tree (CART)
35 Chapter 5. Exercises
36 Chapter 5. Summary
37 Chapter 6. Regression algorithms
38 Chapter 6. Bayesian linear regression
39 Chapter 6. Bayesian linear regression
40 Chapter 6. KNN regression
41 Chapter 6. Gaussian process regression
42 Chapter 6. Exercises
43 Chapter 6. Summary
44 Chapter 7. Selected supervised learning algorithms
45 Chapter 7. Imbalanced learning
46 Chapter 7. Active learning
47 Chapter 7. Model selection Hyperparameter tuning
48 Chapter 7. Ensemble methods
49 Chapter 7. ML research Supervised learning algorithms
50 Chapter 7. Exercises
51 Chapter 7. Summary
52 Part 3. Unsupervised learning
53 Chapter 8. Fundamental unsupervised learning algorithms
54 Chapter 8. Gaussian mixture models
55 Chapter 8. Dimensionality reduction
56 Chapter 8. Exercises
57 Chapter 8. Summary
58 Chapter 9. Selected unsupervised learning algorithms
59 Chapter 9. Density estimators
60 Chapter 9. Structure learning
61 Chapter 9. Simulated annealing
62 Chapter 9. Genetic algorithm
63 Chapter 9. ML research Unsupervised learning
64 Chapter 9. Exercises
65 Chapter 9. Summary
66 Part 4. Deep learning
67 Chapter 10. Fundamental deep learning algorithms
68 Chapter 10. Convolutional neural nets
69 Chapter 10. Recurrent neural nets
70 Chapter 10. Neural network optimizers
71 Chapter 10. Exercises
72 Chapter 10. Summary
73 Chapter 11. Advanced deep learning algorithms
74 Chapter 11. Amortized variational inference
75 Chapter 11. Attention and transformers
76 Chapter 11. Graph neural networks
77 Chapter 11. ML research Deep learning
78 Chapter 11. Exercises
79 Chapter 11. Summary
80 appendix B. Answers to exercises
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