Vizuara Books
Machine Learning & Deep Learning Mastery
ML and deep learning, built by hand from scratch.
A ground-up tour of machine learning and deep learning, from ordinary-least-squares regression to a neural network you code and backpropagate yourself. Every algorithm is derived on paper, implemented in Python, and illustrated with hand-drawn figures.
Read on your Kindle
We'll send this whole book straight to your Kindle — it opens natively, so you can resize the text, read fully offline, and it remembers where you left off. Nothing to download or manage.
Sending to Kindle is for subscribers — subscribe to read the whole library on your Kindle.
00Foundations of Machine Learning4 capsules
Foundations of Machine Learning — 4 chapters.
01What Is Machine Learning?conceptfree12 min02AI vs ML vs Deep Learningintuition🔒13 min03Types of Machine Learningconcept🔒15 min04Supervised vs Unsupervised, Explained Simplyintuition🔒12 min01Regression5 capsules
Regression — 5 chapters.
05Regression Theory and the Line of Best Fitconcept🔒15 min06The OLS Formula, Derivedmath🔒14 min07Ridge Regression Theorymath🔒16 min08Regression in Codecode🔒13 min09Ridge Regression: A Hands-On Democode🔒13 min02Overfitting, Features & Generalization4 capsules
Overfitting, Features & Generalization — 4 chapters.
10The Overfitting Problemconcept🔒13 min11K-Fold Cross-Validationconcept🔒14 min12Feature Engineering Basicsconcept🔒12 min13A Regression Case Studyproject🔒13 min03Linear Classifiers & the Perceptron6 capsules
Linear Classifiers & the Perceptron — 6 chapters.
14Introduction to Linear Classifiersconcept🔒13 min15The Perceptron: Theory and Codecode🔒13 min16Linear Separability and Marginmath🔒12 min17The Perceptron Convergence Theoremdeep-dive🔒12 min18Feature Encoding Theoryconcept🔒14 min19The Perceptron in Codecode🔒12 min04Logistic Regression4 capsules
Logistic Regression — 4 chapters.
20Logistic Regression Fundamentalsconcept🔒13 min21The Sigmoid and Cross-Entropy Lossmath🔒14 min22Logistic Regression, End to Endcode🔒15 min23Logistic Regression in Codecode🔒16 min05Neural Networks: The Forward Pass5 capsules
Neural Networks: The Forward Pass — 5 chapters.
24Neurons, Layers, and Batchesconcept🔒14 min25Activation Functionsconcept🔒14 min26Building Layers with Python Classescode🔒12 min27Loss Functions and the Full Forward Passmath🔒13 min28The Forward Pass in Codecode🔒14 min06Neural Networks: Backpropagation5 capsules
Neural Networks: Backpropagation — 5 chapters.
29The Backward Pass Through a Single Neuronmath🔒13 min30The Backward Pass Through a Layermath🔒14 min31Backpropagation: The Complete Theorydeep-dive🔒15 min32Backpropagation Recap and Codecode🔒15 min33The Entire Backward Pass in Codeproject🔒14 min07Optimizers & Training5 capsules
Optimizers & Training — 5 chapters.
34Gradient Descent and Momentummath🔒13 min35AdaGrad, RMSProp, and Adammath🔒14 min36Optimizers in Codecode🔒14 min37Regularization and Dropout in Neural Netsconcept🔒13 min38Project: Build Your Own Neural Networkproject🔒12 min08CNNs & Decision Trees7 capsules
CNNs & Decision Trees — 6 chapters.
39CNN Fundamentalsconcept🔒14 min40Build Your Own CNN Applicationproject🔒13 min41Introduction to Decision Treesconcept🔒12 min42Let Us Start Building a Decision Treeconcept🔒11 min43Gini Impurity: Choosing the Best Splitmath🔒12 min44Splitting on Numerical Datamath🔒13 min45Project: Code a Decision Tree from Scratchproject🔒13 minRatings & reviews
No ratings yet. Yours would be the first.