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Deep Learning Mathematics Roadmap

This roadmap outlines the mathematical concepts and topics covered in various deep learning resources. It provides a structured path to understand the necessary mathematical foundations for deep learning.

Table of Contents

  1. Linear Algebra
  2. Probability and Statistics
  3. Calculus
  4. Numerical Computation
  5. Machine Learning Fundamentals
  6. Deep Learning Basics
  7. Advanced Deep Learning Topics

1. Linear Algebra

- Vectors and Matrices: Vector operations, matrix operations, dot product, matrix-vector multiplication.

  1. Vectors and Matrices by Math Is Fun

  2. Vectors and Matrices Crash Course by BetterExplained

  3. Vectors and Matrices in MATLAB

  4. Introduction to Vectors and Matrices by Purplemath

  5. Vector and Matrix Basics by MathBootCamps (YouTube video)

  6. Linear Algebra: Vectors and Matrices by MIT OpenCourseWare

  7. Linear Algebra: Foundations to Frontiers by edX

  8. Linear Algebra Refresher Course by Khan Academy

  9. Introduction to Linear Algebra by Gilbert Strang (Textbook)

  10. Linear Algebra Done Right by Sheldon Axler (Textbook)

  11. 3Blue1Brown: Essence of Linear Algebra (YouTube series)

- Matrix Operations: Transpose, trace, determinant, matrix inverse, matrix rank.

  1. Matrix Operations: Addition, Subtraction, Scalar Multiplication by Math Easy Solutions

  2. Matrix Operations: Multiplication, Transpose, and Determinant by Math CUE math

  3. Matrix Operations by Purplemath

  4. Linear Algebra Toolkit: Matrix Operations by MathsIsPower4U

  5. Matrix Operations by MathOnlineSchool

  6. Matrix Operations by Khan Academy

  7. Matrix Operations by MathIsPower4U

  8. Linear Algebra: Matrix Operations by MathDoctorBob

  9. Matrix Operations by Krista King Math

  10. Matrix Operations by MathPortal

- Linear Independence and Rank: Linearly independent vectors, rank of a matrix.

  1. Linear Independence and Span by Khan Academy

  2. Rank of a Matrix by Math Is Fun

  3. Linear Independence and Rank by MIT OpenCourseWare

  4. Linear Independence and Rank by MathDoctorBob

  5. Linear Algebra: Introduction to Linear Independence and Span by MathBootCamps

  6. Linear Independence and Rank by MathOnlineSchool

  7. Linear Independence and Rank by Krista King Math

  8. Linear Algebra: Basis, Linear Independence, and Rank by MathTheBeautiful

  9. Linear Independence and Rank by MathPortal

- Matrix Inverse and Pseudoinverse: Inverse matrix, pseudoinverse.

  1. Matrix Inverse by Khan Academy

  2. Pseudoinverse by Math Is Fun

  3. Matrix Inverse and Pseudoinverse by MIT OpenCourseWare

  4. Matrix Inverse and Pseudoinverse by MathDoctorBob

  5. Matrix Inverse and Pseudoinverse by MathTheBeautiful

  6. Matrix Inversion and Pseudoinverse by MathOnlineSchool

  7. Pseudoinverse by Krista King Math

  8. Matrix Inverse and Pseudoinverse by MathPortal

- Eigendecomposition and Diagonalization: Eigenvalues, eigenvectors, eigendecomposition, diagonalization.

  1. Eigenvectors and Eigenvalues by Khan Academy

  2. Eigendecomposition by Math Is Fun

  3. Eigendecomposition and Diagonalization by MIT OpenCourseWare

  4. Eigenvectors, Eigenvalues, and Diagonalization by MathDoctorBob

  5. Eigenvalues and Eigenvectors by 3Blue1Brown

  6. Eigendecomposition and Diagonalization by MathTheBeautiful

  7. Eigenvectors and Eigenvalues by MathOnlineSchool

  8. Eigendecomposition and Diagonalization by Krista King Math

  9. Eigendecomposition and Diagonalization by MathPortal

- Singular Value Decomposition (SVD): SVD theorem, SVD computation, low-rank approximation.

  1. Singular Value Decomposition (SVD) by Khan Academy

  2. Singular Value Decomposition (SVD) by Math Is Fun

  3. Singular Value Decomposition (SVD) by MIT OpenCourseWare

  4. Singular Value Decomposition (SVD) by MathDoctorBob

  5. Singular Value Decomposition (SVD) by MathTheBeautiful

  6. Singular Value Decomposition (SVD) by MathOnlineSchool

  7. Singular Value Decomposition (SVD) by Krista King Math

  8. Singular Value Decomposition (SVD) by MathPortal

2. Probability and Statistics

- Probability Basics: Sample space, events, probability axioms, conditional probability, Bayes' rule.

s

  1. Probability Basics by Khan Academy

  2. Introduction to Probability by MIT OpenCourseWare

  3. Probability Basics by Math Is Fun

  4. Probability Basics by Stat Trek

  5. Probability Basics by CrashCourse

  6. Introduction to Probability by Khan Academy

  7. Probability Fundamentals by Udacity

  8. Introduction to Probability and Data by Duke University (Coursera)

  9. Probability Basics by Statistics How To

  10. Probability Basics by Math Goodies

- Random Variables and Probability Distributions: Discrete and continuous random variables, probability mass function (PMF), probability density function (PDF).

  1. Random Variables and Probability Distributions by Khan Academy

  2. Random Variables and Probability Distributions by MIT OpenCourseWare

  3. Random Variables and Probability Distributions by Stat Trek

  4. Random Variables and Probability Distributions by Math Is Fun

  5. Random Variables and Probability Distributions by CrashCourse

  6. Random Variables and Probability Distributions by Khan Academy

  7. Introduction to Random Variables and Probability Distributions by Rice University (Coursera)

  8. Random Variables and Probability Distributions by CliffsNotes

  9. Introduction to Random Variables and Probability Distributions by Study.com

  10. Random Variables and Probability Distributions by Math Goodies

- Expectation, Variance, and Covariance: Expected value, variance, covariance, correlation coefficient.

Learning Resources: Expectation, Variance, and Covariance

  1. Expectation, Variance, and Covariance by Khan Academy

  2. Expectation, Variance, and Covariance by Math Is Fun

  3. Expectation, Variance, and Covariance by Stat Trek

  4. Expectation, Variance, and Covariance by MIT OpenCourseWare

  5. Expectation, Variance, and Covariance by CrashCourse

  6. Expectation, Variance, and Covariance by MathDoctorBob

  7. Expectation, Variance, and Covariance by Krista King Math

  8. Expectation, Variance, and Covariance by Math Goodies

- Common Probability Distributions: Uniform, Bernoulli, Binomial, Gaussian (Normal), Exponential, Poisson.

Learning Resources: Common Probability Distributions

  1. Common Probability Distributions by Khan Academy

  2. Common Probability Distributions by Stat Trek

  3. Common Probability Distributions by Math Is Fun

  4. Common Probability Distributions by CrashCourse

  5. Common Probability Distributions by Rice University (Coursera)

  6. Common Probability Distributions by MIT OpenCourseWare

  7. Probability Distributions by Krista King Math

  8. Introduction to Probability Distributions by Study.com

  9. Common Probability Distributions by Math Goodies

    - Bayes' Rule and Conditional Probability: Bayes' theorem, prior probability, posterior probability.

    Learning Resources: Bayes' Rule and Conditional Probability

  10. Bayes' Rule and Conditional Probability by Khan Academy

  11. Bayes' Rule and Conditional Probability by Math Is Fun

  12. Bayes' Rule and Conditional Probability by CrashCourse

  13. Bayes' Rule and Conditional Probability by Stat Trek

  14. Bayes' Rule and Conditional Probability by MIT OpenCourseWare

  15. Bayes' Rule and Conditional Probability by MathDoctorBob

  16. Conditional Probability and Bayes' Rule by Krista King Math

  17. Bayes' Rule and Conditional Probability by Math Goodies

  18. Bayes' Theorem by Better Explained

  19. Bayes' Rule and Conditional Probability by Study.com

- Information Theory: Entropy, cross-entropy, Kullback-Leibler (KL) divergence.

Learning Resources: Information Theory

  1. Information Theory by Khan Academy

  2. Information Theory by MIT OpenCourseWare

  3. Information Theory by Math Is Fun

  4. Information Theory by CrashCourse

  5. Information Theory by Stanford University (Coursera)

  6. Information Theory by Krista King Math

  7. An Introduction to Information Theory by John Watrous

  8. Information Theory by All About Circuits

  9. Information Theory by Math Goodies

3. Calculus

- Differential Calculus: Derivatives, chain rule, partial derivatives.

Learning Resources: Differential Calculus

  1. Differential Calculus by Khan Academy

  2. Differential Calculus by MIT OpenCourseWare

  3. Differential Calculus by Math Is Fun

  4. Differential Calculus by CrashCourse

  5. Calculus 1: Differentiation by The Essence of Mathematics

  6. Differential Calculus by Krista King Math

  7. Differential Calculus by MathDoctorBob

  8. Calculus I: Differentiation by UCI Open

  9. Differential Calculus by Math Goodies

- Integral Calculus: Integrals, definite and indefinite integrals, multivariable calculus, gradients.

Learning Resources: Integral Calculus

  1. Integral Calculus by Khan Academy

  2. Integral Calculus by MIT OpenCourseWare

  3. Integral Calculus by Math Is Fun

  4. Integral Calculus by CrashCourse

  5. Calculus 2: Integration by The Essence of Mathematics

  6. Integral Calculus by Krista King Math

  7. Integral Calculus by MathDoctorBob

  8. Calculus II: Integration by UCI Open

  9. Integral Calculus by Math Goodies

- Optimization Techniques: Gradient descent, stochastic gradient descent (SGD), learning rate, convex optimization.

Learning Resources: Optimization Techniques

  1. Optimization Techniques by Khan Academy

  2. Optimization Techniques by MIT OpenCourseWare

  3. Optimization Techniques by Math Is Fun

  4. Optimization Techniques by CrashCourse

  5. Optimization Techniques by Stanford University (Coursera)

  6. Optimization Techniques by Krista King Math

  7. Optimization Techniques by MathDoctorBob

  8. Optimization Techniques by University of Washington (Coursera)

  9. Optimization Techniques by Math Goodies

4. Numerical Computation

- Floating Point Representation: Floating point format, precision, machine epsilon.

Learning Resources: Floating Point Representation

  1. Floating Point Representation by Khan Academy

  2. Floating Point Representation by Wikipedia

  3. Floating Point Representation by Exploring Binary

  4. IEEE 754 Floating Point Standard by Explained Visually

  5. Floating Point Representation by Computerphile

  6. Floating Point Representation by MathWorks

  7. Floating Point Representation by GeeksforGeeks

  8. Floating Point Representation by Math Goodies

- Numerical Stability: Stability issues in numerical computations, conditioning and ill-conditioning.

Learning Resources: Numerical Stability

  1. Numerical Stability by Wikipedia

  2. Numerical Stability and Conditioning by Khan Academy

  3. Numerical Stability by Numerical Tours

  4. Numerical Stability by MathWorks

  5. Numerical Stability by MIT OpenCourseWare

  6. Numerical Stability and Conditioning by Numerical Methods for Engineers

  7. Floating Point Arithmetic and Numerical Stability by Computational Physics with Python

  8. Numerical Stability in Machine Learning by Towards Data Science

  9. Numerical Stability in Deep Learning by Machine Learning Mastery

- Gradient-Based Optimization: Calculating gradients, optimization algorithms, learning rate tuning.

Learning Resources: Gradient-Based Optimization

  1. Gradient Descent by Khan Academy

  2. Gradient-Based Optimization by Stanford University (Coursera)

  3. Gradient-Based Optimization by Machine Learning Mastery

  4. Gradient-Based Optimization by Andrew Ng

  5. Gradient-Based Optimization by DeepLearning.AI

  6. Gradient-Based Optimization by MathWorks

  7. Gradient Descent Optimization Algorithms by Sebastian Ruder

  8. Gradient-Based Optimization by Christopher Bishop

  9. Gradient-Based Optimization by OpenAI Spinning Up

- Autodiff and Symbolic Differentiation: Automatic differentiation, symbolic differentiation.

Learning Resources: Autodiff and Symbolic Differentiation

  1. Automatic Differentiation by Khan Academy

  2. Automatic Differentiation by Stanford University

  3. Automatic Differentiation by DiffSharp

  4. Symbolic Differentiation by Math Is Fun

  5. Symbolic Differentiation by MIT OpenCourseWare

  6. Automatic Differentiation by TensorFlow

  7. Automatic Differentiation by PyTorch

  8. Automatic Differentiation and Symbolic Differentiation by MathWorks

  9. Automatic Differentiation and Symbolic Differentiation by UC Berkeley

5. Machine Learning Fundamentals

- Linear Regression: Model representation, cost function, normal equation, gradient descent for linear regression.

Learning Resources: Linear Regression

  1. Linear Regression by Khan Academy

  2. Linear Regression by Stanford University (Coursera)

  3. Linear Regression by Andrew Ng

  4. Linear Regression by Towards Data Science

  5. Linear Regression by StatQuest with Josh Starmer

  6. Linear Regression by MathWorks

  7. Linear Regression by Machine Learning Mastery

  8. Linear Regression by Python Data Science Handbook

  9. Linear Regression by OpenAI Spinning Up

- Logistic Regression: Sigmoid function, logistic regression model, binary and multiclass logistic regression.

Learning Resources: Logistic Regression

  1. Logistic Regression by Khan Academy

  2. Logistic Regression by Stanford University (Coursera)

  3. Logistic Regression by Andrew Ng

  4. Logistic Regression by Towards Data Science

  5. Logistic Regression by StatQuest with Josh Starmer

  6. Logistic Regression by MathWorks

  7. Logistic Regression by Machine Learning Mastery

  8. Logistic Regression by Python Data Science Handbook

  9. Logistic Regression by OpenAI Spinning Up

- Support Vector Machines (SVM): Linear SVM, kernel trick, soft margin SVM.

Learning Resources: Support Vector Machines (SVM)

  1. Support Vector Machines (SVM) by Khan Academy

  2. Support Vector Machines (SVM) by Stanford University (Coursera)

  3. Support Vector Machines (SVM) by Andrew Ng

  4. Support Vector Machines (SVM) by StatQuest with Josh Starmer

  5. Support Vector Machines (SVM) by Scikit-learn Documentation

  6. Support Vector Machines (SVM) by Machine Learning Mastery

  7. Support Vector Machines (SVM) by Python Data Science Handbook

  8. Support Vector Machines (SVM) by OpenAI Spinning Up

  9. Support Vector Machines (SVM) by LIBSVM

- Decision Trees and Random Forests: Decision tree construction, random forests.

Learning Resources: Decision Trees and Random Forests

  1. Decision Trees by Khan Academy

  2. Decision Trees and Random Forests by Stanford University (Coursera)

  3. Decision Trees by Andrew Ng

  4. Decision Trees and Random Forests by StatQuest with Josh Starmer

  5. Decision Trees and Random Forests by Scikit-learn Documentation

  6. Decision Trees and Random Forests by Machine Learning Mastery

  7. Decision Trees and Random Forests by Python Data Science Handbook

  8. Decision Trees and Random Forests by OpenAI Spinning Up

  9. Decision Trees and Random Forests by Scikit-learn

- Evaluation Metrics: Accuracy, precision, recall, F1 score, ROC curve, AUC-ROC.

Learning Resources: Evaluation Metrics

  1. Evaluation Metrics for Machine Learning by Towards Data Science

  2. Evaluation Metrics for Classification by Machine Learning Mastery

  3. Evaluation Metrics for Regression by Machine Learning Mastery

  4. Evaluation Metrics for Binary Classification by Scikit-learn Documentation

  5. Evaluation Metrics for Multiclass Classification by Scikit-learn Documentation

  6. Evaluation Metrics for Imbalanced Classification by Machine Learning Mastery

  7. Evaluation Metrics for Clustering by Scikit-learn Documentation

  8. Evaluation Metrics for Recommender Systems by Machine Learning Mastery

  9. Evaluation Metrics for Natural Language Processing (NLP) by Machine Learning Mastery

  10. Evaluation Metrics for Time Series Forecasting by Machine Learning Mastery

6. Deep Learning Basics

- Feedforward Neural Networks: Architecture, activation functions, forward propagation, backward propagation.

Learning Resources: Feedforward Neural Networks

  1. Neural Networks by Khan Academy

  2. Feedforward Neural Networks by Stanford University (Coursera)

  3. Feedforward Neural Networks by Andrew Ng

  4. Feedforward Neural Networks by DeepLearning.AI

  5. Feedforward Neural Networks by PyTorch

  6. Feedforward Neural Networks by TensorFlow

  7. Feedforward Neural Networks by Machine Learning Mastery

  8. Feedforward Neural Networks by Python Data Science Handbook

  9. Feedforward Neural Networks by OpenAI Spinning Up

- Backpropagation Algorithm: Calculating gradients using backpropagation, weight updates.

Learning Resources: Backpropagation Algorithm

  1. Backpropagation Algorithm by Khan Academy

  2. Backpropagation Algorithm by Stanford University (Coursera)

  3. Backpropagation Algorithm by Andrew Ng

  4. Backpropagation Algorithm by DeepLearning.AI

  5. Backpropagation Algorithm by Machine Learning Mastery

  6. Backpropagation Algorithm by Towards Data Science

  7. Backpropagation Algorithm by Python Data Science Handbook

  8. Backpropagation Algorithm by OpenAI Spinning Up

  9. Backpropagation Algorithm by Deep Learning with PyTorch

- Weight Initialization: Xavier/Glorot initialization, He initialization.

Learning Resources: Weight Initialization

  1. Weight Initialization in Neural Networks by Machine Learning Mastery

  2. Weight Initialization in Deep Learning by Deeplearning.AI

  3. Weight Initialization in Neural Networks by TensorFlow

  4. Weight Initialization in Neural Networks by PyTorch

  5. Weight Initialization in Neural Networks by Deep Learning with Python book

  6. Weight Initialization in Neural Networks by Towards Data Science

  7. Weight Initialization in Neural Networks by Neural Designer

  8. Weight Initialization in Neural Networks by Machine Learning Wiki

  9. Weight Initialization in Neural Networks by OpenAI Spinning Up

- Gradient-Based Optimization Algorithms: Gradient descent, mini-batch gradient descent, stochastic gradient descent.

Learning Resources: Gradient-Based Optimization Algorithms

  1. Gradient-Based Optimization Algorithms by Machine Learning Mastery

  2. Gradient-Based Optimization Algorithms by Stanford University (Coursera)

  3. Gradient-Based Optimization Algorithms by Andrew Ng

  4. Gradient-Based Optimization Algorithms by DeepLearning.AI

  5. Gradient-Based Optimization Algorithms by Sebastian Ruder

  6. Gradient-Based Optimization Algorithms by PyTorch

  7. Gradient-Based Optimization Algorithms by TensorFlow

  8. Gradient-Based Optimization Algorithms by OpenAI Spinning Up

  9. Gradient-Based Optimization Algorithms by Machine Learning Wiki

  10. Gradient-Based Optimization Algorithms by Deep Learning with Python book

- Regularization Techniques: L1 and L2 regularization, dropout.

Learning Resources: Regularization Techniques

  1. Regularization Techniques in Machine Learning by Machine Learning Mastery

  2. Regularization Techniques in Deep Learning by Deeplearning.AI

  3. Regularization Techniques in Machine Learning by Towards Data Science

  4. Regularization Techniques in Neural Networks by DeepLearning.AI

  5. Regularization Techniques in Machine Learning by Scikit-learn Documentation

  6. Regularization Techniques in Deep Learning by TensorFlow

  7. Regularization Techniques in Neural Networks by PyTorch

  8. Regularization Techniques in Machine Learning by Sebastian Raschka

  9. Regularization Techniques in Machine Learning by OpenAI Spinning Up

- Convolutional Neural Networks (CNNs): Convolutional layers, pooling layers, convolution arithmetic.

Learning Resources: Convolutional Neural Networks (CNNs)

  1. Convolutional Neural Networks by Stanford University (Coursera)

  2. Convolutional Neural Networks (CNNs) by DeepLearning.AI

  3. Convolutional Neural Networks (CNNs) by Andrew Ng

  4. Convolutional Neural Networks by Machine Learning Mastery

  5. Convolutional Neural Networks by TensorFlow

  6. Convolutional Neural Networks by PyTorch

  7. Convolutional Neural Networks by Deep Learning with Python book

  8. Convolutional Neural Networks by Machine Learning Wiki

  9. Convolutional Neural Networks by OpenAI Spinning Up

- Recurrent Neural Networks (RNNs): RNN cells, LSTM, bidirectional RNNs.

Learning Resources: Recurrent Neural Networks (RNNs)

  1. Recurrent Neural Networks by Stanford University (Coursera)

  2. Recurrent Neural Networks (RNNs) by DeepLearning.AI

  3. Recurrent Neural Networks (RNNs) by Andrew Ng

  4. Recurrent Neural Networks by Machine Learning Mastery

  5. Recurrent Neural Networks by TensorFlow

  6. Recurrent Neural Networks by PyTorch

  7. Recurrent Neural Networks by Deep Learning with Python book

  8. Recurrent Neural Networks by Machine Learning Wiki

  9. Recurrent Neural Networks by OpenAI Spinning Up

- Generative Adversarial Networks (GANs): Generator and discriminator networks, GAN training.

Learning Resources: Generative Adversarial Networks (GANs)

  1. Generative Adversarial Networks (GANs) by Stanford University (Coursera)

  2. Generative Adversarial Networks (GANs) by DeepLearning.AI

  3. Generative Adversarial Networks (GANs) by Ian Goodfellow, et al. (Original GAN Paper)

  4. Generative Adversarial Networks (GANs) by Machine Learning Mastery

  5. Generative Adversarial Networks (GANs) by TensorFlow

  6. Generative Adversarial Networks (GANs) by PyTorch

  7. Generative Adversarial Networks (GANs) by Deep Learning with Python book

  8. Generative Adversarial Networks (GANs) by Machine Learning Wiki

  9. Generative Adversarial Networks (GANs) by OpenAI Spinning Up

7. Advanced Deep Learning Topics

- Batch Normalization: Normalizing activations in deep neural networks.

Learning Resources: Batch Normalization

  1. Batch Normalization by Machine Learning Mastery

  2. Batch Normalization by Stanford University (Coursera)

  3. Batch Normalization by DeepLearning.AI

  4. Batch Normalization by Andrew Ng

  5. Batch Normalization by TensorFlow

  6. Batch Normalization by PyTorch

  7. Batch Normalization by Deep Learning with Python book

  8. Batch Normalization by Machine Learning Wiki

  9. Batch Normalization by OpenAI Spinning Up

- Transfer Learning: Leveraging pre-trained models for new tasks.

Learning Resources: Batch Normalization

  1. Batch Normalization by Machine Learning Mastery

  2. Batch Normalization by Stanford University (Coursera)

  3. Batch Normalization by DeepLearning.AI

  4. Batch Normalization by Andrew Ng

  5. Batch Normalization by TensorFlow

  6. Batch Normalization by PyTorch

  7. Batch Normalization by Deep Learning with Python book

  8. Batch Normalization by Machine Learning Wiki

  9. Batch Normalization by OpenAI Spinning Up

- Reinforcement Learning: Markov decision processes, Q-learning, policy gradients.

Learning Resources: Reinforcement Learning

  1. Reinforcement Learning by David Silver (DeepMind)

  2. Reinforcement Learning by Stanford University (Coursera)

  3. Reinforcement Learning by DeepLearning.AI

  4. Reinforcement Learning by OpenAI

  5. Reinforcement Learning by Sutton and Barto (Book)

  6. Reinforcement Learning by TensorFlow

  7. Reinforcement Learning by PyTorch

  8. Reinforcement Learning by Machine Learning Wiki

  9. Reinforcement Learning by OpenAI Spinning Up

- Natural Language Processing (NLP): Word embeddings, recurrent neural networks for sequence modeling.

Learning Resources: Natural Language Processing (NLP)

  1. Natural Language Processing by Stanford University (Coursera)

  2. Natural Language Processing with Deep Learning by DeepLearning.AI

  3. Natural Language Processing (NLP) by Fast.ai

  4. Natural Language Processing (NLP) by TensorFlow

  5. Natural Language Processing (NLP) by PyTorch

  6. Natural Language Processing (NLP) by NLTK (Natural Language Toolkit)

  7. Natural Language Processing (NLP) by Machine Learning Mastery

  8. Natural Language Processing (NLP) by Machine Learning Wiki

  9. Natural Language Processing (NLP) by OpenAI Spinning Up

- Time Series Analysis: Modeling and forecasting time series data with deep learning.

Learning Resources: Time Series Analysis

  1. Time Series Analysis and Its Applications by Shumway and Stoffer (Book)

  2. Practical Time Series Analysis by Aileen Nielsen

  3. Time Series Analysis by Stanford University (Coursera)

  4. Time Series Analysis by Kaggle

  5. Time Series Analysis by Machine Learning Mastery

  6. Time Series Analysis by TensorFlow

  7. Time Series Analysis by PyTorch

  8. Time Series Analysis by Machine Learning Wiki

  9. Time Series Analysis by OpenAI Spinning Up

- Autoencoders and Variational Autoencoders (VAEs): Unsupervised learning, dimensionality reduction, generative models.

Learning Resources: Autoencoders and Variational Autoencoders (VAEs)

  1. Autoencoders by Stanford University (Coursera)

  2. Autoencoders and Variational Autoencoders (VAEs) by DeepLearning.AI

  3. Autoencoders by TensorFlow

  4. Variational Autoencoders (VAEs) by TensorFlow

  5. Autoencoders and Variational Autoencoders (VAEs) by PyTorch

  6. Autoencoders and Variational Autoencoders (VAEs) by Machine Learning Mastery

  7. Autoencoders and Variational Autoencoders (VAEs) by Machine Learning Wiki

  8. Autoencoders and Variational Autoencoders (VAEs) by OpenAI Spinning Up

  9. Autoencoders and Variational Autoencoders (VAEs) by Christopher Olah

  10. Variational Autoencoders (VAEs) by Carl Doersch

  11. Autoencoders and Variational Autoencoders (VAEs) by OpenAI

  12. Building Autoencoders in Keras by François Chollet (Keras Blog)

  13. Autoencoders and Variational Autoencoders (VAEs) by Google Developers

  14. Deep Learning Book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

  15. Autoencoders and Variational Autoencoders (VAEs) by Distill.pub

  16. Variational Autoencoders (VAEs) by OpenAI Spinning Up

- Model Interpretability and Explainability: Techniques to interpret and explain deep learning models.

Learning Resources: Model Interpretability and Explainability

  1. Interpretable Machine Learning by Christoph Molnar (Book)

  2. Interpretable Machine Learning by Microsoft Research

  3. Model Interpretability and Explainability by Google AI

  4. Model Interpretability and Explainability by OpenAI

  5. Model Interpretability and Explainability by scikit-learn (Python Library)

  6. Explainable AI (XAI) by DARPA

  7. Model Interpretability and Explainability by Machine Learning Wiki

  8. Model Interpretability and Explainability by Towards Data Science

  9. Model Interpretability and Explainability by OpenAI Spinning Up

  10. A Unified Approach to Interpreting Model Predictions by Marco Tulio Ribeiro, et al.

  11. SHAP (SHapley Additive exPlanations) by Lundberg and Lee

  12. LIME (Local Interpretable Model-Agnostic Explanations) by Ribeiro, Singh, and Guestrin

  13. Anchors: High-Precision Model-Agnostic Explanations by Ribeiro, Singh, and Guestrin

  14. Interpretable Deep Learning with Python by Yuriy Guts

  15. Explainable AI and Machine Learning Interpretability by IBM Developer

  16. Interpretable Machine Learning in Python by Christoph Molnar

  17. InterpretML: A Python Library for Model Interpretability by Microsoft

  18. Model Interpretability and Explainability by OpenAI Spinning Up

Contributing

This roadmap is a compilation of mathematical concepts covered in various deep learning resources, including the following:

  1. "Deep Learning" book by Ian Goodfellow, Yoshua Bengio, and Aaron Courville.
  2. "Deep Learning with Python, Second Edition" by François Chollet.
  3. "Grokking Deep Learning" by Andrew Trask.
  4. "Deep Learning: A Practitioner's Approach" by Josh Patterson and Adam Gibson.
  5. "Deep Learning for Coders with fastai and PyTorch" by Jeremy Howard and Sylvain Gugger.

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