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Pro deep learning with TensorFlow 2.0 : a mathematical approach to advanced artificial intelligence in Python /
This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0. Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Apress,
[2023]
|
| Edición: | Second edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Intro
- Table of Contents
- About the Author
- About the Technical Reviewer
- Introduction
- Chapter 1: Mathematical Foundations
- Linear Algebra
- Vector
- Scalar
- Matrix
- Tensor
- Matrix Operations and Manipulations
- Addition of Two Matrices
- Subtraction of Two Matrices
- Product of Two Matrices
- Transpose of a Matrix
- Dot Product of Two Vectors
- Matrix Working on a Vector
- Linear Independence of Vectors
- Rank of a Matrix
- Identity Matrix or Operator
- Determinant of a Matrix
- Interpretation of Determinant
- Inverse of a Matrix
- Norm of a Vector
- Pseudo-Inverse of a Matrix
- Unit Vector in the Direction of a Specific Vector
- Projection of a Vector in the Direction of Another Vector
- Eigen Vectors
- Characteristic Equation of a Matrix
- Power Iteration Method for Computing Eigen Vector
- Calculus
- Differentiation
- Gradient of a Function
- Successive Partial Derivatives
- Hessian Matrix of a Function
- Maxima and Minima of Functions
- Rules for Maxima and Minima for a Univariate Function
- Local Minima and Global Minima
- Positive Semi-definite and Positive Definite
- Convex Set
- Convex Function
- Non-convex Function
- Multivariate Convex and Non-convex Functions Examples
- Taylor Series
- Probability
- Unions, Intersection, and Conditional Probability
- Chain Rule of Probability for Intersection of Event
- Mutually Exclusive Events
- Independence of Events
- Conditional Independence of Events
- Bayes Rule
- Probability Mass Function
- Probability Density Function
- Expectation of a Random Variable
- Variance of a Random Variable
- Skewness and Kurtosis
- Covariance
- Correlation Coefficient
- Some Common Probability Distribution
- Uniform Distribution
- Normal Distribution
- Multivariate Normal Distribution
- Bernoulli Distribution
- Binomial Distribution
- Poisson Distribution
- Beta Distribution
- Dirichlet Distribution
- Gamma Distribution
- Likelihood Function
- Maximum Likelihood Estimate
- Hypothesis Testing and p Value
- Formulation of Machine-Learning Algorithm and Optimization Techniques
- Supervised Learning
- Linear Regression as a Supervised Learning Method
- Linear Regression Through Vector Space Approach
- Classification
- Hyperplanes and Linear Classifiers
- Unsupervised Learning
- Reinforcement Learning
- Optimization Techniques for Machine-Learning Gradient Descent
- Gradient Descent for a Multivariate Cost Function
- Contour Plot and Contour Lines
- Steepest Descent
- Stochastic Gradient Descent
- Newton's Method
- Linear Curve
- Negative Curvature
- Positive Curvature
- Constrained Optimization Problem
- A Few Important Topics in Machine Learning
- Dimensionality-Reduction Methods
- Principal Component Analysis
- When Will PCA Be Useful in Data Reduction?
- How Do You Know How Much Variance Is Retained by the Selected Principal Components?
- Singular Value Decomposition