With clear explanations and practical examples, each chapter focuses on a specific topic and includes Python code and multiple-choice review questions to reinforce your understanding. From linear equations to advanced techniques like deep learning and reinforcement learning, you will gain a solid understanding of the mathematical concepts essential for working in the field of AI.

In this comprehensive book, you will learn:

– Solving systems of linear equations using matrix methods.
– Exploring quadratic equations and their applications in optimization.
– Finding eigenvalues and eigenvectors for feature extraction.
– Decomposing matrices using algorithms like LU Decomposition and SVD.
– Implementing gradient descent for linear regression and neural networks.
– Estimating linear regression parameters using Ordinary Least Squares (OLS).
– Modeling binary outcomes with logistic regression.
– Applying the softmax function for multiclass classification.
– Understanding various activation functions and their role in neural networks.
– Training neural networks using backpropagation.
– Classifying data using K-Nearest Neighbors and decision trees.
– Harnessing the power of ensemble methods with random forests.
– Maximizing margins with support vector machines.
– Reducing dimensionality with principal component analysis (PCA).
– Discriminating between classes using linear discriminant analysis (LDA).
– Applying probabilistic classification with Naive Bayes.
– Clustering data points using K-Means and Gaussian Mixture Models.
– Modeling sequential data with Hidden Markov Models and Markov Chains.
– Applying filtering algorithms like Particle Filters and Kalman Filters.
– Extracting features with convolutional neural networks (CNN).
– Modeling sequence data with recurrent neural networks (RNN) and LSTMs.
– Enhancing RNNs with attention mechanisms and transformer networks.
– Exploring generative modeling with autoencoders, VAEs, and GANs.
– Mastering reinforcement learning basics, Q-learning, and actor-critic methods.
– Understanding Bayesian inference, Gaussian processes, and MCMC methods.
– Solving complex optimization problems with linear programming, integer programming, and convex optimization.
– Implementing dynamic programming, A* search algorithm, and PageRank.
– Analyzing text data with Latent Dirichlet Allocation (LDA) and matrix factorization.
– Preventing overfitting and regularization techniques like ridge regression and lasso regression.
– Optimizing non-linear functions with Newton’s method and stochastic gradient descent.
– Implementing batch gradient descent and mini-batch gradient descent.
– Applying regularization techniques like dropout and batch normalization.
– Boosting weak classifiers with AdaBoost and gradient boosting machines (GBM).
– Visualizing high-dimensional data with t-SNE dimensionality reduction.

This book is ideal for both beginners looking to build a strong mathematical foundation in AI and experienced practitioners seeking to deepen their knowledge and sharpen their skills. So why wait? Grab your copy today and embark on a fascinating journey into the mathematical foundations of AI!