Basic Mathematical Foundations of AI Hands on with Python (Mastering Machine Learning)

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Free Download Basic Mathematical Foundations of AI: Hands on with Python (Mastering Machine Learning)
English | 2024 | ISBN: B0DBQ37ZMJ | Pages: 521 | PDF | 9.63 MB
Unveil the power of AI with this comprehensive guide to the fundamental mathematical foundations behind it. Whether you're a beginner or an advanced learner, this book is the perfect resource to dive deep into the mathematics that underpin artificial intelligence and machine learning.

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.
  

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