£62.31

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

Languages

Price data last checked 124 day(s) ago - refreshing...

View at Amazon

We'll watch every seller, every day. One email when your price arrives.

It has never been this cheap. We have no record of a lower price.

£62 today · cheaper than every other day in the last 23 months

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 553 days • 553 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 553 days • 1 price levels

Days at Price

Price Analysis

Most common price: £62 (553 days, 100.0%)

Price range: £62 - £62

Price levels: 1 different prices over 553 days

Description

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. 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!

Product Specifications

Format: hardcover
ASIN: B0DBR8K4LY
Category: Books > Subjects > Computing & Internet > Programming > Languages
Domain: Amazon UK
Release Date: 31 July 2024
Listed Since: 31 July 2024

Barcode

No barcode data available

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

It has never been this cheap. We have no record of a lower price.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

AI: All About the AI World

Master AI From Beginner To Business Leader.: Discover How To Streamline Your AI Journey, From Beginner Tools To Cutting Edge Business Applications ... (Artificial Intelligence For Everyone.)

The Future of Robots & Our World: How AI & Automation Will Shape Humanity: The Impact of Artificial Intelligence and Robotics on Human Society

Intelligent Systems and Applications: Proceedings of the 2021 Intelligent Systems Conference (IntelliSys) Volume 3: 296 (Lecture Notes in Networks and Systems, 296)

Artificial Intelligence and Its Contexts: Security, Business and Governance (Advanced Sciences and Technologies for Security Applications)

Intelligent Systems and Applications: Proceedings of the 2021 Intelligent Systems Conference (IntelliSys) Volume 1: 294 (Lecture Notes in Networks and Systems, 294)

Responsible AI: Implementing Ethical and Unbiased Algorithms

Modern Approaches in Machine Learning and Cognitive Science: A Walkthrough: Latest Trends in AI, Volume 2: 956 (Studies in Computational Intelligence, 956)

Artificial Intelligence and Applications

Fundamental Mathematical Concepts for Machine Learning in Science

Advances in Artificial Intelligence: Selected Papers from the Annual Conference of Japanese Society of Artificial Intelligence (JSAI 2020): 1357 (Advances in Intelligent Systems and Computing, 1357)

AI*IA 2005: Advances in Artificial Intelligence: 9th Congress of the Italian Association for Artificial Intelligence Milan, Italy, September 21-23, ... (Lecture Notes in Computer Science, 3673)

Knowledge-Based Information Systems in Practice: 30 (Smart Innovation, Systems and Technologies, 30)

Handbook of Research on AI and ML for Intelligent Machines and Systems

Trends in Data Engineering Methods for Intelligent Systems: Proceedings of the International Conference on Artificial Intelligence and Applied ... and ... and Communications Technologies, 76)

Springer Advances in Artificial Intelligence JSAI 2019 Book

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Plunkett's Artificial Intelligence (AI) & Machine Learning Industry Almanac 2024: Artificial Intelligence (AI) & Machine Learning Industry Market Research, Statistics, Trends and Leading Companies

Modern Trends and Techniques in Computer Science: 3rd Computer Science On-line Conference 2014 (CSOC 2014): 285 (Advances in Intelligent Systems and Computing, 285)

Data Science and Predictive Analytics: Biomedical and Health Applications using R (The Springer Series in Applied Machine Learning)

Universal Artificial Intelligence: Sequential Decisions Based on Algorithmic Probability

Lecture Notes in Computational Intelligence and Decision Making: 2021 International Scientific Conference "Intellectual Systems of Decision-making and ... and Communications Technologies, 77)

Math and Architectures of Deep Learning

Intelligent Systems in Industrial Applications: 949 (Studies in Computational Intelligence, 949)