فهرست مطالب

• Cover
• Copyright
• Contributors
• Table of Contents
• Preface
• Section 1: Fundamentals and Core Algorithms
• Chapter 1: Overview of Algorithms
• What is an algorithm?
• Python packages
• Algorithm design techniques
• Performance analysis
• Selecting an algorithm
• Validating an algorithm
• Summary
• Chapter 2: Data Structures Used in Algorithms
• Exploring Python built-in data types
• Exploring abstract data types
• Summary
• Chapter 3: Sorting and Searching Algorithms
• Introducing sorting algorithms
• Introduction to searching algorithms
• Practical applications
• Summary
• Chapter 4: Designing Algorithms
• Introducing the basic concepts of designing an algorithm
• Understanding algorithmic strategies
• A practical application – solving the TSP
• Presenting the PageRank algorithm
• Understanding linear programming
• Summary
• Chapter 5: Graph Algorithms
• Understanding graphs: a brief introduction
• Graph theory and network analysis
• Representations of graphs
• Graph mechanics and types
• Introducing network analysis theory
• Understanding graph traversals
• Case study: fraud detection using SNA
• Summary
• Section 2: Machine Learning Algorithms
• Chapter 6: Unsupervised Machine Learning Algorithms
• Introducing unsupervised learning
• Understanding clustering algorithms
• Steps of hierarchical clustering
• Coding a hierarchical clustering algorithm
• Understanding DBSCAN
• Creating clusters using DBSCAN in Python
• Evaluating the clusters
• Dimensionality reduction
• Association rules mining
• Summary
• Chapter 7: Traditional Supervised Learning Algorithms
• Understanding supervised machine learning
• Formulating supervised machine learning problems
• Understanding classification algorithms
• Decision tree classification algorithm
• Understanding the ensemble methods
• Logistic regression
• The SVM algorithm
• Bayes’ theorem
• For classification algorithms, the winner is...
• Linear regression
• For regression algorithms, the winner is...
• Practical example – how to predict the weather
• Summary
• Chapter 8: Neural Network Algorithms
• The evolution of neural networks
• Understanding neural networks
• Training a neural network
• Understanding the anatomy of a neural network
• Defining gradient descent
• Activation functions
• Tools and frameworks
• Choosing a sequential or functional model
• Understanding the types of neural networks
• Using transfer learning
• Case study – using deep learning for fraud detection
• Summary
• Chapter 9: Algorithms for Natural Language Processing
• Introducing NLP
• Understanding NLP terminology
• Cleaning data using Python
• Understanding the Term Document Matrix
• Introduction to word embedding
• Implementing word embedding with Word2Vec
• Case study: Restaurant review sentiment analysis
• Applications of NLP
• Summary
• Chapter 10: Understanding Sequential Models
• Understanding sequential data
• Data representation for sequential models
• Introducing RNNs
• GRU
• Introducing LSTM
• Summary
• Chapter 11: Advanced Sequential Modeling Algorithms
• The evolution of advanced sequential modeling techniques
• Exploring autoencoders
• Understanding the Seq2Seq model
• Understanding the attention mechanism
• Delving into self-attention
• Transformers: the evolution in neural networks after self-attention
• LLMs
• Summary
• Section 3: Advanced Topics
• Chapter 12: Recommendation Engines
• Introducing recommendation systems
• Types of recommendation engines
• Understanding the limitations of recommendation systems
• Areas of practical applications
• Practical example – creating a recommendation engine
• Summary
• Chapter 13: Algorithmic Strategies for Data Handling
• Introduction to data algorithms
• Presenting the CAP theorem
• Decoding data compression algorithms
• Practical example: Data management in AWS: A focus on CAP theorem and compression algorithms
• Summary
• Chapter 14: Cryptography
• Introduction to cryptography
• Understanding the types of cryptographic techniques
• Example: security concerns when deploying a machine learning model
• Summary
• Chapter 15: Large-Scale Algorithms
• Introduction to large-scale algorithms
• Characterizing performant infrastructure for large-scale algorithms
• Strategizing multi-resource processing
• Understanding theoretical limitations of parallel computing
• How Apache Spark empowers large-scale algorithm processing
• Using large-scale algorithms in cloud computing
• Summary
• Chapter 16: Practical Considerations
• Challenges facing algorithmic solutions
• Failure of Tay, the Twitter AI bot
• The explainability of an algorithm
• Understanding ethics and algorithms
• Reducing bias in models
• When to use algorithms
• Summary
• Packt page
• Other Books You May Enjoy
• Index