Tabla de Contenidos: “…Evaluating and drafting contracts -- Performing due diligence -- Managing intellectual property -- Chapter 3 Launching into the AI Marketing Era -- Ready or Not: AI Is Your New Marketing Copilot -- Putting performance marketers at risk -- Competing with creative directors -- Watching AI Upend the Corporate World -- Taking Foundational Steps Toward AI Marketing -- Addressing the marketing dichotomy -- Assessing progress with the AI checklist -- Adopting a Strategic Framework for Entering the AI Era -- Going for liftoff -- Initiating atmospheric ascent -- Reaching escape velocity -- Dominating deep space -- Part 2 Exploring Fundamental AI Structures and Concepts -- Chapter 4 Collecting, Organizing, and Transforming Data -- Defining Data in the Context of AI -- Considering the quality of data -- Getting an appropriate quantity of data -- Choosing Data Collection Methods for Marketing with AI -- Identifying data sources and methods -- Minding data privacy and ethics -- Putting Your Marketing Data in Its Place -- Understanding Data via Manual and Automated Systems -- Preparing the Data for Use by AI Algorithms and Models -- Perfecting data by cleaning -- Transforming data -- Splitting data into subsets -- Trimming down data -- Handling imbalanced and irrelevant data -- Chapter 5 Making Connections: Machine Learning and Neural Networks -- Examining the Process of Machine Learning -- Understanding Neural Networks -- Layers of a neural network -- Challenges with neural networks -- Supervised and Unsupervised Learning -- Following the path of supervised learning -- Embracing the freedom of unsupervised learning -- Exploring Reinforcement Learning -- Reinforcement learning in e-mail marketing -- Weighing explorations against exploits -- Mastering Sequences and Time Series -- Seeing how neural networks excel at time series analysis…”

Tabla de Contenidos: “…3.8 Tensor metadata: Size, offset, and stride -- 3.8.1 Views of another tensor's storage -- 3.8.2 Transposing without copying -- 3.8.3 Transposing in higher dimensions -- 3.8.4 Contiguous tensors -- 3.9 Moving tensors to the GPU -- 3.9.1 Managing a tensor's device attribute -- 3.10 NumPy interoperability -- 3.11 Generalized tensors are tensors, too -- 3.12 Serializing tensors -- 3.12.1 Serializing to HDF5 with h5py -- 3.13 Conclusion -- 3.14 Exercises -- 3.15 Summary -- 4 Real-world data representation using tensors -- 4.1 Working with images -- 4.1.1 Adding color channels -- 4.1.2 Loading an image file -- 4.1.3 Changing the layout -- 4.1.4 Normalizing the data -- 4.2 3D images: Volumetric data -- 4.2.1 Loading a specialized format -- 4.3 Representing tabular data -- 4.3.1 Using a real-world dataset -- 4.3.2 Loading a wine data tensor -- 4.3.3 Representing scores -- 4.3.4 One-hot encoding -- 4.3.5 When to categorize -- 4.3.6 Finding thresholds -- 4.4 Working with time series -- 4.4.1 Adding a time dimension -- 4.4.2 Shaping the data by time period -- 4.4.3 Ready for training -- 4.5 Representing text -- 4.5.1 Converting text to numbers -- 4.5.2 One-hot-encoding characters -- 4.5.3 One-hot encoding whole words -- 4.5.4 Text embeddings -- 4.5.5 Text embeddings as a blueprint -- 4.6 Conclusion -- 4.7 Exercises -- 4.8 Summary -- 5 The mechanics of learning -- 5.1 A timeless lesson in modeling -- 5.2 Learning is just parameter estimation -- 5.2.1 A hot problem -- 5.2.2 Gathering some data -- 5.2.3 Visualizing the data -- 5.2.4 Choosing a linear model as a first try -- 5.3 Less loss is what we want -- 5.3.1 From problem back to PyTorch -- 5.4 Down along the gradient -- 5.4.1 Decreasing loss -- 5.4.2 Getting analytical -- 5.4.3 Iterating to fit the model -- 5.4.4 Normalizing inputs -- 5.4.5 Visualizing (again)…”