--- title: "1 What is deep learning?: 1 What Is Deep Learning 1" id: "13338" type: "page" slug: "01-1-what-is-deep-learning-1" published_at: "2026-07-19T18:46:28+00:00" modified_at: "2026-07-19T18:46:29+00:00" url: "https://preppers-paradise.com/library/deeplearningwithrthirdedition/01-1-what-is-deep-learning-1/" markdown_url: "https://preppers-paradise.com/library/deeplearningwithrthirdedition/01-1-what-is-deep-learning-1.md" excerpt: "This book offers a comprehensive guide to deep learning, detailing its foundational concepts, underlying mathematics, and practical implementation. Readers will learn to design, train, and deploy neur" taxonomy_category: - "AI & Machine Learning" - "Books" - "Free Teaser" taxonomy_post_tag: - "artificial intelligence" - "computer vision" - "data science" - "deep learning" - "keras" - "machine learning" - "neural networks" - "pytorch" - "tensorflow" - "time series" --- # 1 What is deep learning?: 1 What Is Deep Learning 1 [← 1 What is deep learning?](/library/deeplearningwithrthirdedition/) Chapter 1 of 80 · Free teaser # 1 What is deep learning? ## 1.1 Artificial intelligence, machine learning, and deep learning ## 1.2 Artificial intelligence ## 1.3 Machine learning ## 1.4 Learning rules and representations from data ## 1.5 The "deep" in "deep learning" ## 1.6 Understanding how deep learning works, in three figures ## 1.7 What makes deep learning different ## 1.8 The age of generative AI ## 1.9 What deep learning has achieved so far ## 1.10 Beware of the short-term hype ## 1.11 Summer can turn to winter ## 1.12 The promise of AI ## 1.13 How R fits in # 2 The mathematical building blocks of neural networks ## 2.1 A first look at a neural network ## 2.2 Data representations for neural networks ### Scalars (rank-0 tensors) ### Vectors (rank-1 tensors) ### Matrices (rank-2 tensors) ### Rank-3 tensors and higher-rank tensors ### Key attributes ### Manipulating NumPy tensors in R ### The notion of data batches ### Real-world examples of data tensors ## 2.3 The gears of neural networks: Tensor operations ### Element-wise operations ### Broadcasting ### Tensor products ### Tensor reshaping ### Geometric interpretation of tensor operations ### A geometric interpretation of deep learning ## 2.4 The engine of neural networks: Gradient-based optimization ### What's a derivative? ### Derivative of a tensor operation: The gradient ### Stochastic gradient descent ### Chaining derivatives: The Backpropagation algorithm ## 2.5 Looking back at our first example ### Reimplementing our first example from scratch ### Running one training step ### The full training loop ### Evaluating the model # 3 Introduction to TensorFlow, PyTorch, JAX, and Keras ## 3.1 A brief history of deep learning frameworks ## 3.2 How these frameworks relate to each other ## 3.3 Introduction to TensorFlow ### First steps with TensorFlow ### An end-to-end example: A linear classifier in pure TensorFlow ### What makes the TensorFlow approach unique ## 3.4 Introduction to PyTorch ### First steps with PyTorch ### An end-to-end example: A linear classifier in pure PyTorch ### What makes the PyTorch approach unique ## 3.5 Introduction to JAX ### First steps with JAX ### Tensors in JAX ### Random number generation in JAX ### An end-to-end example: A linear classifier in pure JAX ### What makes the JAX approach unique ## 3.6 Introduction to Keras ### First steps with Keras ### Layers: The building blocks of deep learning ### From layers to models ### The "compile" step: Configuring the learning process ### Picking a loss function ### Understanding the fit() method ### Monitoring loss and metrics on validation data ### Inference: Using a model after training # 4 Classification and regression ## 4.1 Classifying movie reviews: A binary classification example ### The IMDb dataset ### Preparing the data ### Building the model ### Validating the approach ### Using a trained model to generate predictions on new data ### Further experiments ### Wrapping up ## 4.2 Classifying newswires: A multiclass classification example ### The Reuters dataset ### Preparing the data ### Building the model ### Validating the approach ### Generating predictions on new data ### A different way to handle the labels and the loss ### The importance of having sufficiently large intermediate layers ### Further experiments ### Wrapping up ## 4.3 Predicting house prices: A regression example ### The California housing dataset ### Preparing the data ### Building the model ### Validating the approach using K-fold validation ### Generating predictions on new data ### Wrapping up # 5 Fundamentals of machine learning ## 5.1 Generalization: The goal of machine learning ### Underfitting and overfitting ### The nature of generalization in deep learning ## 5.2 Evaluating machine learning models ### Training, validation, and test sets ### Beating a commonsense baseline ### Things to keep in mind about model evaluation ## 5.3 Improving model fit ### Tuning key gradient descent parameters ### Using better architecture priors ### Increasing model capacity ## 5.4 Improving generalization ### Dataset curation ### Feature engineering ### Using early stopping ### Regularizing a model # 6 The universal workflow of machine learning ## 6.1 Defining the task ### Framing the problem ### Collecting a dataset ### Understanding your data ### Choosing a measure of success ## 6.2 Developing a model ### Preparing the data ### Choosing an evaluation protocol ### Beating a baseline ### Scaling up: Developing a model that overfits ### Regularizing and tuning your model ## 6.3 Deploying your model ### Explaining your work to stakeholders and setting expectations ### Shipping an inference model ### Monitoring your model in the wild ### Maintaining your model # 7 A deep dive into Keras ## 7.1 A spectrum of workflows ## 7.2 Different ways to build Keras models ### The Sequential model ### The Functional API ### Subclassing the Model class ### Mixing and matching different components ### Remember: Use the right tool for the job ## 7.3 Using built-in training and evaluation loops * Writing our own metrics * Using callbacks * The EarlyStopping and ModelCheckpoint callbacks * Writing our own callbacks * Monitoring and visualization with TensorBoard ## 7.4 Writing training and evaluation loops * Training vs. inference * Writing custom training step functions * Low-level usage of metrics * Using fit() with a custom training loop * Handling metrics in a custom train_step()