book

Deep Learning for Coders with fastai and PyTorch

by Jeremy Howard, Sylvain Gugger

July 2020

Intermediate to advanced

621 pages

16h 47m

English

O'Reilly Media, Inc.

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Preface
Who This Book Is ForWhat You Need to KnowWhat You Will LearnO’Reilly Online LearningHow to Contact Us
Foreword
I. Deep Learning in Practice
1. Your Deep Learning Journey
Deep Learning Is for EveryoneNeural Networks: A Brief HistoryWho We AreHow to Learn Deep LearningYour Projects and Your MindsetThe Software: PyTorch, fastai, and Jupyter (And Why It Doesn’t Matter)Your First ModelGetting a GPU Deep Learning ServerRunning Your First NotebookWhat Is Machine Learning?What Is a Neural Network?A Bit of Deep Learning JargonLimitations Inherent to Machine LearningHow Our Image Recognizer WorksWhat Our Image Recognizer LearnedImage Recognizers Can Tackle Non-Image TasksJargon RecapDeep Learning Is Not Just for Image ClassificationValidation Sets and Test SetsUse Judgment in Defining Test SetsA Choose Your Own Adventure MomentQuestionnaireFurther Research
2. From Model to Production
The Practice of Deep LearningStarting Your ProjectThe State of Deep LearningThe Drivetrain ApproachGathering DataFrom Data to DataLoadersData AugmentationTraining Your Model, and Using It to Clean Your DataTurning Your Model into an Online ApplicationUsing the Model for InferenceCreating a Notebook App from the ModelTurning Your Notebook into a Real AppDeploying Your AppHow to Avoid DisasterUnforeseen Consequences and Feedback LoopsGet Writing!QuestionnaireFurther Research
3. Data Ethics
Key Examples for Data EthicsBugs and Recourse: Buggy Algorithm Used for Healthcare BenefitsFeedback Loops: YouTube’s Recommendation SystemBias: Professor Latanya Sweeney “Arrested”Why Does This Matter?Integrating Machine Learning with Product DesignTopics in Data EthicsRecourse and AccountabilityFeedback LoopsBiasDisinformationIdentifying and Addressing Ethical IssuesAnalyze a Project You Are Working OnProcesses to ImplementThe Power of DiversityFairness, Accountability, and TransparencyRole of PolicyThe Effectiveness of RegulationRights and PolicyCars: A Historical PrecedentConclusionQuestionnaireFurther ResearchDeep Learning in Practice: That’s a Wrap!
II. Understanding fastai’s Applications
4. Under the Hood: Training a Digit Classifier
Pixels: The Foundations of Computer VisionFirst Try: Pixel SimilarityNumPy Arrays and PyTorch TensorsComputing Metrics Using BroadcastingStochastic Gradient DescentCalculating GradientsStepping with a Learning RateAn End-to-End SGD ExampleSummarizing Gradient DescentThe MNIST Loss FunctionSigmoidSGD and Mini-BatchesPutting It All TogetherCreating an OptimizerAdding a NonlinearityGoing DeeperJargon RecapQuestionnaireFurther Research
5. Image Classification
From Dogs and Cats to Pet BreedsPresizingChecking and Debugging a DataBlockCross-Entropy LossViewing Activations and LabelsSoftmaxLog LikelihoodTaking the logModel InterpretationImproving Our ModelThe Learning Rate FinderUnfreezing and Transfer LearningDiscriminative Learning RatesSelecting the Number of EpochsDeeper ArchitecturesConclusionQuestionnaireFurther Research
6. Other Computer Vision Problems
Multi-Label ClassificationThe DataConstructing a DataBlockBinary Cross EntropyRegressionAssembling the DataTraining a ModelConclusionQuestionnaireFurther Research

7. Training a State-of-the-Art Model
ImagenetteNormalizationProgressive ResizingTest Time AugmentationMixupLabel SmoothingConclusionQuestionnaireFurther Research
8. Collaborative Filtering Deep Dive
A First Look at the DataLearning the Latent FactorsCreating the DataLoadersCollaborative Filtering from ScratchWeight DecayCreating Our Own Embedding ModuleInterpreting Embeddings and BiasesUsing fastai.collabEmbedding DistanceBootstrapping a Collaborative Filtering ModelDeep Learning for Collaborative FilteringConclusionQuestionnaireFurther Research
9. Tabular Modeling Deep Dive
Categorical EmbeddingsBeyond Deep LearningThe DatasetKaggle CompetitionsLook at the DataDecision TreesHandling DatesUsing TabularPandas and TabularProcCreating the Decision TreeCategorical VariablesRandom ForestsCreating a Random ForestOut-of-Bag ErrorModel InterpretationTree Variance for Prediction ConfidenceFeature ImportanceRemoving Low-Importance VariablesRemoving Redundant FeaturesPartial DependenceData LeakageTree InterpreterExtrapolation and Neural NetworksThe Extrapolation ProblemFinding Out-of-Domain DataUsing a Neural NetworkEnsemblingBoostingCombining Embeddings with Other MethodsConclusionQuestionnaireFurther Research
10. NLP Deep Dive: RNNs
Text PreprocessingTokenizationWord Tokenization with fastaiSubword TokenizationNumericalization with fastaiPutting Our Texts into Batches for a Language ModelTraining a Text ClassifierLanguage Model Using DataBlockFine-Tuning the Language ModelSaving and Loading ModelsText GenerationCreating the Classifier DataLoadersFine-Tuning the ClassifierDisinformation and Language ModelsConclusionQuestionnaireFurther Research
11. Data Munging with fastai’s Mid-Level API
Going Deeper into fastai’s Layered APITransformsWriting Your Own TransformPipelineTfmdLists and Datasets: Transformed CollectionsTfmdListsDatasetsApplying the Mid-Level Data API: SiamesePairConclusionQuestionnaireFurther ResearchUnderstanding fastai’s Applications: Wrap Up
III. Foundations of Deep Learning
12. A Language Model from Scratch
The DataOur First Language Model from ScratchOur Language Model in PyTorchOur First Recurrent Neural NetworkImproving the RNNMaintaining the State of an RNNCreating More SignalMultilayer RNNsThe ModelExploding or Disappearing ActivationsLSTMBuilding an LSTM from ScratchTraining a Language Model Using LSTMsRegularizing an LSTMDropoutActivation Regularization and Temporal Activation RegularizationTraining a Weight-Tied Regularized LSTMConclusionQuestionnaireFurther Research
13. Convolutional Neural Networks
The Magic of ConvolutionsMapping a Convolutional KernelConvolutions in PyTorchStrides and PaddingUnderstanding the Convolution EquationsOur First Convolutional Neural NetworkCreating the CNNUnderstanding Convolution ArithmeticReceptive FieldsA Note About TwitterColor ImagesImproving Training StabilityA Simple BaselineIncrease Batch Size1cycle TrainingBatch NormalizationConclusionQuestionnaireFurther Research
14. ResNets
Going Back to ImagenetteBuilding a Modern CNN: ResNetSkip ConnectionsA State-of-the-Art ResNetBottleneck LayersConclusionQuestionnaireFurther Research
15. Application Architectures Deep Dive
Computer Visioncnn_learnerunet_learnerA Siamese NetworkNatural Language ProcessingTabularConclusionQuestionnaireFurther Research
16. The Training Process
Establishing a BaselineA Generic OptimizerMomentumRMSPropAdamDecoupled Weight DecayCallbacksCreating a CallbackCallback Ordering and ExceptionsConclusionQuestionnaireFurther ResearchFoundations of Deep Learning: Wrap Up
IV. Deep Learning from Scratch
17. A Neural Net from the Foundations
Building a Neural Net Layer from ScratchModeling a NeuronMatrix Multiplication from ScratchElementwise ArithmeticBroadcastingEinstein SummationThe Forward and Backward PassesDefining and Initializing a LayerGradients and the Backward PassRefactoring the ModelGoing to PyTorchConclusionQuestionnaireFurther Research
18. CNN Interpretation with CAM
CAM and HooksGradient CAMConclusionQuestionnaireFurther Research
19. A fastai Learner from Scratch
DataDatasetModule and ParameterSimple CNNLossLearnerCallbacksScheduling the Learning RateConclusionQuestionnaireFurther Research
20. Concluding Thoughts
A. Creating a Blog
Blogging with GitHub PagesCreating the RepositorySetting Up Your Home PageCreating PostsSynchronizing GitHub and Your ComputerJupyter for Blogging
B. Data Project Checklist
Data ScientistsStrategyDataAnalyticsImplementationMaintenanceConstraints
Index

Content preview from Deep Learning for Coders with fastai and PyTorch

Chapter 3. Data Ethics

As we discussed in Chapters 1 and 2, sometimes machine learning models can go wrong. They can have bugs. They can be presented with data that they haven’t seen before and behave in ways we don’t expect. Or they could work exactly as designed, but be used for something that we would much prefer they were never, ever used for.

Because deep learning is such a powerful tool and can be used for so many things, it becomes particularly important that we consider the consequences of our choices. The philosophical study of ethics is the study of right and wrong, including how we can define those terms, recognize right and wrong actions, and understand the connection between actions and consequences. The field of data ethics has been around for a long time, and many academics are focused on this field. It is being used to help define policy in many jurisdictions; it is being used in companies big and small to consider how best to ensure good societal outcomes from product development; and it is being used by researchers who want to make sure that the work they are doing is used for good, and not for bad.

As a deep learning practitioner, therefore, you will likely ...

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Build a Large Language Model (From Scratch)

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Deep Learning for Coders with fastai and PyTorch

by Jeremy Howard, Sylvain Gugger

Chapter 3. Data Ethics

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.