AI and Machine Learning for Coders: A Programmer's Guide to Artificial Intelligence

I’m a tad suspicious about listening to books that are too deep in the weeds with code. If they’re about programming concepts, audiobooks can be suitable, but if they involve code like this one, I like to have a physical picture of the lines of code. However, I was pleasantly surprised that this book conveyed many ideas despite communicating code aurally, too. Artificial intelligence (AI) and machine learning (ML) are huge topics today. I read this book just to supplement my broader knowledge, but while reading, I found a few applications that relate directly to my work. I’m going to try out TensorFlow, the software demoed here, in my work this week. I can see where it might be a lot more effective for my users than calling remote, server-based services from OpenAI.This book features the Python-based TensorFlow as the main framework. It shows how to use it to build your own large-language model (LLM). When I started this book, I didn’t realize that it’d also demonstrate how to use TensorFlow in web situations, which is my main domain. TensorFlowJS can use an LLM built in Python to perform features in a website. This book also describes how an API can house AI/ML models from TensorFlow. Mobile devices and embedded systems like Raspberry Pi receive their own chapter, too, due to their limited processing capabilities.Honestly, I did not have super-high expectations when I read this book. I just wanted to fill in my AI knowledge from a programmer’s perspective. I did not expect to apply the knowledge directly. Instead, I found my mind engaged with new ways to think about AI – even from a non-programmer’s perspective. I’m now able to better explain how to maximize AI use to my non-IT, scientific team. And I’m really chomping at the bit to see whether TensorFlowJS can meet the needs of my latest project. It’s a total joy when a book surprises with its intellectual depth and applicability. This book did both in spades, and I’m glad I listened to it!

I’d summarize this in a sentence as “an introduction to machine learning with TensorFlow,” TensorFlow being Google’s platform for building, training and distributing AI models.I enjoyed this book a lot more than than 100-Page Book on Machine Learning, because the examples and scenarios felt more practice and grounded in the kind of interface that I would have with AI/ML in the workplace. This book was more focused on tools than theory, and went into theoretical concepts when needed to demystify the parameters/API of various TensorFlow toolsets.So far, this has been my favorite introduction to the topic, and I have ready 3-5 different AI/ML books.

I have a subscription to online O'Reilly, so I can view the book. It is pretty good as far as having practical details. It is not like it is just a collection of code with no explanation. Usually Amazon shows the table of contents, but not for this one. So here it is:ForewordPrefaceWho Should Read This BookWhy I Wrote This BookNavigating This BookTechnology You Need to UnderstandOnline ResourcesConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgmentsI. Building Models1. Introduction to TensorFlowWhat Is Machine Learning?Limitations of Traditional ProgrammingFrom Programming to LearningWhat Is TensorFlow?Using TensorFlowInstalling TensorFlow in PythonUsing TensorFlow in PyCharmUsing TensorFlow in Google ColabGetting Started with Machine LearningSeeing What the Network LearnedSummary2. Introduction to Computer VisionRecognizing Clothing ItemsThe Data: Fashion MNISTNeurons for VisionDesigning the Neural NetworkThe Complete CodeTraining the Neural NetworkExploring the Model OutputTraining for Longer—Discovering OverfittingStopping TrainingSummary3. Going Beyond the Basics: Detecting Features in ImagesConvolutionsPoolingImplementing Convolutional Neural NetworksExploring the Convolutional NetworkBuilding a CNN to Distinguish Between Horses and HumansThe Horses or Humans DatasetThe Keras ImageDataGeneratorCNN Architecture for Horses or HumansAdding Validation to the Horses or Humans DatasetTesting Horse or Human ImagesImage AugmentationTransfer LearningMulticlass ClassificationDropout RegularizationSummary4. Using Public Datasets with TensorFlow DatasetsGetting Started with TFDSUsing TFDS with Keras ModelsLoading Specific VersionsUsing Mapping Functions for AugmentationUsing TensorFlow AddonsUsing Custom SplitsUnderstanding TFRecordThe ETL Process for Managing Data in TensorFlowOptimizing the Load PhaseParallelizing ETL to Improve Training PerformanceSummary5. Introduction to Natural Language ProcessingEncoding Language into NumbersGetting Started with TokenizationTurning Sentences into SequencesRemoving Stopwords and Cleaning TextWorking with Real Data SourcesGetting Text from TensorFlow DatasetsGetting Text from CSV FilesGetting Text from JSON FilesSummary6. Making Sentiment Programmable Using EmbeddingsEstablishing Meaning from WordsA Simple Example: Positives and NegativesGoing a Little Deeper: VectorsEmbeddings in TensorFlowBuilding a Sarcasm Detector Using EmbeddingsReducing Overfitting in Language ModelsUsing the Model to Classify a SentenceVisualizing the EmbeddingsUsing Pretrained Embeddings from TensorFlow HubSummary7. Recurrent Neural Networks for Natural Language ProcessingThe Basis of RecurrenceExtending Recurrence for LanguageCreating a Text Classifier with RNNsStacking LSTMsUsing Pretrained Embeddings with RNNsSummary8. Using TensorFlow to Create TextTurning Sequences into Input SequencesCreating the ModelGenerating TextPredicting the Next WordCompounding Predictions to Generate TextExtending the DatasetChanging the Model ArchitectureImproving the DataCharacter-Based EncodingSummary9. Understanding Sequence and Time Series DataCommon Attributes of Time SeriesTrendSeasonalityAutocorrelationNoiseTechniques for Predicting Time SeriesNaive Prediction to Create a BaselineMeasuring Prediction AccuracyLess Naive: Using Moving Average for PredictionImproving the Moving Average AnalysisSummary10. Creating ML Models to Predict SequencesCreating a Windowed DatasetCreating a Windowed Version of the Time Series DatasetCreating and Training a DNN to Fit the Sequence DataEvaluating the Results of the DNNExploring the Overall PredictionTuning the Learning RateExploring Hyperparameter Tuning with Keras TunerSummary11. Using Convolutional and Recurrent Methods for Sequence ModelsConvolutions for Sequence DataCoding ConvolutionsExperimenting with the Conv1D HyperparametersUsing NASA Weather DataReading GISS Data in PythonUsing RNNs for Sequence ModelingExploring a Larger DatasetUsing Other Recurrent MethodsUsing DropoutUsing Bidirectional RNNsSummaryII. Using Models12. An Introduction to TensorFlow LiteWhat Is TensorFlow Lite?Walkthrough: Creating and Converting a Model to TensorFlow LiteStep 1. Save the ModelStep 2. Convert and Save the ModelStep 3. Load the TFLite Model and Allocate TensorsStep 4. Perform the PredictionWalkthrough: Transfer Learning an Image Classifier and Converting to TensorFlow LiteStep 1. Build and Save the ModelStep 2. Convert the Model to TensorFlow LiteStep 3. Optimize the ModelSummary13. Using TensorFlow Lite in Android AppsWhat Is Android Studio?Creating Your First TensorFlow Lite Android AppStep 1. Create a New Android ProjectStep 2. Edit Your Layout FileStep 3. Add the TensorFlow Lite DependenciesStep 4. Add Your TensorFlow Lite ModelStep 5. Write the Activity Code to Use TensorFlow Lite for InferenceMoving Beyond “Hello World”—Processing ImagesTensorFlow Lite Sample AppsSummary14. Using TensorFlow Lite in iOS AppsCreating Your First TensorFlow Lite App with XcodeStep 1. Create a Basic iOS AppStep 2. Add TensorFlow Lite to Your ProjectStep 3. Create the User InterfaceStep 4. Add and Initialize the Model Inference ClassStep 5. Perform the InferenceStep 6. Add the Model to Your AppStep 7. Add the UI LogicMoving Beyond “Hello World”—Processing ImagesTensorFlow Lite Sample AppsSummary15. An Introduction to TensorFlow.jsWhat Is TensorFlow.js?Installing and Using the Brackets IDEBuilding Your First TensorFlow.js ModelCreating an Iris ClassifierSummary16. Coding Techniques for Computer Vision in TensorFlow.jsJavaScript Considerations for TensorFlow DevelopersBuilding a CNN in JavaScriptUsing Callbacks for VisualizationTraining with the MNIST DatasetRunning Inference on Images in TensorFlow.jsSummary17. Reusing and Converting Python Models to JavaScriptConverting Python-Based Models to JavaScriptUsing the Converted ModelsUsing Preconverted JavaScript ModelsUsing the Toxicity Text ClassifierUsing MobileNet for Image Classification in the BrowserUsing PoseNetSummary18. Transfer Learning in JavaScriptTransfer Learning from MobileNetStep 1. Download MobileNet and Identify the Layers to UseStep 2. Create Your Own Model Architecture with the Outputs from MobileNet as Its InputStep 3. Gather and Format the DataStep 4. Train the ModelStep 5. Run Inference with the ModelTransfer Learning from TensorFlow HubUsing Models from TensorFlow.orgSummary19. Deployment with TensorFlow ServingWhat Is TensorFlow Serving?Installing TensorFlow ServingInstalling Using DockerInstalling Directly on LinuxBuilding and Serving a ModelExploring Server ConfigurationSummary20. AI Ethics, Fairness, and PrivacyFairness in ProgrammingFairness in Machine LearningTools for FairnessThe What-If ToolFacetsFederated LearningStep 1. Identify Available Devices for TrainingStep 2. Identify Suitable Available Devices for TrainingStep 3. Deploy a Trainable Model to Your Training SetStep 4. Return the Results of the Training to the ServerStep 5. Deploy the New Master Model to the ClientsSecure Aggregation with Federated LearningFederated Learning with TensorFlow FederatedGoogle’s AI PrinciplesSummaryIndex

well written explanations and easy to understand

This explains the subject pretty well, and my disappointment is that i thought the technology was a lot more advanced.

Tonight, I completed reading of Laurence Moroney’s book “AI and Machine Learning for Coders”. I enjoyed this book from cover to cover, and I am especially thankful for a few things.First, this book did a great job of explaining how tensorflow can be used to solve different types of problems, such as computer vision, natural language processing, and time series forecasting. It even went into text generation, which got my creativity going.Second, this book is written for coders, but it does explain how neural networks work at a high level. It doesn’t drop a wall of math on the reader, which I appreciated. I feel much more comfortable now, after reading this.And finally, the book ends by discussing interpretability, bias/fairness, and Google’s AI principles. I found that to be a beautiful ending.I give this five stars. The few issues I had were trivial. This book is outstanding. I’m so much better off for having read it.