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

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.

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

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

Met and exceeded my expectations. Great walkthroughs and explanations on coding machine learning into different environment (using TensorFlow). Also good starter discussion on ML techniques. I especially liked the mx+b example of neural networks, very intuitive. Answered lots of questions I had about using my models, such as with JavaScript or using transfer learning.The last chapter on ethics / federated learning felt a little short (but the fact it was there was already good). I would have liked a little more on dealing with / identifying model drift and training on new data while not overwriting the weights of the old. Essentially more lifecycle stuff. Discussion of ML model deployment was all TensorFlow but like I really enjoyed it all.

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.

Laurence is the teacher you always wish you had. He covers every aspect of TensorFlow with deep yet tangible intelligence. If you're worried about understanding the practice of machine learning and artificial intelligence, don't worry any longer. This book guides you through various aspects of the TensorFlow framework, but it also leans into the varying aspects of popular AI problems.You get the full tour in this book, from NLP coding to embedding your models in edge devices. Each section is overflowing with code samples to help you solidify what you're learning.10 out of 10, a cornerstone of any AI bookshelf.

If you start with this book, watch Laurence's videos on the TensorFlow/ Google Developer youtube channel and work all the examples and exercises, you will have a solid foundation in deep learning. Then you could potentially learn more from the documentation itself or by using Aurélien Géron's book.I feel other Oreilly books should be like this.Although math is almost absent in this book (hence 'for coders'), Laurence does a great job explaining convolutions and maxpooling etc.. I've seen lectures from many sources that misattribute pixel/dimension reduction to the convolution step and not the pooling layer (worse yet, lump both together) but the author clearly explains the concepts.

If you like to learn new frameworks or technologies by tinkering with practical examples, this book is excellent. It provides great coverage of data preparation and the creation, training, testing, and evaluation of models for computer vision, natural language, and time series data using Tensorflow and the Keras Sequential API.

El contenido del libro es muy bueno para iniciarse en deep learning, pero no hay algún indicador de que el libro es escala de grises y no a color

Das Buch reißt gleich den Inhalt von mehreren Cousera-Kursen von Deeplearning-AI an und ist gut zu lesen, mit vielen Code-Beispielen. Für mich war es eine gute Zusammenfassung der absolvierten Kurse, die größten Teils vom Auto gehaltenwerden.

Based on the book's description, I expected an introduction to the principles and algorithms behind AI and how to turn them into usable code. Instead, this book offers little more than a lengthy tutorial on how to use a pre-existing API called TensorFlow.It's an absolutely fine book for people who want to learn how to use TensorFlow, but a bit misleading, since as a developer I would like to know exactly what my code does, math an all, and I prefer to rely as little as possible on existing frameworks.It seems to want to appeal to a different kind of programmer.

Ho chiesto la sostituzione poichè la copia aquistata (51€) era in bianco/nero..... arriva la sostituzione anch'essa in bianco/nero...... solo tempo perso.Come si fa a stampare libri tecnici in bianco/nero?