Materias:

Materias:

Materias:

Materias:

Tabla de Contenidos:

Tabla de Contenidos: “…7.2.1 Implementing the stem -- 7.2.2 Implementing Inception-ResNet type A block -- 7.2.3 Implementing the Inception-ResNet type B block -- 7.2.4 Implementing the reduction block -- 7.2.5 Putting everything together -- 7.2.6 Training Minception -- 7.3 If you can't beat them, join 'em: Using pretrained networks for enhancing performance -- 7.3.1 Transfer learning: Reusing existing knowledge in deep neural networks -- 7.4 Grad-CAM: Making CNNs confess -- Summary -- Answers to exercises -- 8 Telling things apart: Image segmentation -- 8.1 Understanding the data -- 8.2 Getting serious: Defining a TensorFlow data pipeline -- 8.2.1 Optimizing tf.data pipelines -- 8.2.2 The final tf.data pipeline -- 8.3 DeepLabv3: Using pretrained networks to segment images -- 8.3.1 A quick overview of the ResNet-50 model -- 8.3.2 Atrous convolution: Increasing the receptive field of convolution layers with holes -- 8.3.3 Implementing DeepLab v3 using the Keras functional API -- 8.3.4 Implementing the atrous spatial pyramid pooling module -- 8.3.5 Putting everything together -- 8.4 Compiling the model: Loss functions and evaluation metrics in image segmentation -- 8.4.1 Loss functions -- 8.4.2 Evaluation metrics -- 8.5 Training the model -- 8.6 Evaluating the model -- Summary -- Answers to exercises -- 9 Natural language processing with TensorFlow: Sentiment analysis -- 9.1 What the text? …”

Tabla de Contenidos: “…An overview -- How? Code example -- TensorFlow useful elements -- An autoencoder without the decoder -- Selecting layers -- Training only some layers -- Complete source -- Summary -- Chapter 8: Machine Learning Best Practices and Troubleshooting -- Building Machine Learning Systems -- Data Preparation -- Split of Train/Development/Test set -- Mismatch of the Dev and Test set -- When to Change Dev/Test Set -- Bias and Variance -- Data Imbalance -- Collecting more data -- Look at your performance metric -- Data synthesis/Augmentation -- Resample Data -- Loss function Weighting -- Evaluation Metrics -- Code Structure best Practice -- Singleton Pattern -- Recipe for CNN creation -- Summary -- Chapter 9: Training at Scale -- Storing data in TFRecords -- Making a TFRecord -- Storing encoded images -- Sharding -- Making efficient pipelines -- Parallel calls for map transformations -- Getting a batch -- Prefetching -- Tracing your graph -- Distributed computing in TensorFlow -- Model/data parallelism -- Synchronous/asynchronous SGD -- When data does not fit on one computer -- The advantages of NoSQL systems -- Installing Cassandra (Ubuntu 16.04) -- The CQLSH tool -- Creating databases, tables, and indexes…”

Tabla de Contenidos: “…-- 11.9 Graphing training metrics with TensorBoard -- 11.9.1 Running TensorBoard -- 11.9.2 Adding TensorBoard support to the metrics logging function -- 11.10 Why isn't the model learning to detect nodules? …”

Tabla de Contenidos: “…5.3.3 Narrowband MIMO Channel Sounding -- 5.4 Wideband Sounding: Correlative Sounding -- 5.4.1 ML-sequences -- 5.4.2 Cross-Correlation Using the FFT -- 5.4.3 Digital Matched Filters -- 5.5 Wideband Sounding: Sampled Spectrum Channel Sounding -- 5.6 Switched-array Architectures -- 5.7 Timing and Carrier Recovery -- 5.7.1 Digital Timing Recovery Methods -- 5.7.2 Phase Recovery Using a Decision Directed Feedback Loop -- 5.8 Summary and Discussion -- 5.9 Notes and References -- Chapter 6: Experimental Verifications -- 6.1 Validation Metrics -- 6.1.1 Channel Capacity -- 6.1.2 The Diversity and Correlation Metrics -- 6.1.3 The Demmel Condition Number -- 6.1.4 The Environmental Characterization Metric -- 6.1.5 Correlation Matrix Difference Metric -- 6.2 WMSDR Experimental Setup -- 6.2.1 Terminology -- 6.2.2 Measurement Description -- 6.3 BYU Wideband Channel Sounder Experimental Setup -- 6.3.1 BYU Transmitter Set -- 6.3.2 BYU Receiver Set -- 6.3.3 Measurement Description -- 6.4 Experimental Results -- 6.4.1 Capacity Measure: Methodology -- 6.4.2 Results: MIMO APS and Spatial Structure -- 6.4.3 Results: Wideband Correlation Matrices -- 6.5 Discussion -- 6.5.1 Accuracy of the Results -- 6.5.2 Sources of Error -- 6.6 Summary and Discussion -- 6.7 Notes and References -- Appendix A: An Introduction to Tensor Algebra -- Appendix B: Proof of Theorems from Chapter 3 -- Appendix C: COST 273 Model Summary -- Glossary -- Bibliography -- Index…”

“…You will start by exploring tensor handling, automatic gradient calculation with autograd, and the fundamentals of PyTorch model training. …”

Tabla de Contenidos: “…Grouping data -- Single-column grouping -- Double-column grouping -- Iterating through grouped data -- Iterating through single- and double-column grouped data -- Using the .apply method -- Data aggregation of grouped data -- Data aggregation on single-column grouping -- Data aggregation on double-column grouping -- A simple application of groupby on real data -- Summary -- Section 3: Building Data-Driven Applications -- Chapter 8: Creating a No-Code Data Analysis/Handling System -- Technical requirements -- Setting up the project environment -- Structuring and designing the app -- App layout and the DataTable component -- Implementing DataTable components -- File upload and state management -- Creating different DataFrame operation components -- Implementing the Describe component -- Implementing the Query component -- Implementing the Df2df component -- Implementing the Arithmetic component -- Implementing the chart component -- Implementing the ChartPlane component -- Implementing the ChartViz component -- Integrating ChartViz and ChartPlane into App.js -- Summary -- Chapter 9: Basics of Machine Learning -- Technical requirements -- Introduction to machine learning -- A simple analogy of a machine learning system -- Why machine learning works -- Objective functions -- Evaluation metrics -- Machine learning problems/tasks -- Supervised learning -- Unsupervised learning -- Machine learning in JavaScript -- Applications of machine learning -- Resources to understand machine learning in depth -- Summary -- Chapter 10: Introduction to TensorFlow.js -- Technical requirements -- What is TensorFlow.js? …”

Tabla de Contenidos: “…Logics for extensional, locally complete analysis via domain refinements -- Clustered Relational Thread-Modular Abstract Interpretation with Local Traces -- Adversarial Reachability for Program-level Security Analysis -- Automated Grading of Regular Expressions -- Builtin Types viewed as Inductive Families -- Pragmatic Gradual Polymorphism with References -- Modal crash types for intermittent computing -- Gradual Tensor Shape Checking -- A Type System for Effect Handlers and Dynamic Labels -- Interpreting Knowledge-based Programs -- Contextual Modal Type Theory with Polymorphic Contexts -- A Complete Inference System for Skip-free Guarded Kleene Algebra with Tests -- Quorum Tree Abstractions of Consensus Protocols -- MAG π : Types for Failure-Prone Communication -- System $Fˆ\mu \omega$ with Context-free Session Types -- Safe Session-Based Concurrency with Shared Linear State -- Bunched Fuzz: Sensitivity for Vector Metrics -- Fast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing -- Type-safe Quantum Programming in Idris -- Automatic Alignment in Higher-Order Probabilistic Programming Languages…”

Tabla de Contenidos: “…Best Practices for Removing Sensitive Data -- Summary -- Exam Essentials -- Review Questions -- Chapter 7 Model Building -- Choice of Framework and Model Parallelism -- Data Parallelism -- Model Parallelism -- Modeling Techniques -- Artificial Neural Network -- Deep Neural Network (DNN) -- Convolutional Neural Network -- Recurrent Neural Network -- What Loss Function to Use -- Gradient Descent -- Learning Rate -- Batch -- Batch Size -- Epoch -- Hyperparameters -- Transfer Learning -- Semi-supervised Learning -- When You Need Semi-supervised Learning -- Limitations of SSL -- Data Augmentation -- Offline Augmentation -- Online Augmentation -- Model Generalization and Strategies to Handle Overfitting and Underfitting -- Bias Variance Trade-Off -- Underfitting -- Overfitting -- Regularization -- Summary -- Exam Essentials -- Review Questions -- Chapter 8 Model Training and Hyperparameter Tuning -- Ingestion of Various File Types into Training -- Collect -- Process -- Store and Analyze -- Developing Models in Vertex AI Workbench by Using Common Frameworks -- Creating a Managed Notebook -- Exploring Managed JupyterLab Features -- Data Integration -- BigQuery Integration -- Ability to Scale the Compute Up or Down -- Git Integration for Team Collaboration -- Schedule or Execute a Notebook Code -- Creating a User-Managed Notebook -- Training a Model as a Job in Different Environments -- Training Workflow with Vertex AI -- Training Dataset Options in Vertex AI -- Pre-built Containers -- Custom Containers -- Distributed Training -- Hyperparameter Tuning -- Why Hyperparameters Are Important -- Techniques to Speed Up Hyperparameter Optimization -- How Vertex AI Hyperparameter Tuning Works -- Vertex AI Vizier -- Tracking Metrics During Training -- Interactive Shell -- TensorFlow Profiler -- What-If Tool -- Retraining/Redeployment Evaluation -- Data Drift…”

Tabla de Contenidos: “…-- Discover a world of opportunities with Google Translate -- Getting started -- The program -- The header -- Implementing Google's translation service -- Google Translate from a linguist's perspective -- Playing with the tool -- Linguistic assessment of Google Translate -- Lexical field theory -- Jargon -- Translating is not just translating but interpreting -- How to check a translation -- AI as a new frontier -- Lexical field and polysemy -- Exploring the frontier - the program -- k-nearest neighbor algorithm -- The KNN algorithm -- The knn_polysemy.py program -- Implementing the KNN compressed function in Google_Translate_Customized.py -- Conclusions on the Google Translate customized experiment -- The disruptive revolutionary loop -- Summary -- Questions -- Further reading -- Chapter 9: Getting Your Neurons to Work -- Technical requirements -- Defining a CNN -- Defining a CNN -- Initializing the CNN -- Adding a 2D convolution -- Kernel -- Intuitive approach -- Developers' approach -- Mathematical approach -- Shape -- ReLu -- Pooling -- Next convolution and pooling layer -- Flattening -- Dense layers -- Dense activation functions -- Training a CNN model -- The goal -- Compiling the model -- Loss function -- Quadratic loss function -- Binary cross-entropy -- Adam optimizer -- Metrics -- Training dataset -- Data augmentation -- Loading the data -- Testing dataset -- Data augmentation -- Loading the data -- Training with the classifier -- Saving the model -- Next steps -- Summary -- Questions -- Further reading and references -- Chapter 10: Applying Biomimicking to Artificial Intelligence -- Technical requirements -- Human biomimicking -- TensorFlow, an open source machine learning framework -- Does deep learning represent our brain or our mind? …”

Tabla de Contenidos: “…Authentication and authorization -- Data governance -- Data lineage -- Other data governance measures -- Hands-on exercise - data management for ML -- Creating a data lake using Lake Formation -- Creating a data ingestion pipeline -- Creating a Glue Data Catalog -- Discovering and querying data in the data lake -- Creating an Amazon Glue ETL job to process data for ML -- Building a data pipeline using Glue workflows -- Summary -- Chapter 5: Exploring Open-Source ML Libraries -- Technical requirements -- Core features of open-source ML libraries -- Understanding the scikit-learn ML library -- Installing scikit-learn -- Core components of scikit-learn -- Understanding the Apache Spark ML library -- Installing Spark ML -- Core components of the Spark ML library -- Understanding the TensorFlow deep learning library -- Installing TensorFlow -- Core components of TensorFlow -- Hands-on exercise - training a TensorFlow model -- Understanding the PyTorch deep learning library -- Installing PyTorch -- Core components of PyTorch -- Hands-on exercise - building and training a PyTorch model -- How to choose between TensorFlow and PyTorch -- Summary -- Chapter 6: Kubernetes Container Orchestration Infrastructure Management -- Technical requirements -- Introduction to containers -- Overview of Kubernetes and its core concepts -- Namespaces -- Pods -- Deployment -- Kubernetes Job -- Kubernetes custom resources and operators -- Services -- Networking on Kubernetes -- Security and access management -- API authentication and authorization -- Hands-on - creating a Kubernetes infrastructure on AWS -- Problem statement -- Lab instruction -- Summary -- Chapter 7: Open-Source ML Platforms -- Core components of an ML platform -- Open-source technologies for building ML platforms -- Implementing a data science environment -- Building a model training environment…”

Tabla de Contenidos: “…Denoising autoencoders -- An example of a denoising autoencoder with TensorFlow -- Sparse autoencoders -- Adding sparseness to the Fashion MNIST deep convolutional autoencoder -- Variational autoencoders -- An example of a variational autoencoder with TensorFlow -- Summary -- Chapter 12: Generative Adversarial Networks -- Adversarial training -- Example of DCGAN with TensorFlow -- Wasserstein GAN (WGAN) -- Example of WGAN with TensorFlow -- Summary -- Chapter 13: Deep Belief Networks -- MRF -- RBMs -- DBNs -- Example of unsupervised DBN in Python -- Example of Supervised DBN with Python -- Summary -- Chapter 14: Introduction to Reinforcement Learning -- Reinforcement Learning fundamentals -- Environment -- Rewards -- Checkerboard environment in Python -- Policy -- Policy iteration -- Policy iteration in the checkerboard environment -- Value iteration -- Value iteration in the checkerboard environment -- TD(0) algorithm -- TD(0) in the checkerboard environment -- Summary -- Chapter 15: Advanced Policy Estimation Algorithms -- TD(λ) algorithm -- TD(λ) in a more complex Checkerboard environment -- Actor-Critic TD(0) in the checkerboard environment -- SARSA algorithm -- SARSA in the checkerboard environment -- Q-learning -- Q-learning in the checkerboard environment -- Q-learning using a neural network -- Summary -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos: “…Applying the FNN XOR function to optimizing subsets of data -- Summary -- Questions -- Further reading -- Chapter 9: Abstract Image Classification with Convolutional Neural Networks (CNNs) -- Introducing CNNs -- Defining a CNN -- Initializing the CNN -- Adding a 2D convolution layer -- Kernel -- Shape -- ReLU -- Pooling -- Next convolution and pooling layer -- Flattening -- Dense layers -- Dense activation functions -- Training a CNN model -- The goal -- Compiling the model -- The loss function -- The Adam optimizer -- Metrics -- The training dataset -- Data augmentation -- Loading the data -- The testing dataset -- Data augmentation on the testing dataset -- Loading the data -- Training with the classifier -- Saving the model -- Next steps -- Summary -- Questions -- Further reading and references -- Chapter 10: Conceptual Representation Learning -- Generating profit with transfer learning -- The motivation behind transfer learning -- Inductive thinking -- Inductive abstraction -- The problem AI needs to solve -- The gap concept -- Loading the trained TensorFlow 2.x model -- Loading and displaying the model -- Loading the model to use it -- Defining a strategy -- Making the model profitable by using it for another problem -- Domain learning -- How to use the programs -- The trained models used in this section -- The trained model program -- Gap - loaded or underloaded -- Gap - jammed or open lanes -- Gap datasets and subsets -- Generalizing the (the gap conceptual dataset) -- The motivation of conceptual representation learning metamodels applied to dimensionality -- The curse of dimensionality -- The blessing of dimensionality -- Summary -- Questions -- Further reading -- Chapter 11: Combining Reinforcement Learning and Deep Learning -- Planning and scheduling today and tomorrow -- A real-time manufacturing process…”

Tabla de Contenidos: “…CPU -- 7.1.2 Downloading the clothing dataset -- 7.1.3 TensorFlow and Keras -- 7.1.4 images -- 7.2 Convolutional neural networks -- 7.2.1 Using a pretrained model -- 7.2.2 Getting predictions -- 7.3 Internals of the model -- 7.3.1 Convolutional layers -- 7.3.2 Dense layers -- 7.4 Training the model -- 7.4.1 Transfer learning -- 7.4.2 Loading the data -- 7.4.3 Creating the model -- 7.4.4 Training the model -- 7.4.5 Adjusting the learning rate -- 7.4.6 Saving the model and checkpointing -- 7.4.7 Adding more layers -- 7.4.8 Regularization and dropout -- 7.4.9 Data augmentation -- 7.4.10 Training a larger model -- 7.5 Using the model -- 7.5.1 Loading the model -- 7.5.2 Evaluating the model -- 7.5.3 Getting the predictions -- 7.6 Next steps -- 7.6.1 Exercises -- 7.6.2 Other projects -- Summary -- Answers to exercises -- 8 Serverless deep learning -- 8.1 Serverless: AWS Lambda -- 8.1.1 TensorFlow Lite -- 8.1.2 Converting the model to TF Lite format -- 8.1.3 Preparing the images -- 8.1.4 Using the TensorFlow Lite model -- 8.1.5 Code for the lambda function -- 8.1.6 Preparing the Docker image -- 8.1.7 Pushing the image to AWS ECR -- 8.1.8 Creating the lambda function -- 8.1.9 Creating the API Gateway -- 8.2 Next steps -- 8.2.1 Exercises -- 8.2.2 Other projects -- Summary -- 9 Serving models with Kubernetes and Kubeflow -- 9.1 Kubernetes and Kubeflow -- 9.2 Serving models with TensorFlow Serving -- 9.2.1 Overview of the serving architecture -- 9.2.2 The saved_model format -- 9.2.3 Running TensorFlow Serving locally -- 9.2.4 Invoking the TF Serving model from Jupyter…”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Table of Contents -- Preface -- Chapter 1: From Data to Decisions - Getting Started with Analytic Applications -- Designing an advanced analytic solution -- Data layer: warehouses, lakes, and streams -- Modeling layer -- Deployment layer -- Reporting layer -- Case study: sentiment analysis of social media feeds -- Data input and transformation -- Sanity checking -- Model development -- Scoring -- Visualization and reporting -- Case study: targeted e-mail campaigns -- Data input and transformation -- Sanity checking -- Model development -- Scoring -- Visualization and reporting -- Summary -- Chapter 2: Exploratory Data Analysis and Visualization in Python -- Exploring categorical and numerical data in IPython -- Installing IPython notebook -- The notebook interface -- Loading and inspecting data -- Basic manipulations - grouping, filtering, mapping, and pivoting -- Charting with Matplotlib -- Time series analysis -- Cleaning and converting -- Time series diagnostics -- Joining signals and correlation -- Working with geospatial data -- Loading geospatial data -- Working in the cloud -- Introduction to PySpark -- Creating the SparkContext -- Creating an RDD -- Creating a Spark DataFrame -- Summary -- Chapter 3: Finding Patterns in the Noise - Clustering and Unsupervised Learning -- Similarity and distance metrics -- Numerical distance metrics -- Correlation similarity metrics and time series -- Similarity metrics for categorical data -- K-means clustering -- Affinity propagation - automatically choosing cluster numbers -- k-medoids -- Agglomerative clustering -- Where agglomerative clustering fails -- Streaming clustering in Spark -- Summary -- Chapter 4: Connecting the Dots with Models - Regression Methods -- Linear regression -- Data preparation…”

“…The first two chapters cover much of the more advanced background material on Linear Algebra (like dual spaces, multilinear functions and tensor products.) Chapter 3 gives an ab initio exposition of the basic results concerning the topology of metric spaces, particularly of normed linear spaces.The last chapter deals with miscellaneous applications of the Differential Calculus including an introduction to the Calculus of Variations. …”