Tabla de Contenidos: “…Matricization -- 3.8. Subspaces associated with a tensor and multilinear rank -- 3.9. …”

Materias: “…Matrices…”

Tabla de Contenidos: “…6.7.3 Attention Mechanism Network -- 6.7.4 Graph Convolutional Neural Network -- 6.8 Hands-On of Automobile Fuel Consumption Prediction -- 6.8.1 Dataset -- 6.8.2 Create a Network -- 6.8.3 Training and Testing -- 6.9 References -- Chapter 7: Backward Propagation Algorithm -- 7.1 Derivatives and Gradients -- 7.2 Common Properties of Derivatives -- 7.2.1 Common Derivatives -- 7.2.2 Common Property of Derivatives -- 7.2.3 Hands-On Derivative Finding -- 7.3 Derivative of Activation Function -- 7.3.1 Derivative of Sigmoid Function -- 7.3.2 Derivative of ReLU Function -- 7.3.3 Derivative of LeakyReLU Function -- 7.3.4 Derivative of Tanh Function -- 7.4 Gradient of Loss Function -- 7.4.1 Gradient of Mean Square Error Function -- 7.4.2 Gradient of Cross-Entropy Function -- 7.5 Gradient of Fully Connected Layer -- 7.5.1 Gradient of a Single Neuron -- 7.5.2 Gradient of Fully Connected Layer -- 7.6 Chain Rule -- 7.7 Back Propagation Algorithm -- 7.8 Hands-On Optimization of Himmelblau -- 7.9 Hands-On Back Propagation Algorithm -- 7.9.1 Dataset -- 7.9.2 Network Layer -- 7.9.3 Network model -- 7.9.4 Network Training -- 7.9.5 Network Performance -- 7.10 References -- Chapter 8: Keras Advanced API -- 8.1 Common Functional Modules -- 8.1.1 Common Network Layer Classes -- 8.1.2 Network Container -- 8.2 Model Configuration, Training, and Testing -- 8.2.1 Model Configuration -- 8.2.2 Model Training -- 8.2.3 Model Testing -- 8.3 Model Saving and Loading -- 8.3.1 Tensor Method -- 8.3.2 Network Method -- 8.3.3 SavedModel method -- 8.4 Custom Network -- 8.4.1 Custom Network Layer -- 8.4.2 Customized Network -- 8.5 Model Zoo -- 8.5.1 Load Model -- 8.6 Metrics -- 8.6.1 Create a Metrics Container -- 8.6.2 Write Data -- 8.6.3 Read Statistical Data -- 8.6.4 Clear the Container -- 8.6.5 Hands-On Accuracy Metric -- 8.7 Visualization -- 8.7.1 Model Side -- 8.7.2 Browser Side…”

Tabla de Contenidos: “…-- Dropout -- Early Stopping -- Additional Methods -- Exercises -- References -- Chapter 5: Advanced Optimizers -- Available Optimizers in Keras in TensorFlow 2.5 -- Advanced Optimizers -- Exponentially Weighted Averages -- Momentum -- RMSProp -- Adam -- Comparison of the Optimizers' Performance -- Small Coding Digression -- Which Optimizer Should You Use?…”

“…En este trabajo estudiamos los tensores de tipo (0,2). Con este objetivo introduci- mos y desarrollamos el concepto de super espacio. …”
Enlace del recurso

Tabla de Contenidos: “…Networks for sequence data -- RNNs and LSTMs -- Building a better optimizer -- Gradient descent to ADAM -- Xavier initialization -- Summary -- References -- Chapter 4: Teaching Networks to Generate Digits -- The MNIST database -- Retrieving and loading the MNIST dataset in TensorFlow -- Restricted Boltzmann Machines: generating pixels with statistical mechanics -- Hopfield networks and energy equations for neural networks -- Modeling data with uncertainty with Restricted Boltzmann Machines -- Contrastive divergence: Approximating a gradient -- Stacking Restricted Boltzmann Machines to generate images: the Deep Belief Network -- Creating an RBM using the TensorFlow Keras layers API -- Creating a DBN with the Keras Model API -- Summary -- References -- Chapter 5: Painting Pictures with Neural Networks Using VAEs -- Creating separable encodings of images -- The variational objective -- The reparameterization trick -- Inverse Autoregressive Flow -- Importing CIFAR -- Creating the network from TensorFlow 2 -- Summary -- References -- Chapter 6: Image Generation with GANs -- The taxonomy of generative models -- Generative adversarial networks -- The generator model -- Training GANs -- Non-saturating generator cost -- Maximum likelihood game -- Vanilla GAN -- Improved GANs -- Deep Convolutional GAN -- Vector arithmetic -- Conditional GAN -- Wasserstein GAN -- Progressive GAN -- The overall method -- Progressive growth-smooth fade-in -- Minibatch standard deviation -- Equalized learning rate -- Pixelwise normalization -- TensorFlow Hub implementation -- Challenges -- Training instability -- Mode collapse -- Uninformative loss and evaluation metrics -- Summary -- References -- Chapter 7: Style Transfer with GANs -- Paired style transfer using pix2pix GAN -- The U-Net generator -- The Patch-GAN discriminator -- Loss -- Training pix2pix -- Use cases…”

“…Matrices, determinantes, vectores y tensores…”

“…"ML development often focuses on metrics, delaying work on deployment and scaling issues. …”

“…Fairness Indicators is a new feature built into TensorFlow Extended (TFX) and on top of TensorFlow Model Analysis. …”

“…This course builds on the training in Marvin Bertin's "Introduction to TensorFlow-Slim", which covered the basic concepts and uses of the TensorFlow-Slim (TF-Slim) API. …”

Tabla de Contenidos: “…Chapter 3: Monitoring Network Training Using TensorBoard -- A brief overview of TensorBoard -- Setting up TensorBoard -- Installing TensorBoard -- How TensorBoard talks to Keras/TensorFlow -- Running TensorBoard -- Connecting Keras to TensorBoard -- Introducing Keras callbacks -- Creating a TensorBoard callback -- Using TensorBoard -- Visualizing training -- Visualizing network graphs -- Visualizing a broken network -- Summary -- Chapter 4: Using Deep Learning to Solve Binary Classification Problems -- Binary classification and deep neural networks -- Benefits of deep neural networks -- Drawbacks of deep neural networks -- Case study - epileptic seizure recognition -- Defining our dataset -- Loading data -- Model inputs and outputs -- The cost function -- Using metrics to assess the performance -- Building a binary classifier in Keras -- The input layer -- The hidden layers -- What happens if we use too many neurons? …”

Tabla de Contenidos: “…Cover -- Copyright and Credits -- Packt Upsell -- Foreword -- Contributors -- Table of Contents -- Preface -- Chapter 1: Getting Started -- Understanding deep learning -- Perceptron -- Activation functions -- Sigmoid -- The hyperbolic tangent function -- The Rectified Linear Unit (ReLU) -- Artificial neural network (ANN) -- One-hot encoding -- Softmax -- Cross-entropy -- Dropout -- Batch normalization -- L1 and L2 regularization -- Training neural networks -- Backpropagation -- Gradient descent -- Stochastic gradient descent -- Playing with TensorFlow playground -- Convolutional neural network -- Kernel -- Max pooling -- Recurrent neural networks (RNN) -- Long short-term memory (LSTM) -- Deep learning for computer vision -- Classification -- Detection or localization and segmentation -- Similarity learning -- Image captioning -- Generative models -- Video analysis -- Development environment setup -- Hardware and Operating Systems - OS -- General Purpose - Graphics Processing Unit (GP-GPU) -- Computer Unified Device Architecture - CUDA -- CUDA Deep Neural Network - CUDNN -- Installing software packages -- Python -- Open Computer Vision - OpenCV -- The TensorFlow library -- Installing TensorFlow -- TensorFlow example to print Hello, TensorFlow -- TensorFlow example for adding two numbers -- TensorBoard -- The TensorFlow Serving tool -- The Keras library -- Summary -- Chapter 2: Image Classification -- Training the MNIST model in TensorFlow -- The MNIST datasets -- Loading the MNIST data -- Building a perceptron -- Defining placeholders for input data and targets -- Defining the variables for a fully connected layer -- Training the model with data -- Building a multilayer convolutional network -- Utilizing TensorBoard in deep learning -- Training the MNIST model in Keras -- Preparing the dataset -- Building the model…”

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Tabla de Contenidos: “…Data download and exploration -- Preparing the data for our models -- The binary classification model -- The regression model -- Improving the binary classification model -- The unreasonable effectiveness of data -- Summary -- Chapter 5: Image Classification Using Convolutional Neural Networks -- CNNs -- Convolutional layers -- Pooling layers -- Dropout -- Flatten layers, dense layers, and softmax -- Image classification using the MXNet library -- Base model (no convolutional layers) -- LeNet -- Classification using the fashion MNIST dataset -- References/further reading -- Summary -- Chapter 6: Tuning and Optimizing Models -- Evaluation metrics and evaluating performance -- Types of evaluation metric -- Evaluating performance -- Data preparation -- Different data distributions -- Data partition between training, test, and validation sets -- Standardization -- Data leakage -- Data augmentation -- Using data augmentation to increase the training data -- Test time augmentation -- Using data augmentation in deep learning libraries -- Tuning hyperparameters -- Grid search -- Random search -- Use case-using LIME for interpretability -- Model interpretability with LIME -- Summary -- Chapter 7: Natural Language Processing Using Deep Learning -- Document classification -- The Reuters dataset -- Traditional text classification -- Deep learning text classification -- Word vectors -- Comparing traditional text classification and deep learning -- Advanced deep learning text classification -- 1D convolutional neural network model -- Recurrent neural network model -- Long short term memory model -- Gated Recurrent Units model -- Bidirectional LSTM model -- Stacked bidirectional model -- Bidirectional with 1D convolutional neural network model -- Comparing the deep learning NLP architectures -- Summary -- Chapter 8: Deep Learning Models Using TensorFlow in R.…”

Tabla de Contenidos: “…-- The rise of Transformer 4.0 seamless APIs -- Choosing ready-to-use API-driven libraries -- Choosing a Transformer Model -- The role of Industry 4.0 artificial intelligence specialists -- Summary -- Questions -- References -- Chapter 2: Getting Started with the Architecture of the Transformer Model -- The rise of the Transformer: Attention is All You Need -- The encoder stack -- Input embedding -- Positional encoding -- Sublayer 1: Multi-head attention -- Sublayer 2: Feedforward network -- The decoder stack -- Output embedding and position encoding -- The attention layers -- The FFN sublayer, the post-LN, and the linear layer -- Training and performance -- Tranformer models in Hugging Face -- Summary -- Questions -- References -- Chapter 3: Fine-Tuning BERT Models -- The architecture of BERT -- The encoder stack -- Preparing the pretraining input environment -- Pretraining and fine-tuning a BERT model -- Fine-tuning BERT -- Hardware constraints -- Installing the Hugging Face PyTorch interface for BERT -- Importing the modules -- Specifying CUDA as the device for torch -- Loading the dataset -- Creating sentences, label lists, and adding BERT tokens -- Activating the BERT tokenizer -- Processing the data -- Creating attention masks -- Splitting the data into training and validation sets -- Converting all the data into torch tensors -- Selecting a batch size and creating an iterator -- BERT model configuration…”

Tabla de Contenidos: “…Cover -- Copyright -- Credits -- About the Authors -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Maths for Neural Networks -- Understanding linear algebra -- Environment setup -- Setting up the Python environment in Pycharm -- Linear algebra structures -- Scalars, vectors, and matrices -- Tensors -- Operations -- Vectors -- Matrices -- Matrix multiplication -- Trace operator -- Matrix transpose -- Matrix diagonals -- Identity matrix -- Inverse matrix -- Solving linear equations -- Singular value decomposition -- Eigenvalue decomposition -- Principal Component Analysis -- Calculus -- Gradient -- Hessian -- Determinant -- Optimization -- Optimizers -- Summary -- Chapter 2: Deep Feedforward Networks -- Defining feedforward networks -- Understanding backpropagation -- Implementing feedforward networks with TensorFlow -- Analyzing the Iris dataset -- Code execution -- Implementing feedforward networks with images -- Analyzing the effect of activation functions on the feedforward networks accuracy -- Summary -- Chapter 3: Optimization for Neural Networks -- What is optimization? …”

Tabla de Contenidos: “…Activity: Exploring the Bitcoin Dataset and Preparing Data for Model -- Using Keras as a TensorFlow Interface -- Model Components -- Activity: Creating a TensorFlow Model Using Keras -- From Data Preparation to Modeling -- Training a Neural Network -- Reshaping Time-Series Data -- Making Predictions -- Overfitting -- Activity: Assembling a Deep Learning System -- Summary -- Chapter 6: Model Evaluation and Optimization -- Model Evaluation -- Problem Categories -- Loss Functions, Accuracy, and Error Rates -- Different Loss Functions, Same Architecture -- Using TensorBoard -- Implementing Model Evaluation Metrics -- Evaluating the Bitcoin Model -- Overfitting -- Model Predictions -- Interpreting Predictions -- Activity:Creating an Active Training Environment -- Hyperparameter Optimization -- Layers and Nodes - Adding More Layers -- Adding More Nodes -- Layers and Nodes - Implementation -- Epochs -- Epochs - Implementation -- Activation Functions -- Linear (Identity) -- Hyperbolic Tangent (Tanh) -- Rectifid Linear Unit -- Activation Functions - Implementation -- Regularization Strategies -- L2 Regularization -- Dropout -- Regularization Strategies - Implementation -- Optimization Results -- Activity:Optimizing a Deep Learning Model -- Summary -- Chapter 7: Productization -- Handling New Data -- Separating Data and Model -- Data Component -- Model Component -- Dealing with New Data -- Re-Training an Old Model -- Training a New Model -- Activity: Dealing with New Data -- Deploying a Model as a Web Application -- Application Architecture and Technologies -- Deploying and Using Cryptonic -- Activity: Deploying a Deep Learning Application -- Summary -- Other Books You May Enjoy -- Index…”

Materias: