“…Ahora bien, Microsoft, Nvidia o Epic Games no se quieren quedar atrás y también están apostando fuerte por el metaverso. …”

“…Ahora bien, Microsoft, Nvidia o Epic Games no se quieren quedar atrás y también están apostando fuerte por el metaverso. …”

“…Rapidly prototype and program new IoT and Edge solutions using low-cost Maker tech, such as those from Arduino, Raspberry Pi and Nvidia. With a focus on the electronics, this book allows experienced computer science students as well as researchers, practitioners, and even hobbyists to dive right into actual engineering of prototypes and not just theoretical programming and algorithms. …”

Tabla de Contenidos:

“…You'll review assembly language programming for the ARM Processor in 64-bit mode and write programs for a number of single board computers, including the Nvidia Jetson Nano and the Raspberry Pi (running 64-bit Linux). …”

Tabla de Contenidos: “…Implementing the Cucumber Beetle audio -- Implementing the Cucumber Man audio -- Introduction to Unity's lights and shadows -- Adding light sources -- Directional light -- Point light -- Spot light -- Area light -- Shadows -- Discovering Unity's special effects -- Particle System -- Trail Renderer -- Adding visual effects to our game -- Adding a Point light to our cherry trees -- Add a special effect using the Particle System -- Summary -- Chapter 13: Optimizing Our Game for Deployment -- Using the Profiler window -- Getting more out of the Profilers -- Optimizing scripts -- Optimized code example -- Optimizing graphics rendering -- Occlusion culling -- Lighting -- Mesh renderer -- Additional optimizations -- Level of detail -- Static colliders -- Creating builds -- Understanding the Unity build process -- Build settings -- PC, Mac, and Linux standalone -- iOS -- tvOS -- Android -- HTML 5/WebGL -- Facebook -- Xbox One -- PlayStation 4 and PlayStation Vita -- Player Settings -- Summary -- Chapter 14: Virtual Reality -- Welcome to virtual reality -- Development tools -- Oculus -- GearVR -- OpenVR -- PlayStation VR -- Enabling virtual reality in Unity -- Requisite SDKs -- Configuring your Unity project -- Recommendations from Unity technologies -- Starter content -- Oculus VR -- Oculus Sample Framework -- Oculus Stereo Shading Re-Projection Sample -- Oculus Integration -- Vive software -- Vive Stereo Rendering Toolkit -- Vive Input Utility -- Vive Media Decoder -- NVIDIA -- NVIDIA VRWorks -- NVIDIA VR Samples -- Unity Technologies -- Summary -- Other Books You May Enjoy -- Index…”

Tabla de Contenidos: “…1.6.2 Minimize Dynamically Changing the Path and Changing the Variable Class 1.6.3 Maintain a Balance Between the Code Readability and Optimization; 1.7 Examples; 2 Configurations for MATLAB and CUDA; 2.1 Chapter Objectives; 2.2 MATLAB Configuration for c-mex Programming; 2.2.1 Checklists; 2.2.1.1 C/C++ Compilers; 2.2.1.2 NVIDIA CUDA Compiler nvcc; 2.2.2 Compiler Selection; 2.3 "Hello, mex!" …”

Tabla de Contenidos: “…Examples of Sequential Data -- Exercise 9.01: Training an ANN for Sequential Data - Nvidia Stock Prediction -- Recurrent Neural Networks -- RNN Architecture -- Vanishing Gradient Problem -- Long Short-Term Memory Network -- Exercise 9.02: Building an RNN with an LSTM Layer - Nvidia Stock Prediction -- Activity 9.01: Building an RNN with Multiple LSTM Layers to Predict Power Consumption -- Natural Language Processing -- Data Preprocessing -- Dataset Cleaning -- Generating a Sequence and Tokenization -- Padding Sequences -- Back Propagation Through Time (BPTT) -- Exercise 9.03: Building an RNN with an LSTM Layer for Natural Language Processing -- Activity 9.02: Building an RNN for Predicting Tweets' Sentiment -- Summary -- Chapter 10: Custom TensorFlow Components -- Introduction -- TensorFlow APIs -- Implementing Custom Loss Functions -- Building a Custom Loss Function with the Functional API -- Building a Custom Loss Function with the Subclassing API -- Exercise 10.01: Building a Custom Loss Function -- Implementing Custom Layers -- Introduction to ResNet Blocks -- Building Custom Layers with the Functional API -- Building Custom Layers with Subclassing -- Exercise 10.02: Building a Custom Layer -- Activity 10.01: Building a Model with Custom Layers and a Custom Loss Function -- Summary -- Chapter 11: Generative Models -- Introduction -- Text Generation -- Extending NLP Sequence Models to Generate Text -- Dataset Cleaning -- Generating a Sequence and Tokenization -- Generating a Sequence of n-gram Tokens -- Padding Sequences -- Exercise 11.01: Generating Text -- Generative Adversarial Networks -- The Generator Network -- The Discriminator Network -- The Adversarial Network -- Combining the Generative and Discriminative Models -- Generating Real Samples with Class Labels -- Creating Latent Points for the Generator…”

Tabla de Contenidos: “…Leveraging FeedParser and Whisper to create searchable text -- Enhancing interactions and learning with Whisper -- Challenges of implementing real-time ASR using Whisper -- Implementing Whisper in customer service -- Advancing language learning with Whisper -- Optimizing the environment to deploy ASR solutions built using Whisper -- Introducing OpenVINO -- Applying OpenVINO Model Optimizer to Whisper -- Generating video subtitles using Whisper and OpenVINO -- Summary -- Chapter 7: Exploring Advanced Voice Capabilities -- Technical requirements -- Leveraging the power of quantization -- Quantizing Whisper with CTranslate2 and running inference with Faster-Whisper -- Quantizing Distil-Whisper with OpenVINO -- Facing the challenges and opportunities of real-time speech recognition -- Building a real-time ASR demo with Hugging Face Whisper -- Summary -- Chapter 8: Diarizing Speech with WhisperX and NVIDIA's NeMo -- Technical requirements -- Augmenting Whisper with speaker diarization -- Understanding the limitations and constraints of diarization -- Bringing transformers into speech diarization -- Introducing NVIDIA's NeMo framework -- Integrating Whisper and NeMo -- An introduction to speaker embeddings -- Differentiating NVIDIA's NeMo capabilities -- Performing hands-on speech diarization -- Setting up the environment -- Streamlining the diarization workflow with helper functions -- Separating music from speech using Demucs -- Transcribing audio using WhisperX -- Aligning the transcription with the original audio using Wav2Vec2 -- Using NeMo's MSDD model for speaker diarization -- Mapping speakers to sentences according to timestamps -- Enhancing speaker attribution with punctuation-based realignment -- Finalizing the diarization process -- Summary -- Chapter 9: Harnessing Whisper for Personalized Voice Synthesis -- Technical requirements…”

Tabla de Contenidos: “…Front Cover; CUDA Programming: A Developer's Guide to ParallelComputing with GPUs; Copyright; Contents; Preface; Chapter 1 - A Short History of Supercomputing; INTRODUCTION; VON NEUMANN ARCHITECTURE; CRAY; CONNECTION MACHINE; CELL PROCESSOR; MULTINODE COMPUTING; THE EARLY DAYS OF GPGPU CODING; THE DEATH OF THE SINGLE-CORE SOLUTION; NVIDIA AND CUDA; GPU HARDWARE; ALTERNATIVES TO CUDA; CONCLUSION; Chapter 2 - Understanding Parallelism with GPUs; INTRODUCTION; TRADITIONAL SERIAL CODE; SERIAL/PARALLEL PROBLEMS; CONCURRENCY; TYPES OF PARALLELISM; FLYNN'S TAXONOMY; SOME COMMON PARALLEL PATTERNS…”

Tabla de Contenidos: “…Intro -- Inhalt -- Einführung -- Teil I: PyTorch und neuronale Netze -- Kapitel 1: Grundlagen von PyTorch -- Google Colab -- PyTorch-Tensoren -- Automatische Gradienten mit PyTorch -- Berechnungsgraphen -- Lernziele -- Kapitel 2: Erstes neuronales Netz mit PyTorch -- Das MNIST-Bilddatensatz -- Die MNIST-Daten abrufen -- Ein Blick auf die Daten -- Ein einfaches neuronales Netz -- Das Training visualisieren -- Die Klasse für den MNIST-Datensatz -- Unsere Klassifizierer trainieren -- Das neuronale Netz abfragen -- Die Performance des Klassifizierers einfach ermitteln -- Kapitel 3: Verfeinerungen -- Verlustfunktion -- Aktivierungsfunktion -- Optimierungsmethode -- Normalisierung -- Kombinierte Verfeinerungen -- Lernziele -- Kapitel 4: Grundlagen von CUDA -- NumPy vs. Python -- NVIDIA CUDA -- CUDA in Python verwenden -- Lernziele -- Teil II: Generative Adversarial Networks erstellen -- Kapitel 5: Das GAN-Konzept -- Bilder generieren -- Gegnerisches Training -- Ein GAN trainieren -- GANs sind schwer zu trainieren -- Lernziele -- Kapitel 6: Einfache 1010-Muster -- Echte Datenquelle -- Den Diskriminator erstellen -- Den Diskriminator testen -- Den Generator erstellen -- Die Generatorausgabe überprüfen -- Das GAN trainieren -- Lernziele -- Kapitel 7: Handgeschriebene Ziffern -- Die Datensatzklasse -- Der MNIST-Diskriminator -- Den Diskriminator testen -- MNIST-Generator -- Die Generatorausgabe testen -- Das GAN trainieren -- Mode Collapse -- Das GAN-Training verbessern -- Mit Startwerten experimentieren -- Lernziele -- Kapitel 8: Menschliche Gesichter -- Farbbilder -- Der CelebA-Datensatz -- Hierarchisches Datenformat -- Die Daten abrufen -- Die Daten inspizieren -- Die Datensatzklasse -- Der Diskriminator -- Den Diskriminator testen -- GPU-Beschleunigung -- Der Generator -- Die Generatorausgabe überprüfen -- Das GAN trainieren -- Lernziele…”

Tabla de Contenidos: “…Multi-GPU Rendering on NVIDIA Quadro; V. Transfers; 28. Asynchronous Buffer Transfers; 29. …”

Tabla de Contenidos:

Tabla de Contenidos: “…Performance Increase by Frequency, and Its Limitations Superscalar Execution; VLIW; SIMD and Vector Processing; Hardware Multithreading; Multi-Core Architectures; Integration: Systems-on-Chip and the APU; Cache Hierarchies and Memory Systems; The architectural design space; CPU Designs; Low-Power CPUs; Mainstream Desktop CPUs; Intel Itanium 2; Niagara; GPU Architectures; Handheld GPUs; At the High End: AMD Radeon HD7970 and NVIDIA GTX580; APU and APU-Like Designs; Summary; References; Chapter 4: Basic OpenCL Examples; Introduction; Example Applications; Simple Matrix Multiplication Example…”

Tabla de Contenidos: “…; Set a Personal Default Theme; Tips for Users of NVIDIA Display Cards…”

Tabla de Contenidos: “…Single-Item ViewUsing Other Apps; Summary; Chapter 7 Music, Movies, and Games; Listening to Music on Your Tablet; Playing Stored Music; Using Other Music Apps; Watching Movies on Your Tablet; Watching Stored Movies on Your Tablet; Watching Streamed Movies on Your Tablet; Renting Google Play Movies; Viewing Hulu, Netflix, Blockbuster Videos, and More; Viewing Tablet Videos on Your TV; Playing Games on Your Tablet; Finding Games; Finding Play Store Games; Finding Nvidia Tegra Zone Games; Adding a Controller; Summary; Chapter 8 Using Your Android Tablet Wherever You Go…”

Tabla de Contenidos: “…Presentation of Existing Solutions; 3.1.1. NVIDIA Fermi GPU; 3.1.2. AMD ATI; 3.1.3. Cell Broadband Engine Architecture; 3.1.4. …”

Tabla de Contenidos: “…8.2.3.1 Object Recognition -- 8.2.3.2 Object Detection -- 8.2.3.3 Object Tracking -- 8.2.4 Edge Computing, Fog Computing, and Cloud Computing -- 8.2.4.1 Edge Computing -- 8.2.4.2 Fog Computing -- 8.2.4.3 Cloud Computing -- 8.2.5 Benefits of Computer Vision‐Driven Traffic Management -- 8.2.6 Challenges of Computer Vision‐Driven Traffic Management -- 8.2.6.1 Big Data Issues -- 8.2.6.2 Privacy Issues -- 8.2.6.3 Technical Barriers -- 8.3 Research Methodology -- 8.3.1 Research Questions and Objectives -- 8.3.2 Study Design -- 8.3.2.1 Selection Rationale -- 8.3.2.2 Potential Challenges -- 8.3.3 Adapted Study Design Research Approach -- 8.3.4 Selected Hardware and Software -- 8.3.4.1 Hardware: The NVIDIA Jetson Nano Developer Kit and Accompanying Items -- 8.3.5 Hardware Proposed -- 8.3.5.1 Software Stack: NVIDIA Jetpack SDK and Accompanying Requirements (All Iterations) -- 8.3.6 Software Proposed -- 8.4 Conclusion -- References -- Chapter 9 Implementation and Evaluation of Computer Vision Prototype for Vehicle Detection -- 9.1 Prototype Setup -- 9.1.1 Introduction -- 9.1.2 Environment Setup -- 9.2 Testing -- 9.2.1 Design and Development: The Default Model and the First Iteration -- 9.2.2 Testing (Multiple Images) -- 9.2.3 Analysis (Multiple Images) -- 9.2.4 Testing (MP4 File) -- 9.2.5 Testing (Livestream Camera) -- 9.3 Iteration 2: Transfer Learning Model -- 9.3.1 Design and Development -- 9.3.2 Test (Multiple Images) -- 9.3.3 Analysis (Multiple Images) -- 9.3.4 Test (MP4 File) -- 9.3.5 Analysis (MP4 File) -- 9.3.6 Test (Livestream Camera) -- 9.3.7 Analysis (Livestream Camera) -- 9.3.8 Redesign -- 9.4 Iteration 3: Increased Sample Size and Change of Accuracy Analysis (Images) -- 9.4.1 Design and Development -- 9.4.2 Testing -- 9.4.3 Analysis -- 9.4.3.1 Confusion Matrices -- 9.4.3.2 Precision, Recall, and F‐score -- 9.5 Findings and Discussion…”

Tabla de Contenidos: “…; 1.5 Our approach: Organization of the book; 1.6 Chapter Review; Chapter 2: Getting started; 2.1 Hardware Requirements; 2.2 Software requirements; 2.2.1 NVIDIA CUDA Toolkit; Windows; Linux…”

Tabla de Contenidos: