Tabla de Contenidos: “…Adding noise to images using diffusers -- Defining the UNet model -- Training the UNet model -- Defining the optimizer and learning schedule -- Using Hugging Face Accelerate to accelerate training -- Running the model training loop -- Generating realistic anime images using (reverse) diffusion -- Understanding text-to-image generation using diffusion -- Encoding text input into an embedding vector -- Ingesting additional text data in the (conditional) UNet model -- Using the Stable Diffusion model to generate images from text -- Summary -- Reference list -- Chapter 11: Deep Reinforcement Learning -- Reviewing RL concepts -- Types of RL algorithms -- Model-based -- Model-Free -- Discussing Q-learning -- Understanding deep Q-learning -- Using two separate DNNs -- Experience replay buffer -- Building a DQN model in PyTorch -- Initializing the main and target CNN models -- Defining the experience replay buffer -- Setting up the environment -- Defining the CNN optimization function -- Managing and running episodes -- Training the DQN model to learn Pong -- Summary -- Reference list -- Chapter 12: Model Training Optimizations -- Distributed training with PyTorch -- Training the MNIST model in a regular fashion -- Training the MNIST model in a distributed fashion -- Distributed training on GPUs with CUDA -- Automatic mixed precision training -- Regular model training on a GPU -- Mixed precision training on a GPU -- Summary -- Reference list -- Chapter 13: Operationalizing PyTorch Models into Production -- Model serving in PyTorch -- Creating a PyTorch model inference pipeline -- Saving and loading a trained model -- Building the inference pipeline -- Building a basic model server -- Writing a basic app using Flask -- Using Flask to build our model server -- Setting up model inference for Flask serving -- Building a Flask app to serve model…”

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