◆ 출판사 ◆

Packt Publishing


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◆ 목차 ◆

Preface Chapter 1: Overview of Deep Learning Using PyTorch A refresher on deep learning Optimization schedule Exploring the PyTorch library in contrast to TensorFlow Summary Reference list Chapter 2: Deep CNN Architectures Why are CNNs so powerful? Evolution of CNN architectures Developing LeNet from scratch Fine-tuning the AlexNet model Running a pretrained VGG model Exploring GoogLeNet and Inception v3 Discussing ResNet and DenseNet architectures Understanding EfficientNets and the future of CNN architectures Summary References Chapter 3: Combining CNNs and LSTMs Building a neural network with CNNs and LSTMs Building an image caption generator using PyTorch Summary References Chapter 4: Deep Recurrent Model Architectures Exploring the evolution of recurrent networks Training RNNs for sentiment analysis Building a bidirectional LSTM Discussing GRUs and attention-based models Summary References Chapter 5: Advanced Hybrid Models Building a transformer model for language modeling Developing a RandWireNN model from scratch Summary References Chapter 6: Graph Neural Networks Introduction to GNNs Types of graph learning tasks Reviewing prominent GNN models Training a GAT model with PyTorch Geometric Summary Reference list Chapter 7: Music and Text Generation with PyTorch Building a transformer-based text generator with PyTorch Using GPT models as text generators Generating MIDI music with LSTMs using PyTorch Summary References Chapter 8: Neural Style Transfer Understanding how to transfer style between images Implementing neural style transfer using PyTorch Summary References Chapter 9: Deep Convolutional GANs Defining the generator and discriminator networks Training a DCGAN using PyTorch Using GANs for style transfer Summary References Chapter 10: Image Generation Using Diffusion Understanding image generation using diffusion Training a diffusion model for image generation Understanding text-to-image generation using diffusion Using the Stable Diffusion model to generate images from text Summary Reference list Chapter 11: Deep Reinforcement Learning Reviewing RL concepts Discussing Q-learning Understanding deep Q-learning Building a DQN model in PyTorch Summary Reference list Chapter 12: Model Training Optimizations Distributed training with PyTorch Distributed training on GPUs with CUDA Summary Reference list Chapter 13: Operationalizing PyTorch Models into Production Model serving in PyTorch Building a basic model server Creating a model microservice Serving a PyTorch model using TorchServe Exporting universal PyTorch models using TorchScript and ONNX Running a PyTorch model in C++ Using ONNX to export PyTorch models Serving PyTorch models in the cloud Summary Chapter 14: PyTorch on Mobile Devices Deploying a PyTorch model on Android Using the phone camera in the Android app to capture images Building PyTorch apps on iOS Summary Reference list Chapter 15: Rapid Prototyping with PyTorch Using fastai to set up model training in a few minutes Training models on any hardware using PyTorch Lightning Profiling MNIST model inference using PyTorch Profiler Summary Reference list Chapter 16: PyTorch and AutoML Finding the best neural architectures with AutoML Using Optuna for hyperparameter search Summary Reference list Chapter 17: PyTorch and Explainable AI Model interpretability in PyTorch Using Captum to interpret models Summary Reference List Chapter 18: Recommendation Systems with PyTorch Using deep learning for recommendation systems Understanding and processing the MovieLens dataset Training and evaluating a recommendation system model Building a recommendation system using the trained model Summary Reference list Chapter 19: PyTorch and Hugging Face Understanding Hugging Face within the PyTorch context Using the Hugging Face Hub for pre-trained models Using the Hugging Face Datasets library with PyTorch Using Accelerate to speed up PyTorch model training Using Optimum to optimize PyTorch model deployment Summary Reference list Index


◆ 저자소개 ◆

Jha, Ashish Ranjan
저자 : Jha, Ashish Ranjan Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), a master's degree in Computer Science from EPFL (Switzerland), and an MBA degree from Quantic School of Business (Washington). He has received a distinction in all 3 of his degrees. He has worked for large technology companies, including Oracle and Sony as well as the more recent tech unicorns such as Revolut, mostly focused on artificial intelligence. He currently works as a machine learning engineer. Ashish has worked on a range of products and projects, from developing an app that uses sensor data to predict the mode of transport to detecting fraud in car damage insurance claims. Besides being an author, machine learning engineer, and data scientist, he also blogs frequently on his personal blog site about the latest research and engineering topics around machine learning.