PyTorch Recipes

Is beginner friendly, explaining the step-by-step process for learning and understanding PyTorch Includes helpful tips and tricks for using PyTorch to train deep learning models Covers newer topics like distributed PyTorch, sci-kit learn compatibility, and deployment of PyTorch models

Autorentext

Pradeepta Mishra is the Director of AI, Fosfor at L&T Infotech (LTI), leading a large group of Data Scientists, computational linguistics experts, Machine Learning and Deep Learning experts in building the next-generation product, 'Leni,' the world's first virtual data scientist. He has expertise across core branches of Artificial Intelligence including Autonomous ML and Deep Learning pipelines, ML Ops, Image Processing, Audio Processing, Natural Language Processing (NLP), Natural Language Generation (NLG), design and implementation of expert systems, and personal digital assistants. In 2019 and 2020, he was named one of "India's Top "40Under40DataScientists" by Analytics India Magazine. Two of his books are translated into Chinese and Spanish based on popular demand. He delivered a keynote session at the Global Data Science conference 2018, USA. He has delivered a TEDx talk on "Can Machines Think?", available on the official TEDx YouTube channel. He has mentored more than 2000 data scientists globally. He has delivered 200+ tech talks on data science, ML, DL, NLP, and AI in various Universities, meetups, technical institutions, and community-arranged forums. He is a visiting faculty member to more than 10 universities, where he teaches deep learning and machine learning to professionals, and mentors them in pursuing a rewarding career in Artificial Intelligence.

Inhalt

Chapter 1: Introduction to PyTorch, Tensors, and Tensor Operations.- Chapter 2: Probability Distributions Using PyTorch.- Chapter 3: CNN and RNN Using PyTorch.- Chapter 4: Introduction to Neural Networks Using PyTorch.- Chapter 5: Supervised Learning Using PyTorch.- Chapter 6: Fine-Tuning Deep Learning Models Using PyTorch.- Chapter 7: Natural Language Processing Using PyTorch.- Chapter 8: Distributed PyTorch Modelling, Model Optimization and Deployment.- Chapter 9: Data Augmentation, Feature Engineering and Extractions for Image and Audio.- Chapter 10: PyTorch Model Interpretability and Interface to Sklearn.