PyTorch Recipes: A Problem-Solution Approach to Build, Train and Deploy Neural Network Models - Softcover

Über die Autorin bzw. den Autor

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.

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You'll start by learning how to use tensors to develop and fine-tune neural network models and implement deep learning models such as LSTMs, and RNNs. Next, you'll explore probability distribution concepts using PyTorch, as well as supervised and unsupervised algorithms with PyTorch. This is followed by a deep dive on building models with convolutional neural networks, deep neural networks, and recurrent neural networks using PyTorch. This new edition covers also topics such as Scorch, a compatible module equivalent to the Scikit machine learning library, model quantization to reduce parameter size, and preparing a model for deployment within a production system. Distributed parallel processing for balancing PyTorch workloads, using PyTorch for image processing, audio analysis, and model interpretation are also covered in detail. Each chapter includes recipe code snippets to perform specific activities.