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Mastering PyTorch

You're reading from  Mastering PyTorch

Product type Book
Published in Feb 2021
Publisher Packt
ISBN-13 9781789614381
Pages 450 pages
Edition 1st Edition
Languages
Concepts
Author (1):
Ashish Ranjan Jha Ashish Ranjan Jha
Profile icon Ashish Ranjan Jha
LinkedIn
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.
See other products by Ashish Ranjan Jha

Table of Contents (20) Chapters

Preface Section 1: PyTorch Overview
Chapter 1: Overview of Deep Learning using PyTorch Chapter 2: Combining CNNs and LSTMs Section 2: Working with Advanced Neural Network Architectures
Chapter 3: Deep CNN Architectures Chapter 4: Deep Recurrent Model Architectures Chapter 5: Hybrid Advanced Models Section 3: Generative Models and Deep Reinforcement Learning
Chapter 6: Music and Text Generation with PyTorch Chapter 7: Neural Style Transfer Chapter 8: Deep Convolutional GANs Chapter 9: Deep Reinforcement Learning Section 4: PyTorch in Production Systems
Chapter 10: Operationalizing PyTorch Models into Production Chapter 11: Distributed Training Chapter 12: PyTorch and AutoML Chapter 13: PyTorch and Explainable AI Chapter 14: Rapid Prototyping with PyTorch Other Books You May Enjoy

Using Optuna for hyperparameter search

Optuna is one of the hyperparameter search tools that supports PyTorch. You can read in detail about the search strategies used by the tool, such as TPE (Tree-Structured Parzen Estimation) and CMA-ES (Covariance Matrix Adaptation Evolution Strategy) in the Optuna paper, at https://arxiv.org/pdf/1907.10902.pdf. Besides the advanced hyperparameter search methodologies, the tool provides a sleek API, which we will explore in a moment.

Tool citation

Optuna: A Next-Generation Hyperparameter Optimization Framework.

Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama (2019, in KDD).

In this section, we will once again build and train the MNIST model, this time using Optuna to figure out the optimal hyperparameter setting. We will discuss important parts of the code step by step, in the form of an exercise. The full code can be found here:

https://github.com/PacktPublishing/Mastering-PyTorch/blob/master/Chapter12...

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Authors (1)

Profile icon Ashish Ranjan Jha
LinkedIn
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.
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See other products by Ashish Ranjan Jha

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