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Deep Learning with PyTorch: Build, Train, and Tune Neural Networks Using Python Tools
There are countless ways to put deep learning to good improved medical imaging, credit card fraud detection, long range weather forecasting. PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you - and your deep-learning skills - become more sophisticated.
This book will make that journey engaging and fun.
About the technology
Although many deep learning tools use Python, the PyTorch library is truly Pythonic. Instantly familiar to anyone who knows PyData tools like NumPy and scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It's excellent for building quick models, and it scales smoothly from laptop to enterprise. Because companies like Apple, Facebook, and JPMorgan Chase rely on PyTorch, it's a great skill to have as you expand your career options.
About the book
Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results.
After covering the basics, the book will take you on a journey through larger projects.
What's inside
Training deep neural networks Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Exploring code samples in Jupyter Notebooks About the audience
For Python programmers with an interest in machine learning
About the authors
Eli Stevens had roles from software engineer to CTO and is currently working on machine learning in the self-driving-car industry. Luca Antiga is cofounder of an AI engineering company as well as a former PyTorch contributor. Thomas Viehmann is a PyTorch core developer and machine learning trainer and consultant.
PLEASE When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
This book will make that journey engaging and fun.
About the technology
Although many deep learning tools use Python, the PyTorch library is truly Pythonic. Instantly familiar to anyone who knows PyData tools like NumPy and scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It's excellent for building quick models, and it scales smoothly from laptop to enterprise. Because companies like Apple, Facebook, and JPMorgan Chase rely on PyTorch, it's a great skill to have as you expand your career options.
About the book
Deep Learning with PyTorch teaches you to create neural networks and deep learning systems with PyTorch. This practical book quickly gets you to work building a real-world example from a tumor image classifier. Along the way, it covers best practices for the entire DL pipeline, including the PyTorch Tensor API, loading data in Python, monitoring training, and visualizing results.
After covering the basics, the book will take you on a journey through larger projects.
What's inside
Training deep neural networks Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Exploring code samples in Jupyter Notebooks About the audience
For Python programmers with an interest in machine learning
About the authors
Eli Stevens had roles from software engineer to CTO and is currently working on machine learning in the self-driving-car industry. Luca Antiga is cofounder of an AI engineering company as well as a former PyTorch contributor. Thomas Viehmann is a PyTorch core developer and machine learning trainer and consultant.
PLEASE When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.
Audible Audio
Published June 24, 2021
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