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DEEP LEARNING FROM SCRATCH: BUILDING WITH PYTHON FROM FIRST PRINCIPLES

Seth Weidman

4.4/5 (1290744 ratings)
Read Now Download Now
With the resurgence of neural networks in the 2010s, deep learning has become
essential for machine learning practitioners and even many software engineers.
This book provides a comprehensive introduction for data scientists and software
engineers with machine learning experience. You'll start with deep learning
basics and move quickly to the details of important advanced architectures,
implementing everything from scratch along the way.

Author Seth Weidman shows you how neural networks work using a first principles
approach. You'll learn how to apply multilayer neural networks, convolutional
neural networks, and recurrent neural networks from the ground up. With a
thorough understanding of how neural networks work mathematically,
computationally, and conceptually, you'll be set up for success on all future
deep learning projects.

This book provides:


Extremely clear and thorough mental models--accompanied by working code examples
and mathematical explanations--for understanding neural networks
Methods for implementing multilayer neural networks from scratch, using an
easy-to-understand object-oriented framework
Working implementations and clear-cut explanations of convolutional and
recurrent neural networks
Implementation of these neural network concepts using the popular PyTorch
frameworkWe have made it easy for you to find a PDF Ebooks without any digging.
And by having access to our ebooks online or by storing it on your computer, you
have convenient answers with Deep Learning from Scratch: Building with Python
from First Principles. To get started finding Deep Learning from Scratch:
Building with Python from First Principles, you are right to find our website
which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of
different products represented.
Read More
Pages Array Format PDF, EPUB & Kindle Edition Publisher Array Release Array ISBN
1492041416


DEEP LEARNING FROM SCRATCH: BUILDING WITH PYTHON FROM FIRST PRINCIPLES

Seth Weidman

4.4/5 (1290744 ratings)
Read Download
 * Save for later
 * Add to list

With the resurgence of neural networks in the 2010s, deep learning has become
essential for machine learning practitioners and even many software engineers.
This book provides a comprehensive introduction for data scientists and software
engineers with machine learning experience. You'll start with deep learning
basics and move quickly to the details of important advanced architectures,
implementing everything from scratch along the way.

Author Seth Weidman shows you how neural networks work using a first principles
approach. You'll learn how to apply multilayer neural networks, convolutional
neural networks, and recurrent neural networks from the ground up. With a
thorough understanding of how neural networks work mathematically,
computationally, and conceptually, you'll be set up for success on all future
deep learning projects.

This book provides:


Extremely clear and thorough mental models--accompanied by working code examples
and mathematical explanations--for understanding neural networks
Methods for implementing multilayer neural networks from scratch, using an
easy-to-understand object-oriented framework
Working implementations and clear-cut explanations of convolutional and
recurrent neural networks
Implementation of these neural network concepts using the popular PyTorch
frameworkWe have made it easy for you to find a PDF Ebooks without any digging.
And by having access to our ebooks online or by storing it on your computer, you
have convenient answers with Deep Learning from Scratch: Building with Python
from First Principles. To get started finding Deep Learning from Scratch:
Building with Python from First Principles, you are right to find our website
which has a comprehensive collection of manuals listed.
Our library is the biggest of these that have literally hundreds of thousands of
different products represented.
Pages Array Format PDF, EPUB & Kindle Edition Publisher Array Release Array ISBN
1492041416



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