Deep Learning from the Basics: Python and Deep Learning: Theory and Implementation

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Learn deep learning models through several activities

Begin with simple machine learning problems, and finish by building a complex system of your own

Teach your machines to see by mastering the technologies required for image recognition

Description

Deep learning is rapidly becoming the most preferred way of solving data problems. This is thanks, in part, to its huge variety of mathematical algorithms and their ability to find patterns that are otherwise invisible to us. Deep Learning from the Basics begins with a fast-paced introduction to deep learning with Python, its definition, characteristics, and applications. You’ll learn how to use the Python interpreter and the script files in your applications, and utilize NumPy and Matplotlib in your deep learning models. As you progress through the book, you’ll discover backpropagation—an efficient way to calculate the gradients of weight parameters—and study multilayer perceptrons and their limitations, before, finally, implementing a three-layer neural network and calculating multidimensional arrays. By the end of the book, you’ll have the knowledge to apply the relevant technologies in deep learning.

Who is this book for?

Deep Learning from the Basics is designed for data scientists, data analysts, and developers who want to use deep learning techniques to develop efficient solutions. This book is ideal for those who want a deeper understanding as well as an overview of the technologies. Some working knowledge of Python is a must. Knowledge of NumPy and pandas will be beneficial, but not essential.

What you will learn

Use Python with minimum external sources to implement deep learning programs

Study the various deep learning and neural network theories

Learn how to determine learning coefficients and the initial values of weights

Implement trends such as Batch Normalization, Dropout, and Adam

Explore applications like automatic driving, image generation, and reinforcement learning

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Machine Learning for Time-Series with Python

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Kalyan Kumar May 27, 2021

I bought this book just to cover few concepts of Deep Learning as I wasn't getting any complete picture searching about those concepts online. After start reading this book I was so impressed with content and structure. Starting from basic python, packages , history of perceptron, BP and different NNs. So far the best book I read related to ML/DL. I would highly recommend this book to anyone who is getting into field of AI/ML for solid basis concepts of Deep Learning.

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Payton Soicher Mar 16, 2021

This book does a great job of breaking down the fundamentals of how deep learning works and putting together the pieces to create a neural network process from scratch. This book could be a little intimidating for those who are used to installing packages like Keras, PyTorch, or Tensorflow instead of writing their own pipelines from scratch. I would recommend reading this book for a foundational approach to building deep learning pipelines to making big strides in your understanding of deep learning.Overall, I've really enjoyed reading the book and believe I have a better understanding of how to create neural networks because of it.

F. G. Mar 16, 2021

I put my hands on the book 1 week ago, and it's for everyone. 11th year in the field and this book is helping to remember some basics as well as the new stuff that I haven't had the chance to take a look yet. Overall it's organized and thorough. Can be used as a class book or study desk companion for new projects.

Grad Student Mar 23, 2021

In the last two decades, we’ve seen deep learning go from something a few academics and principal engineers studied, to become the modus operandi for many data scientists today. The availability of widely-maintained, open source tools, and the ability to commission compute instances of almost arbitrary power has made this a default approach for many. One of the most remarkable aspects of this evolution is the pedagogy it inspires.This book’s first principles approach is suitable for anyone with high school algebra, a computer, and a desire to start from the basics. Complex concepts like backwards propagation and choice of optimizers are introduced intuitively. Necessary mathematical prerequisites, like numerical differentiation, matrix calculus, and the computational graph are covered in enough detail to move the reader forward without bogging them down. A synopsis of modern applications gives the interested student a springboard into the academic literature, Kaggle competitions, or (more likely) both.If you’re an aspiring analyst trying to decide if an accelerator program is right for you, a savvy high school student with a budding interest in math and computer science, or just another person curious enough to want to understand the underpinnings of a lot of the consumer-facing data products you interact with today, this book is for you.

Shivam Taneja May 18, 2021

To all the aspiring data scientists who want to excel in Machine learning especially Neural networks and Deep learning- This book is ideal for you. It gives a good amount of knowledge starting from the basics of python explaining all the usefull libraries and moving on to neural networks and deep learning. This book posses a good way to deep dive into this field and gain ample amount of knowledge to crack interviews or work on some really interesting client projects. I have done my master's in Data science and I found this book really helpful in polishing my existing knowledge and giving me a good hold on basics which further helped me understand complex concepts.

Deep Learning from the Basics: Python and Deep Learning: Theory and Implementation

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Deep Learning from the Basics: Python and Deep Learning: Theory and Implementation

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