Discover ways to implement various deep learning algorithms by leveraging Python and other technologiesKey Features* 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 recognitionBook DescriptionDeep 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.
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 learningWho this book is forDeep 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.
Discover ways to implement various deep learning algorithms by leveraging Python and other technologiesKey Features* 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 recognitionBook DescriptionDeep 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.
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 learningWho this book is forDeep 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.