Neural Network for Beginners: Build Deep Neural Networks and Develop Strong Fundamentals using Python’s NumPy, and Matplotlib

Key Features

• Understand applications like reinforcement learning, automatic driving and image generation.
• Understand neural networks accompanied with figures and charts.
• Learn about determining coefficients and initial values of weights.

Description

Deep learning helps you solve issues related to data problems as it has a vast array of mathematical algorithms and has capacity to detect patterns.

This book starts with a quick view of deep learning in Python which would include definition, features and applications. You would be learning about perceptron, neural networks, Backpropagation. This book would also give you a clear insight of how to use Numpy and Matplotlin in deep learning models.

By the end of the book, you’ll have the knowledge to apply the relevant technologies in deep learning.

What you will learn

• To develop deep learning applications, use Python with few outside inputs.
• Study several ideas of profound learning and neural networks.
• Learn how to determine coefficients of learning and weight values.
• Explore applications such as automation, image generation and reinforcement learning.
• Implement trends like batch Normalisation, dropout, and Adam.

Who this book is for

Deep Learning from the Basics is for data scientists, data analysts and developers who wish to build efficient solutions by applying deep learning techniques. Individuals who would want a better grasp of technology and an overview. You should have a workable Python knowledge is a required. NumPy knowledge and pandas will be an advantage, but that’s completely optional.

About the Authors

Sebastian Klaas A data science professional who has great organizational and communication skills. He enjoys solving problems and coming up with unique solutions. I have 10+ years of experience working in data consultancy, customer experience, product analytics, and survey analytics.

He is passionate about Big Data Analysis, Data Driven Decision Making, Data Mining, Data Wrangling, Data Modelling and Predictions, Forecasting the Future, Data Visualization.

Technical statistical and data analysis tools he has grip on includes R, Python, Microsoft Excel, Microsoft SQL.