1.5.2 Creating test and training datasets............. 1.5.3 Setting up the output layer.... .32 1.5.4 Creating the neural network .................... 32 1.5.5 Assessing the accuracy of the trained model 38 Introduction Welcome to the"An introduction to neural networks for beginners"book.The aim of this much larger book is to get you up to speed with all you need to start on the deep learning journey using TensorFlow.Once you're finished,you may like to check out my follow-up book entitled Coding the Deep Learning Revolution -a step by step introduction using Python,Keras and TensorFlow.What is deep learning,and what is TensorFlow?Deep learning is the field of machine learning that is making many state-of-the-art advancements,from beating players at Go and Poker,to speeding up drug discovery and assisting self-driving cars.If these types of cutting edge applications excite you like they excite me,then you will be interesting in learning as much as you can about deep learning.However,that requires you to know quite a bit about how neural networks work.This will be what this book covers-getting you up to speed on the basic concepts of neural networks and how to create them in Python. WHO I AM AND MY APPROACH I am an engineer who works in the energy utility business who uses machine learning almost daily to excel in my duties.I believe that knowledge of machine learning,and its associated concepts,gives you a significant edge in many different industries,and allows you to approach a multitude of problems in novel and interesting ways.I also maintain an avid interest in machine and deep learning in my spare time,and wish to leverage my previous experience as a university lecturer and academic to educate others in the coming Al and machine learning revolution.My main base for doing this is my website- Adventures in Machine Learning. Some educators in this area tend to focus solely on the code,with neglect of the theory. Others focus more on the theory,with neglect of the code.There are problems with both these types of approaches.The first leads to a stunted understanding of what one is doing -you get quite good at implementing frameworks but when something goes awry or not quite to plan,you have no idea how to fix it.The second often leads to people getting swamped in theory and mathematics and losing interest before implementing anything in code. PAGE 2PAGE 2 1.5.2 Creating test and training datasets ............................................................................. 31 1.5.3 Setting up the output layer......................................................................................... 32 1.5.4 Creating the neural network ...................................................................................... 32 1.5.5 Assessing the accuracy of the trained model............................................................ 38 Introduction Welcome to the “An introduction to neural networks for beginners” book. The aim of this much larger book is to get you up to speed with all you need to start on the deep learning journey using TensorFlow. Once you’re finished, you may like to check out my follow-up book entitled Coding the Deep Learning Revolution – A step by step introduction using Python, Keras and TensorFlow. What is deep learning, and what is TensorFlow? Deep learning is the field of machine learning that is making many state-of-the-art advancements, from beating players at Go and Poker, to speeding up drug discovery and assisting self-driving cars. If these types of cutting edge applications excite you like they excite me, then you will be interesting in learning as much as you can about deep learning. However, that requires you to know quite a bit about how neural networks work. This will be what this book covers – getting you up to speed on the basic concepts of neural networks and how to create them in Python. WHO I AM AND MY APPROACH I am an engineer who works in the energy / utility business who uses machine learning almost daily to excel in my duties. I believe that knowledge of machine learning, and its associated concepts, gives you a significant edge in many different industries, and allows you to approach a multitude of problems in novel and interesting ways. I also maintain an avid interest in machine and deep learning in my spare time, and wish to leverage my previous experience as a university lecturer and academic to educate others in the coming AI and machine learning revolution. My main base for doing this is my website – Adventures in Machine Learning. Some educators in this area tend to focus solely on the code, with neglect of the theory. Others focus more on the theory, with neglect of the code. There are problems with both these types of approaches. The first leads to a stunted understanding of what one is doing – you get quite good at implementing frameworks but when something goes awry or not quite to plan, you have no idea how to fix it. The second often leads to people getting swamped in theory and mathematics and losing interest before implementing anything in code