What you would learn in Introduction to Artificial Neural Network and Deep Learning course?
Machine learning is a popular area of Artificial Intelligence and Data Science. It is not a secret it is true that Neural networks are among the most admired and extensively utilized machine learning methods.
Many Data Scientists use Neural Networks without knowing their inner structure. But, knowing these machine learning methods' inner structure and mechanism will help them solve problems faster. This will allow them to adjust, tune, and even create new Neural Networks for various projects.
This is the most efficient way to comprehend how Neural Networks work in the smallest detail. This course also places you ahead of many people who are in data science. There is the chance to join a small group of highly-paid data scientists.
What is the reason for learning Neural Networks for Scientists?
Machine learning is becoming increasingly popular across all industries every month, aiming to increase revenue and decrease expenses. Neural Networks are efficient methods of machine learning in various projects. They can be utilized to speed up and enhance the process of tackling complex problems.
What is it that data scientists require to know about Neural Networks?
The first thing to master is the mathematical concepts behind these models. It is incredible how simple mathematical equations and models are. This course begins with simple examples that will guide you through the fundamental mathematical models that comprise Neural Networks. There isn't a single formula in this course that is not accompanied by an in-depth explanation or illustrations. If you don't like math, then sit back and relax while watching the videos to understand the mathematics that is behind Neural Networks with minimum effort.
Understanding the types of problems that can be solved using Neural Networks is also essential. This course provides a variety of problems that can be solved with Neural Networks, including classification and regression as well as predictions. There will be plenty of examples to help you solve these problems.
What will this course teach?
As mentioned above, This course opens with an intuitive illustration to show what a Neuron is, as the essential part that makes up Neural Networks. The course also teaches you the conceptual and mathematical model of the Neuron. Once you have learned how simple mathematical models of the single Neuron are, you'll observe how it functions in real-time.
The second portion of this course will cover terminology that pertains to machine learning. We will also look at the mathematical model for a specific kind of neuron known as Perceptron and its source of inspiration. We will explore the primary part of a perceptron well.
In the third section, we will collaborate with you to understand the method of training and learning through Neural networks. This will include learning various cost and error functions, optimizing costs, a gradient descent algorithm, the impact of the rate of learning, and the challenges in this field.
In the initial three parts, the course will help you learn the workings of a single nerve (e.g., Perceptron). This is good preparation for the next portion of the course, where we'll discover how to create a network from these neurons. It will be evident how effective even connecting two neurons are. We will discover the effects of multiple neurons and layers in the results of a Neural Network. The principal model in this course is a multi-layer Perceptron (MLP), one of the most admired Neural Networks in science and industry. The course also covers Deep Neural Networks (DNN).
In the fifth segment, the course will be in its fifth section. We'll be introduced to what is known as the Backpropagation (BP) algorithm to build a multi-layer perception system. The theoretical basis, mathematical model, and numerical implementation of the algorithm will be reviewed in depth.
The main issues addressed in Sections 1-5 involve classification, a vital task requiring an array of real-world scenarios. For example, you could sort customers according to their interests in a particular product class. But, some issues require prediction. These problems can be solved with regression methods, and neural networks can also play the role of a regression technique. This is the topic we'll discuss during Section 6 in this class. We begin with an intuitive illustration of how to do regression using one neuron. A live demonstration is available to demonstrate how neurons play the function of a regression model. In this section, you'll discover topics like linear regression and logistic (non-linear) regression, Regression examples and problems with multiple regressions, and an MLP that has three layers to deal with all kinds of repression issues.
The final part of this class will be about solving problems using Neural Networks. We will use Neuroph, a Java-based application that will show instances of Neural Networks within the domains of hand-to-hand and the recognition of handwritten characters and image processing. If you've never used Neuroph, you don't have anything to be concerned about. There are a variety of videos that show you how to build and manage projects using Neuroph.
At the end of this course, you'll be well-versed in Neural Networks and able to utilize them in your work effectively. You will be able to analyze how you can tune and improve the efficiency of Neural Networks based on your project's requirements too.
- A structure for Neural Networks
- The process of learning in Neural Networks
- Visualization of visual information Neural Networks
- Deep Learning and Deep Neural Networks
- How can you classify by using Neural Networks
- Regression and forecasting by using Neural Networks
- The implementation of Neural Networks in Java
- Utilizing Neuroph to develop tests, analyze, and design Neural Networks