What you will learn
- Learn step by step all the mathematical calculations involving artificial neural networks
- Implement neural networks in Python and Numpy from scratch
- Understand concepts like perceptron, activation functions, backpropagation, gradient descent, learning rate, and others
- Build neural networks applied to classification and regression tasks
- Implement neural networks using libraries, such as: Pybrain, sklearn, TensorFlow, and PyTorch
Requirements
- Programming logic
- Basic Python programming
- No prior knowledge about Artificial Neural Networks or Artificial Intelligence
Description
Artificial neural networks are considered to be the most efficient Machine Learning techniques nowadays, with companies the likes of Google, IBM and Microsoft applying them in a myriad of ways. You’ve probably heard about self-driving cars or applications that create new songs, poems, images and even entire movie scripts! The interesting thing about this is that most of these were built using neural networks. Neural networks have been used for a while, but with the rise of Deep Learning, they came back stronger than ever and now are seen as the most advanced technology for data analysis.
One of the biggest problems that I’ve seen in students that start learning about neural networks is the lack of easily understandable content. This is due to the fact that the majority of the materials that are available are very technical and apply a lot of mathematical formulas, which simply makes the learning process incredibly difficult for whomever wishes to take their first steps in this field. With this in mind, the main objective of this course is to present the theoretical and mathematical concepts of neural networks in a simple yet thorough way, so even if you know nothing about neural networks, you’ll understand all the processes. We’ll cover concepts such as perceptrons, activation functions, multilayer networks, gradient descent and backpropagation algorithms, which form the foundations through which you will understand fully how a neural network is made. We’ll also cover the implementations on a step-by-step basis using Python, which is one of the most popular programming languages in the field of Data Science. It’s important to highlight that the step-by-step implementations will be done without using Machine Learning-specific Python libraries, because the idea behind this course is for you to understand how to do all the calculations necessary in order to build a neural network from scratch.
To sum it all up, if you wish to take your first steps in Deep Learning, this course will give you everything you need. It’s also important to note that this course is for students who are getting started with neural networks, therefore the explanations will deliberately be slow and cover each step thoroughly in order for you to learn the content in the best way possible. On the other hand, if you already know your way around neural networks, this course will be very useful for you to revise and review some important concepts.
Are you ready to take the next step in your professional career? I’ll see you in the course!
Who this course is for
- Beginners who are starting to learn about Artificial Neural Networks or Deep Learning
- People interested in the theory of Artificial Neural Networks
- Undergraduate students who are studying subjects related to Artificial Intelligence
- Anyone interested in Artificial Intelligence or Artificial Neural Networks
Course Content
Ratings and Reviews
Review posted on Udemy
The course was nice and provided a good introduction into Deep Learning and Neural Networks. However, during Section 2 of the course when we were studying about the derivations of the networks, I believe that there were a few mistakes. First of all, the formula for the Sigmoid activation function is not (1/(1-e^-(w*x +b)), but rather (1/(1+e^-(w*x +b)). Secondly, the calculations do not make sense when the Loss function is set to y - y_hat. In order to obtain the same results I had to use (y-y_hat)^2, otherwise when I was deriving the calculation were incorrect. On a separate note, I liked that a list of books was provided. I will definitely will be picking those up for more in depth mathematical overview of NN. Thank you!
I came to this course with a little bit of prior experience but not much. It helped to solidify what I already knew and make it available for me to use on my own. Plus it taught me quite a lot of new things. The course is comparatively brief but covers all its topics carefully. The instructor makes it a point to actually explain every step along the way and play around with it, so one can really grasp what is happening. Great course!