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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

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Ratings and Reviews

4.8
Avg. Rating
79 Ratings
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What's your experience? We'd love to know!
Review posted on Udemy
Posted 4 weeks ago
by Anjali Prosacco

I recommend this course to anyone interested in deep learning about Neural Networks in Python. The instructor thoroughly clarified all of the course concepts.

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Review posted on Udemy
Posted 4 weeks ago
by Joan Bartell

A good, concise explanation. It's fun to listen to, and you'll learn a lot.

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Review posted on Udemy
Posted 1 month ago
by Anouk Jong

The course was well-paced and thorough. Concepts were clearly taught, and the practicals were simple to follow. Great course, I really enjoyed it!

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Review posted on Udemy
Posted 4 months ago
by Danielle Zoe Metin

I found too much time was spent on explaining basic mathematical operations and so the course did not hold my attention

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Review posted on Udemy
Posted 5 months ago
by xnaire godking

Very very very detailed explanation, best course to build strong foundations on NN's.

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Review posted on Udemy
Posted 7 months ago
by Joseph Höpker Fernandes

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!

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Review posted on Udemy
Posted 7 months ago
by José Antonio Bautista Hernández

good

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Review posted on Udemy
Posted 7 months ago
by Adela Bruen

Thank you for an excellent course. It was detailed and informative, and I learned far more than I anticipated.

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Review posted on Udemy
Posted 7 months ago
by Evelyn Riley

Amazing training. I was able to learn a lot of things about Python. Thanks

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Review posted on Udemy
Posted 7 months ago
by Peter Powell

Excellent course. The teacher knows what he's talking about. I learned a lot from him.

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