Neural Networks in Python from Scratch: Complete guide
Udemy Coupon Code for Neural Networks in Python from Scratch: Complete guide Course. Learn the fundamentals of Deep Learning of neural networks in Python both in theory and practice!
Created by Jones Granatyr, SuperDataScience Team, Ligency Team, AI Expert Academy | 8.5 hours on-demand video course
Neural Networks Course Overview
Neural Networks in Python from Scratch: Complete guide
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!
What you’ll 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
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Neural Networks in Python Course Reviews
Everything You Need to Know About Neural Networks in Python from Scratch: Complete guide
This course offers a comprehensive and well-structured introduction to Neural Networks. Jones Granatyr, the instructor, brings a wealth of expertise in Data Science, making this course both informative and engaging.
The course structure is easy to follow. Each section, for example, covers a different aspect of Neural Networks in Python Course, ensuring a logical progression through the material. It includes video lectures, readings, and hands-on exercises, which make complex concepts accessible and practical.
Moreover, The Instructors explains each topic clearly and concisely. He supports the lessons with plenty of examples and exercises, which help students grasp the material effectively.
What I appreciated most about this course is its practical focus. For instance, the instructor emphasizes teaching skills and knowledge that are directly applicable to real-world scenarios. Additionally, students gain access to helpful resources such as templates, checklists, and cheat sheets.
Another standout feature is the platform itself. Udemy Coupons offers flexibility, allowing students to learn at their own pace and access course materials from anywhere with an internet connection. Furthermore, the multiple payment options make it easy for students to choose a plan that suits their budget.
In addition, the course community is highly active, with forums where students can ask questions and engage with peers. The instructor, consequently, is very responsive and addresses student inquiries and feedback promptly.
Overall, I highly recommend the Neural Networks in Python from Scratch: Complete guide to anyone looking to learn Neural Networks This well-organized and practical course equips you with the skills and knowledge you need to succeed in this field.
