Mathematics Behind Backpropagation | Theory and Python Code

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Unlock the core concepts of backpropagation, gradients, and gradient descent, and learn to implement them in Python. Master the theory behind neural networks and gain hands-on coding experience by building your own model. In this course, you will embark on a comprehensive learning journey, starting with the essential mathematics behind backpropagation. The course begins with foundational concepts like derivatives, gradients, and partial derivatives, progressing to more advanced topics such as gradient descent and the chain rule. Through these key principles, you'll understand how neural networks learn and optimize. You will also learn about the significance of computational graphs, which help visualize the relationships between the variables in a neural network. With a detailed walkthrough, you'll build a simple neural network, applying concepts like forward pass, loss functions, and backpropagation from scratch. You'll gradually explore more complex topics such as gradient computation, the role of learning rates, and fine-tuning your network. Throughout the course, the hands-on implementation of each concept in Python will solidify your understanding. You'll code your own neural network without relying on pre-built libraries, giving you the ability to understand and manipulate the underlying algorithms. By the end, you'll have the confidence to tackle real-world AI projects and make informed decisions in machine learning. This course is designed for data scientists, aspiring machine learning engineers, and software developers who want to deepen their understanding of neural networks and backpropagation. It is ideal for professionals eager to master the mathematical foundation of AI, and those transitioning into the field of machine learning. No prior experience in deep learning is necessary, though a basic understanding of Python programming is recommended to fully benefit from the hands-on coding sections. Explore the foundations and hands-on implementation of backpropagation, bridging theory with real-world applications in AI, machine learning, and neural networks. From understanding derivatives to building neural networks with Python code, this course offers a comprehensive journey for technical professionals aiming to excel in the field of AI. This course is based on Mathematics Behind Backpropagation | Theory and Python Code, by Patrik Szepesi. This course is licensed and distributed by Packt. All rights reserved. Packt is one of the world's most prolific publishers of cutting-edge technical content. For over two decades we've made it our mission to curate and publish the knowledge of only the very best technical experts. We focus on real-world courses that help our customers get the job done, with coverage that extends across a wide range of established and cutting-edge technical topics. If you're an individual or an organisation that embraces learning by doing, Packt is the perfect fit for you.

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