👍Deep learning is a subset of machine learning that uses artificial neural networks.
💫Neural networks can be understood without relying on prebuilt frameworks like Keras or TensorFlow.
⌚The talk aims to provide a comprehensive understanding of deep learning and neural networks.
🤔Participants will gain insights into the implementation details that frameworks often hide.
📢The talk is suitable for both beginners and individuals familiar with deep learning.
Do I need prior experience with deep learning to understand this talk?
—No, the talk is designed to be accessible to both beginners and individuals familiar with deep learning concepts.
Are there any specific programming languages used in the talk?
—The talk mainly focuses on explaining the concepts and does not require any specific programming language.
Will I be able to build neural networks from scratch after this talk?
—Yes, the second part of the talk includes live coding to demonstrate building a neural network from scratch using Python.
Is this talk suitable for someone interested in applying deep learning to real-world problems?
—Absolutely! The talk provides a solid foundation for understanding deep learning principles and implementation, making it applicable to real-world problem-solving.
How long is the talk?
—The talk is divided into two parts. The first part covers the basics of deep learning and neural networks, while the second part includes live coding and examples. The total duration is approximately [insert duration].
00:01The talk is titled 'Theory of Neuro Networks: Deep Learning Without Frameworks'.
00:03The speaker aims to provide a comprehensive understanding of deep learning and neural networks.
00:08The talk focuses on explaining the basics of deep learning without using frameworks like Keras or TensorFlow.
00:13The goal is to help participants understand the inner workings of deep learning and neural networks by going beyond frameworks.
01:25The speaker introduces themselves as Beau Carnes from the United States, currently working at freecodecamp.org.
01:55Beau shares his experience in creating video courses and his involvement in an open-source community.
03:12Beau explains the difference between machine learning and deep learning, highlighting that deep learning is a subset of machine learning.
07:56Beau introduces the concepts of supervised and unsupervised learning, discussing their differences and use cases.
10:16Beau explains the difference between parametric and non-parametric learning, highlighting their approaches and advantages.
11:59Beau provides an example of supervised parametric learning, focusing on predicting the outcome of a sports game.
