Modern Web Podcast S11E18- ChatGPT Can't Solve These Problems! with Chris Gardner

This podcast episode explores neural networks and their application in artificial intelligence and machine learning. It delves into the structure, function, and training processes of neural networks, emphasizing the importance of understanding linear algebra and calculus as mathematical foundations. The discussion also covers challenges in neural network implementation and training, such as biases and complexities in real-world applications. Additionally, the accessibility of neural networks for non-programmers and the ethical considerations in machine learning are addressed.

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Modern Web

Understanding Neural Networks: A Comprehensive Guide

Neural Networks: A Deep Dive into the Basics

Understanding Neural Networks: When and How to Use Them

Understanding Neural Networks: A Step-by-Step Guide

Demystifying Neural Networks: A Journey Through Linear Algebra and Matrix Manipulation

Understanding Neural Networks: From Matrix Manipulations to Training

Delving into the Complexities of Neural Network Implementation: Calculus, Gradient Descent, and Back Propagation

Understanding the Complexities of Neural Network Training: Back Propagation and Gradient Descent

Using Tooling to Explore Neural Networks and Machine Learning

Navigating Biases in Machine Learning Models: Challenges and Ethical Considerations

The Challenges of Using Machine Learning for Prediction

Modern Web Podcast S11E18- ChatGPT Can't Solve These Problems! with Chris Gardner

Modern Web

00:01Understanding Neural Networks: A Comprehensive Guide

Understanding Neural Networks: A Comprehensive Guide

05:37Neural Networks: A Deep Dive into the Basics

Neural Networks: A Deep Dive into the Basics

10:04Understanding Neural Networks: When and How to Use Them

Understanding Neural Networks: When and How to Use Them

15:30Understanding Neural Networks: A Step-by-Step Guide

Understanding Neural Networks: A Step-by-Step Guide

18:51Demystifying Neural Networks: A Journey Through Linear Algebra and Matrix Manipulation

Demystifying Neural Networks: A Journey Through Linear Algebra and Matrix Manipulation

21:21Understanding Neural Networks: From Matrix Manipulations to Training

Understanding Neural Networks: From Matrix Manipulations to Training

27:13Delving into the Complexities of Neural Network Implementation: Calculus, Gradient Descent, and Back Propagation

Delving into the Complexities of Neural Network Implementation: Calculus, Gradient Descent, and Back Propagation

29:19Understanding the Complexities of Neural Network Training: Back Propagation and Gradient Descent

Understanding the Complexities of Neural Network Training: Back Propagation and Gradient Descent

32:55Using Tooling to Explore Neural Networks and Machine Learning

Using Tooling to Explore Neural Networks and Machine Learning

38:26Navigating Biases in Machine Learning Models: Challenges and Ethical Considerations

Navigating Biases in Machine Learning Models: Challenges and Ethical Considerations

44:03The Challenges of Using Machine Learning for Prediction

The Challenges of Using Machine Learning for Prediction