06-Updating Weights and Biases | Training a Neural Network | Deep Learning from Scratch in Hindi

Welcome to the next video in our Deep Learning from Scratch playlist! In this video, we’ll explore how to update weights and biases, combine the entire code, and run through epochs to train and optimize the model using NumPy.

What You’ll Learn:
Updating Weights and Biases: Understand the process of updating weights and biases during training.

Combining the Code: Learn how to integrate all components of the neural network into a cohesive implementation.

Training the Model: Discover how to run the training process through epochs to optimize the model.

NumPy Implementation: See how to perform these tasks using NumPy, without relying on TensorFlow or PyTorch.

Practical Coding Examples: Follow step-by-step coding examples to solidify your understanding and help you build your own neural networks from scratch.
This video is perfect for those who want to gain a deeper understanding of the training process in neural networks by implementing it from the ground up.

Why Watch This Video?
Hands-On Learning: Gain practical experience by implementing the training process without high-level libraries.

Clear Explanations: Break down complex concepts into easy-to-understand segments.

Coding Examples: Follow along with practical examples to solidify your understanding.

Comprehensive Coverage: Cover all essential aspects of updating weights and biases, and training the model.

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