6 Neural Network Architecture

Описание: Video summary [00:00:00] - [00:06:38]:

This video is part of a tutorial series on TensorFlow. It explains how a simple neural network architecture works, using an example of digit recognition. It covers the steps of input, weight, bias, summation, and activation, and how they are implemented using tensors and operations.

Highlights:
[00:00:00] The purpose and type of input data
Can be text, structured data, or images
Can be represented by tensors of different dimensions
[00:01:14] The process of weight and bias multiplication and summation
Each input is multiplied by a corresponding weight
All the products are added together with a bias term
The result is a single number that is passed to the next layer
[00:03:19] The role of activation functions
They transform the input into an output between 0 and 1
They help determine the probability of a class or category
They can be sigmoid, ReLU, softmax, or others
[00:05:02] The example of digit recognition
It has 10 classes, from 0 to 9
It requires an image as input and a probability as output
It uses a sigmoid activation function to calculate the output
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Playlist for TensorFlow Series 1 & 2:
1.    • Tensorflow Getting Started  
2. 1 Back to Tensors

Prerequisites for this series:
1: Data Science & Machine Learning Using Python:    • Data Science Introduction  
2: Python for Data Science: Python for Data Science: 1. Quick Tour Colab
3: Machine learning with Python: What Is Python?


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What you get in this Playlist:
Good resources to learn TensorFlow, Tensorflow Training Data, Deep Learning Applications, Tensorflow tutorial, Deep Learning tutorials, TensorFlow tutorial for beginners, deep learning tutorials for beginners, Tensorflow Courses, How to Install Tensorflow, Deep Neural Network, Artificial Intelligence


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