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# Introduction

🚪 Beginning to solve problems of computer vision with Tensorflow and Keras

The MNIST database:

**(Modified National Institute of Standards and Technology database)**- 🔎 Fashion-MNIST is consisting of a training set of 60,000 examples and a test set of 10,000 examples
- 🎨 Types:
- 🔢 MNIST: for handwritten digits
- 👗 Fashion-MNIST: for fashion

- 📃 Properties:
- 🌚 Grayscale
- 28x28 px
- 10 different categories

Term | Description |

➰ Sequential | That defines a SEQUENCE of layers in the neural network |

⛓ Flatten | Flatten just takes that square and turns it into a 1 dimensional set (used for input layer) |

🔷 Dense | Adds a layer of neurons |

💥 Activation Function | A formula that introduces non-linear properties to our Network |

✨ Relu | An activation function by the rule: If X>0 return X, else return 0 |

🎨 Softmax | An activation function that takes a set of values, and effectively picks the biggest one |

The main purpose of activation function is to convert a input signal of a node in a NN to an output signal. That output signal now is used as a input in the next layer in the stack 💥

- Values in MNIST are between 0-255 but neural networks work better with normalized data, so we can divide every value by 255 so the values are between 0,1.
- There are multiple criterias to stop training process, we can specify number of epochs or a threshold or both
- Epochs: number of iterations
- Threshold: a threshold for accuracy or loss after each iteration
- Threshold with maximum number of epochs

We can check the accuracy at the end of each epoch by Callbacks 💥

Last modified 3yr ago