🚪 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

Repo

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 💥