Original article was published on Deep Learning on Medium
Neural Network or extention if Linear Regression
Deep learning is a branch of machine learning, where algorithms learn independently from excessive amounts of information.
What cool in deep learning apart from machine learning model ?
We have 3 node in the neural network: the input layer, which is the data we want to analyze. At least 1 or 2 hidden layers which complete the computation with the deep learning algorithm. In the output layer
Neural networks : a hidden layer is located between the input and output of the algorithm, in which the function applies weights to the inputs and directs them through an activation function as the output.
Improvement in logistic regresssion in which we can add Hidden layer in between that generate our Neural network model
Why we use hidden layer ?
Hidden layers allow for the function of a neural network to be broken down into specific transformations of the data. Each hidden layer function is specialized to produce a defined output
transform to tensor that convert into pytorch tensor that we download image from MNIST data set
- Evaluate Training and Validation Dataset
- Gradient descent
- Linear equation (nn.linear)
- Improve Model
Evaluate Training and Validation Dataset
A validation dataset is a sample of data held back from training your model that is used to give an estimate of model
Data Loader is a client application for the bulk import or export of data
The validation dataset is different from the test dataset that is also held back from the training of the model,
PyTorch data loading utility is the torch.utils.data.DataLoader class. It represents a Python iterable over a dataset
Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient
torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.
Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy
Improve Model with one hidden layer
A hidden layer in an artificial neural network is a layer in between input layers and output layers
Activation function to simplity equation (take a result and pass to next input layer)
These are the things we use in training a model : You can check my model
Here you check for mode learning https://jovian.ml