- PyTorch Tutorial
- PyTorch - Home
- PyTorch - Introduction
- PyTorch - Installation
- Mathematical Building Blocks of Neural Networks
- PyTorch - Neural Network Basics
- Universal Workflow of Machine Learning
- Machine Learning vs. Deep Learning
- Implementing First Neural Network
- Neural Networks to Functional Blocks
- PyTorch - Terminologies
- PyTorch - Loading Data
- PyTorch - Linear Regression
- PyTorch - Convolutional Neural Network
- PyTorch - Recurrent Neural Network
- PyTorch - Datasets
- PyTorch - Introduction to Convents
- Training a Convent from Scratch
- PyTorch - Feature Extraction in Convents
- PyTorch - Visualization of Convents
- Sequence Processing with Convents
- PyTorch - Word Embedding
- PyTorch - Recursive Neural Networks
- PyTorch Useful Resources
- PyTorch - Quick Guide
- PyTorch - Useful Resources
- PyTorch - Discussion
Universal Workflow of Machine Learning
Artificial Intelligence is trending nowadays to a greater extent. Machine learning and deep learning constitutes artificial intelligence. The Venn diagram mentioned below explains the relationship of machine learning and deep learning.
Machine Learning
Machine learning is the art of science which allows computers to act as per the designed and programmed algorithms. Many researchers think machine learning is the best way to make progress towards human-level AI. It includes various types of patterns like −
- Supervised Learning Pattern
- Unsupervised Learning Pattern
Deep Learning
Deep learning is a subfield of machine learning where concerned algorithms are inspired by the structure and function of the brain called Artificial Neural Networks.
Deep learning has gained much importance through supervised learning or learning from labelled data and algorithms. Each algorithm in deep learning goes through same process. It includes hierarchy of nonlinear transformation of input and uses to create a statistical model as output.
Machine learning process is defined using following steps −
- Identifies relevant data sets and prepares them for analysis.
- Chooses the type of algorithm to use.
- Builds an analytical model based on the algorithm used.
- Trains the model on test data sets, revising it as needed.
- Runs the model to generate test scores.