Category: pytorch

PyTorch – Machine Learning vs. Deep Learning ”; Previous Next In this chapter, we will discuss the major difference between Machine and Deep learning concepts. Amount of Data Machine learning works with different amounts of data and is mainly used for small amounts of data. Deep learning on the other hand works efficiently if the amount of data increases rapidly. The following diagram depicts the working of machine learning and deep learning with respect to amount of data − Hardware Dependencies Deep learning algorithms are designed to heavily depend on high end machines on a contrary to traditional machine learning algorithms. Deep learning algorithms perform a large amount of matrix multiplication operations which requires a huge hardware support. Feature Engineering Feature engineering is the process of putting domain knowledge into specified features to reduce the complexity of data and make patterns which are visible to learning algorithms. For instance, traditional machine learning patterns focusses on pixels and other attributes needed for feature engineering process. Deep learning algorithms focusses on high level features from data. It reduces the task of developing new feature extractor for every new problem. Print Page Previous Next Advertisements ”;

PyTorch – Loading Data ”; Previous Next PyTorch includes a package called torchvision which is used to load and prepare the dataset. It includes two basic functions namely Dataset and DataLoader which helps in transformation and loading of dataset. Dataset Dataset is used to read and transform a datapoint from the given dataset. The basic syntax to implement is mentioned below − trainset = torchvision.datasets.CIFAR10(root = ”./data”, train = True, download = True, transform = transform) DataLoader is used to shuffle and batch data. It can be used to load the data in parallel with multiprocessing workers. trainloader = torch.utils.data.DataLoader(trainset, batch_size = 4, shuffle = True, num_workers = 2) Example: Loading CSV File We use the Python package Panda to load the csv file. The original file has the following format: (image name, 68 landmarks – each landmark has a x, y coordinates). landmarks_frame = pd.read_csv(”faces/face_landmarks.csv”) n = 65 img_name = landmarks_frame.iloc[n, 0] landmarks = landmarks_frame.iloc[n, 1:].as_matrix() landmarks = landmarks.astype(”float”).reshape(-1, 2) Print Page Previous Next Advertisements ”;

PyTorch – Installation ”; Previous Next PyTorch is a popular deep learning framework. In this tutorial, we consider “Windows 10” as our operating system. The steps for a successful environmental setup are as follows − Step 1 The following link includes a list of packages which includes suitable packages for PyTorch. https://drive.google.com/drive/folders/0B-X0-FlSGfCYdTNldW02UGl4MXM All you need to do is download the respective packages and install it as shown in the following screenshots − Step 2 It involves verifying the installation of PyTorch framework using Anaconda Framework. Following command is used to verify the same − conda list “Conda list” shows the list of frameworks which is installed. The highlighted part shows that PyTorch has been successfully installed in our system. Print Page Previous Next Advertisements ”;

PyTorch Tutorial PDF Version Quick Guide Resources Job Search Discussion PyTorch is an open source machine learning library for Python and is completely based on Torch. It is primarily used for applications such as natural language processing. PyTorch is developed by Facebook”s artificial-intelligence research group along with Uber”s “Pyro” software for the concept of in-built probabilistic programming. Audience This tutorial has been prepared for python developers who focus on research and development with machinelearning algorithms along with natural language processing system. The aim of this tutorial is to completely describe all concepts of PyTorch and realworld examples of the same. Prerequisites Before proceeding with this tutorial, you need knowledge of Python and Anaconda framework (commands used in Anaconda). Having knowledge of artificial intelligence concepts will be an added advantage. Print Page Previous Next Advertisements ”;

Universal Workflow of Machine Learning ”; Previous Next 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. Print Page Previous Next Advertisements ”;