{"id":36643,"date":"2024-11-01T09:50:16","date_gmt":"2024-11-01T09:50:16","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=36643"},"modified":"2024-11-01T11:52:23","modified_gmt":"2024-11-01T11:52:23","slug":"deep-learning-pytorch-course-running-example-files-on-colab","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/36643\/","title":{"rendered":"Deep Learning PyTorch Course, Running Example Files on Colab"},"content":{"rendered":"

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Hello! In this post, we will start with the basics of deep learning<\/strong> and PyTorch<\/strong> and write codes that can be practiced. We will also guide you on how to run the code using Google Colab. PyTorch is a deep learning library suitable for in-depth learning and research, providing an intuitive and flexible dynamic computation graph. A key feature added at each stage of the graph is that researchers and engineers can easily modify and optimize the models as needed.<\/p>\n<\/div>\n

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1. Overview of Deep Learning<\/h2>\n

Deep learning is a field of machine learning that uses artificial neural networks to learn patterns from data. It is mainly used in image recognition, natural language processing, and speech recognition. Essentially, a deep learning model has a structure that receives input data, processes it, and outputs the results. These models are composed of numerous neurons, each processing received values along with weights to produce outputs.<\/p>\n<\/div>\n

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2. What is PyTorch?<\/h2>\n

PyTorch is a deep learning framework developed by Facebook, popular for its ability to write Pythonic code. One of the advantages of PyTorch is its intuitive interface and powerful GPU acceleration capabilities, allowing for efficient handling of large-scale data and complex models. Additionally, it supports Dynamic Computation Graphs, enabling flexible changes to the model’s structure.<\/p>\n<\/div>\n

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3. Setting up Google Colab<\/h2>\n

Google Colab provides an online environment to run Python code. It supports GPU acceleration using CUDA, allowing you to complete model training in a short time.<\/p>\n

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  1. Log in with your Google account.<\/li>\n
  2. Access Google Colab<\/a>.<\/li>\n
  3. Create a new notebook.<\/li>\n
  4. Click ‘Runtime’ -> ‘Change Runtime Type’ in the top menu and select GPU.<\/li>\n<\/ol>\n

    Your Colab environment is now ready!<\/p>\n<\/div>\n

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    4. Basic Usage of PyTorch<\/h2>\n

    4.1. Installing PyTorch<\/h3>\n

    PyTorch is installed by default in Colab, but if you want the latest version, you can install it using the command below.<\/p>\n

    !pip install torch torchvision<\/code><\/pre>\n
    4.2. Tensor<\/h3>\n
    Tensor is the core data structure of PyTorch. It fundamentally supports mathematical operations as an N-dimensional array, providing the following features:<\/p>\n