Deep Learning (DL) is an advanced technique of machine learning (ML) based on neural network algorithm. This technique has found its application in almost every sector of business. Various deep learning models such as convolutional networks, recurrent networks, and GANs are implemented in the projects for trends prediction, image classification, and data generation.
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Install miniconda on your computer.
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Install
git
for working with Github from your terminal window with command:conda install git
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Clone this repository to your local computer, and navigate to your downloaded folder with command:
git clone https://github.com/AndyTKH/Deep-Learning.git cd Deep-Learning
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Create and activate a new environment, named the new environment as
deep-learning-project
with Python 3.6 installed:- Linux or Mac:
conda create -n deep-learning-project python=3.6 source activate deep-learning-project
- Windows:
conda create --name deep-learning-project python=3.6 activate deep-learning-project
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Install latest version of PyTorch and Torchvision:
- Linux or Mac:
conda install pytorch torchvision -c pytorch
- Windows:
conda install pytorch -c pytorch pip install torchvision
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Install the required pip packages, as specified in the requirement text file:
pip install -r requirements.txt
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Now that you have all the required libraries to run my project, assuming your
deep-learning-project
environment is still activated, you may now navigate to the specific project directory to view my project from Jupyter Notebook, replaceproject_directory
with the project name directory. To do this, replaceproject_directory
withImage_Classification_CNN_Project
for the Image Classification project directory, and subsequently, replaceProject_name
withImage_Classification
to open the specific project in Jupyter Notebook browser:cd cd Deep-Learning/project_directory jupyter notebook Project_name.ipynb
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Simply close the terminal window to exit Jupyter Notebook.