Deep learning tools

As deep learning techniques are a bit complicated compared to conventional ML techniques, numerous effort has been made so far to build tools to make deep learning implementations a bit easier. We'll be looking at just 3 of these tools.

  • OpenCV
  • Tensorflow
  • Pytorch

1. OpenCV

Open Computer Vision (OpenCV) library is a widely used open-source computer vision and image processing library. It was originally developed by Intel and later maintained by the OpenCV community.

It provides a range of tools and functions for various computer vision tasks, making it a valuable resource for working on image and video analysis projects.

Here are some key features and capabilities of the OpenCV library:

  • Image processing
  • Computer vision algorithms
  • Video processing
  • Feature detection and matching
  • Machine learning
  • GUI and Visualization
  • Geometric transformations
  • Cross-platform

Generally, OpenCV serves as a powerful and versatile toolbox for computer vision tasks. To easily get started with OpenCV, explore the official docs here: Getting started with OpenCV

2. Tensorflow

TensorFlow is an open-source ML framework developed by the Google Brain team. It's designed to make it easier for researchers and developers to build, train, and deploy ML models, particularly deep neural networks.

It provides a flexible and comprehensive platform for a wide range of machine learning tasks, including image and speech recognition, natural language processing, and more.

Its flexibility, versatility, and active community make it a leading choice in the field of machine learning. Some features of Tensorflow includes:

  • Deep learning
  • Flexible architecture
  • Distributed training
  • Model deployment
  • Wide range of APIs
  • Community and ecosystem

To easily get started with Tensorflow, explore the official docs here: Getting started with Tensorflow

3. Pytorch

PyTorch is an open-source machine learning framework developed by Facebook's AI Research lab (FAIR). Like TensorFlow, PyTorch is designed to facilitate the creation, training, and deployment of machine learning models, particularly deep neural networks.

It has gained popularity for its dynamic computation graph, ease of use, and strong community support.

It's known for its dynamic computation graph, which makes it particularly well-suited for research and experimentation. Some features of PyTorch includes:

  • Dynamic Computation Graph
  • Tensor manipulation
  • Autograd
  • Neural Network Module
  • Eager execution
  • GPU acceleration
  • Dynamic loading of datasets
  • Community and ecosystem

To easily get started with Pytorch, explore the official docs here: Getting started with Pytorch

➡️ Next, we'll look at Computer vision... 🎯