Utilizing Functional Programming Paradigms in Python: Best Practices and Applications
Utilizing Functional Programming Paradigms in Python: Best Practices and Applications
Python Libraries and Frameworks
NumPy is a powerful Python library for numerical and scientific computing that provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.
TensorFlow is an open-source machine learning framework developed by Google that enables the creation and training of deep learning models through the use of computational graphs and a wide range of neural network layers and optimizers.
Theano was an open-source numerical computation library for Python that was widely used for deep learning and scientific computing but has been largely replaced by more modern deep learning frameworks like TensorFlow and PyTorch.
Finding An Effective Python Environment
pyenv
is a polular and marture open-source tool used in the Python development ecosystem on MacOS. (It is pyenv-win on windows). It allows developers to manage multiple Python installations on their system and easily switch between different Python versions. This is especially useful when you’re working on various Python projects that may have different version requirements.
1 | $ pyenv verison |
Creating a Neural Network and Making Predictions with Python's AI
If you’re new to the field of artificial intelligence (AI), Python is an excellent language to begin with because it serves as the foundation for many AI tools and libraries. Deep learning, a method for making predictions based on data, heavily depends on neural networks. This blog will show you through the process of constructing a neural network from the ground up.
In practical applications, rather than creating neural networks from scratch, you would typically utilize deep learning frameworks like TensorFlow or PyTorch. Nevertheless, understanding the inner workings of neural networks can be advantageous as it allows you to design more effective architectures for your deep learning models.