Neural Networks in Python from Scratch: Complete guide
Learn the fundamentals of Deep Learning of neural networks in Python both in theory and practice!
Created by Jones Granatyr, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team | 8.5 hours on-demand video course
Artificial neural networks are considered to be the most efficient Machine Learning techniques nowadays, with companies the likes of Google, IBM and Microsoft applying them in a myriad of ways. You’ve probably heard about self-driving cars or applications that create new songs, poems, images and even entire movie scripts! The interesting thing about this is that most of these were built using neural networks. Neural networks have been used for a while, but with the rise of Deep Learning, they came back stronger than ever and now are seen as the most advanced technology for data analysis.
What you’ll learn
- Learn step by step all the mathematical calculations involving artificial neural networks
- Implement neural networks in Python and Numpy from scratch
- Understand concepts like perceptron, activation functions, backpropagation, gradient descent, learning rate, and others
- Build neural networks applied to classification and regression tasks
- Implement neural networks using libraries, such as: Pybrain, sklearn, TensorFlow, and PyTorch
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