Create a convolutional layer from scratch in python, hack its weights with custom kernels, and verify that its results match with pytorch
If you are starting to work with convolutional layers in deep learning you may be confused at times with the mix of parameters, computations and channels involved. From stripe to padding, input and output channels, kernels and learnable parameters, there is a lot going on. In this article, we are going to go deep till the very bottom of what goes on within these conv layers. We will: Code from scratch in python a convolutional layer to understand bit by bit what is going on when we pass data through one of these layers. Hack the parameters of the convnet to produce a mix of custom kernels and random ones. Compare the results using Pytorch and Python-numpy to verify that we obtain exactly the same outputs. Ready? Let’s do it.
This "Deep Learning vs Machine Learning vs AI vs Data Science" video talks about the differences and relationship between Artificial Intelligence, Machine Learning, Deep Learning, and Data Science.
Artificial Intelligence (AI) vs Machine Learning vs Deep Learning vs Data Science: Artificial intelligence is a field where set of techniques are used to make computers as smart as humans. Machine learning is a sub domain of artificial intelligence where set of statistical and neural network based algorithms are used for training a computer in doing a smart task. Deep learning is all about neural networks. Deep learning is considered to be a sub field of machine learning. Pytorch and Tensorflow are two popular frameworks that can be used in doing deep learning.
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