This post is about four important neural network layer architectures — the building blocks that machine learning engineers use to construct deep learning models. Fully connected, convolution, LSTM & attention all illustrated and explained.
This post is about four important neural network layer architectures — the building blocks that machine learning engineers use to construct deep learning models:
For each layer we will look at:
All code examples are built using
tensorflow==2.2.0 using the Keras Functional API.
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.
What is the difference between machine learning and artificial intelligence and deep learning? Supervised learning is best for classification and regressions Machine Learning models. You can read more about them in this article.
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.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
This article will simply explain the concept which will help you understand the difference between Machine Learning and Deep Learning.