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The Complete AI Data Science Deep Learning Course for Newbies

Deep Learning for Newbies Deep learning is explained mathematically and graphically using Python projects and publications.

What you’ll learn

Introduction to Deep Learning: The Similarities Between Biological and Artificial Neural Networks
Both linear and non-linear activation functions
Some examples of functions are the step function, sign function, linear function, tangent hyperbolic function, leaky reLU function, sigmoid, and softmax.
The ANN Project in Python, an advanced ANN model Backward (ANN) and forward (ANN)
CNN Python project, stride, padding, pooling, flattening, filter or kernel, varieties of filters A Convolutional Neural Network (CNN) is a
RNN models of many-one, many-many, and recurrent neural networks (RNN)

Requirements

Python and machine learning fundamentals.

Description

Deep Learning from the Ground Up This is how machine learning has been expanded. Beginners who wish to understand the basics of artificial intelligence and deep learning should take this course. The course offers graphical explanations, in-depth theory, and video explanations with fundamental introductions. Each part has downloadable materials, including Python code and literature. The lectures are engaging, opulent, and brief. Walking you through everything takes less time. Students in college and university who don’t know much about how machine learning works should definitely study it.

The major objective of creating this course is to clearly and simply explain deep learning and artificial intelligence. Through the online editor collab, all of the codes were completed. Numerous businesses and organizations continue to choose Python. Because it handles much of the complexity for the user and allows novices to concentrate on understanding the major principles rather than the minor details, it has displaced Java as the most popular language for learning to code.