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Advanced Deep Learning – Part 1

We are going to master how Deep Learning works by implementing different algorithm like CNN, LSTM & fundamental essential of Deep Learning (Channels, Kernels, & filters etc.). This is the first part of the course where in next part we will be implementing custom object detection using YOLO.

Course Overview

A convolutional neural network (CNN) is a feed-forward neural network that is generally used to analyze visual images by processing data with grid-like topology. Everyone learns CNN & sequential modeling but is lagging with mathematical intuition behind the algorithm & essential concepts like channels, kernels & filters which are backbones of Deep Learning. 

As you progress in this course, you are going to learn how Convolutional Neural Network (CNN) works & mathematics behind different algorithm like CNN & LSTM. Building a model is not the only motive but also to observe how well the model is performing & you will know how activation functions work & why it is important for networks, with practical implementation from scratch. 

By the end of the course, you are going to master the sequential modeling behind Deep Learning. Instead of learning different models, you are going to master the in-depth mathematical intuition while implementing different algorithms.

What You Will Learn

Inside the Course

8 Lessons | 26 Topics | 8 Quizzes

Course Content

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