This course provides an in-depth tutorial on Deep Learning in 5 hours. The learning outcomes include understanding the differences between AI, ML, DL, and Data Science, grasping the working principles of Perceptron, learning about activation functions, loss functions, optimizers, and practical implementation of Artificial Neural Networks and Convolutional Neural Networks. The teaching method involves a series of video tutorials covering various topics with timestamps. The course is intended for individuals interested in deepening their knowledge of Deep Learning concepts and applications.
Overview
Syllabus
Introduction
AI vs ML vs DL vs Data Science
Why Deep Learning Is Becoming Popular?
Introduction To Perceptron
Working Of Perceptron With Weights And Bias
Forward Propogation,Backward Propogation And Weight Updateion Formula
Chain Rule Of Derivatives
Vanishing Gradient Problem
Different types Of Activation Functions
Different types Of Loss functions
Different type Of Optimizers
Practical Implementation OF ANN
Black Box Models VsWhite Box Models
Convolutional Neural Network
Practical Implementation Of CNN
Taught by
Krish Naik
