Overview What You Will Learn Curriculum Instructor

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 a 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 the backbones of Deep Learning. 

As you progress in this course, you are going to learn how Convolutional Neural Networks (CNN) works & mathematics behind different algorithms like CNN & LSTM. Building a model is not the only motive but also observing 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 in-depth mathematical intuition while implementing different algorithms.

What You Will Learn

  • You will learn about Deep Learning and the concept of Neuron
  • kernel
  • receptive field
  • etc.
  • You will learn how Deep Learning algorithms process unstructured data to extract more features.
  • You will learn about different architecture used in deep learning and its implementation for industrial use.
  • You will learn to examine convolutional neural networks and the recurrent neural network connections to a feed-forward neural network.

Program Curriculum

  • Introduction to Deep Learning
  • Applications of Deep Learning
  • How Images are Represented Digitally?
  • $7 Million Cybersecurity Scholarship by EC-Council
  • Chapter 1 Quiz

  • What are Neurons?
  • Difference Between Machine Learning Algorithm and Neural Networks
  • Intuition Behind Neural Networks
  • Chapter 2 Quiz

  • What are Channels and Kernels?
  • What is Convolution?
  • Padding & Pooling Operations
  • Chapter 3 Quiz

  • Flattering and Fully Connected Network
  • Deep Dive into Receptive Field
  • Different Types of Convolution
  • Batch Normalization
  • Chapter 4 Quiz

  • Why Do We Need Activation Functions?
  • Different Types of Activation Functions
  • Intuition Behind Gradient Descent
  • Chapter 5 Quiz

  • Problem Statement
  • Importing Libraries & Data Preprocessing
  • Creating Model
  • Model Evaluation
  • Chapter 6 Quiz

  • What Are Autoencoders and Where to Use Them?
  • Application of Autoencoders on Clustering
  • Applications of Auto encoder on Images
  • Chapter 7 Quiz

  • Problems with RNN and Application of LSTM
  • How Does LSTM work?
  • Implementation of LSTM
  • Chapter 8 Quiz
Load more modules

Instructor

Vivek Chaudhary chaudhary

Vivek Chaudhary currently works as a freelance data scientist and has worked with different product-based & EdTech startups. He has published one of the best-selling books on Amazon, “Data Investigation-EDA the right way”. His areas of expertise are applied statistics, EDA, data cleaning techniques, and feature engineering and process to building statistical models. According to him “Building Assumptions” is the important factor to apply statistical tools in real-time. If you can’t build assumptions, then no matter how much you learn at the end, it will be difficult to apply statistical techniques. He has mentored 200+ professionals to start their journey and helped them understand applied statistics & EDA.

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