Deep Learning

Deep Learning diploma  80 HR

Module one: Python

why python?
python with AI and ML
Input & Output
Data types
Boolean & Comparison and Logic
If Conditions
For Loops
Built-in functions & Operators
Numbers & Math
Variables Scope
Command Lines
File Handling
Anaconda Environment
Jupyter Notebook
GPU And Google Colab
Object-Oriented Programming (OOP)

Module Two: Artificial Neural Networks

Difference between AI, DL, ML and ANN

Introduction to Neural Networks

Binary Classification

Logistic Regression

Gradient Descent

Deep layer neural network

Forward and Backward Propagation

Regularization and Dropout

Adam optimization algorithm

Tuning process

Multi Classification with Deep Learning

Deep Learning with TensorFlow And Keras

Transfer learning


Module Three: Convolutional Neural Networks

Introduction to Computer Vision

CNN Architecture

Padding & Strided Convolutions

Pooling Layers

Convolutional Neural Networks & Datasets

Object Detection

Non-max Suppression

YOLO Algorithm

Face Verification and Binary Classification

Docker Container