Deep Learning diploma 80 HR
Module one: Python
why python?
python with AI and ML
Input & Output
Variables
Data types
Boolean & Comparison and Logic
If Conditions
For Loops
Built-in functions & Operators
Numbers & Math
Functions
Variables Scope
Modules
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
Projects
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