Deep Learning & Neural Networks Python

Course details

• Understand the fundamental concepts of deep learning and how it differs from traditional machine learning.
• Gain proficiency in using Keras, a powerful deep learning library, for building and training neural network models.
• Develop practical skills in creating and optimizing neural network models for different datasets, including image recognition tasks and regression problems.

• Course Introduction and Table of Contents
• Deep Learning Overview
• Choosing Between ML or DL for the next AI project - Quick Theory Session
• Preparing Your Computer
• Python Basics
• Theano Library Installation and Sample Program to Test
• TensorFlow library Installation and Sample Program to Test
• Keras Installation and Switching Theano and TensorFlow Backends
• Explaining Multi-Layer Perceptron Concepts
• Explaining Neural Networks Steps and Terminology
• First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset
• Explaining Training and Evaluation Concepts
• Pima Indian Model - Steps Explained
• Coding the Pima Indian Model
• Pima Indian Model - Performance Evaluation
• Pima Indian Model - Performance Evaluation - k-fold Validation - Keras
• Pima Indian Model - Performance Evaluation - Hyper Parameters
• Understanding Iris Flower Multi-Class Dataset
• Developing the Iris Flower Multi-Class Model
• Understanding the Sonar Returns Dataset
• Developing the Sonar Returns Model
• Sonar Performance Improvement - Data Preparation - Standardization
• Sonar Performance Improvement - Layer Tuning for Smaller Network
• Sonar Performance Improvement - Layer Tuning for Larger Network
• Understanding the Boston Housing Regression Dataset
• Developing the Boston Housing Baseline Model
• Boston Performance Improvement by Standardization
• Boston Performance Improvement by Deeper Network Tuning
• Boston Performance Improvement by Wider Network Tuning
• Save & Load the Trained Model as JSON File (Pima Indian Dataset)
• Save and Load Model as YAML File - Pima Indian Dataset
• Load and Predict using the Pima Indian Diabetes Model
• Load and Predict using the Iris Flower Multi-Class Model
• Load and Predict using the Sonar Returns Model
• Load and Predict using the Boston Housing Regression Model
• An Introduction to Checkpointing
• Checkpoint Neural Network Model Improvements
• Checkpoint Neural Network Best Model
• Loading the Saved Checkpoint
• Plotting Model Behavior History
• Dropout Regularization - Visible Layer
• Dropout Regularization - Hidden Layer
• Learning Rate Schedule using Ionosphere Dataset - Intro
• Time Based Learning Rate Schedule
• Drop Based Learning Rate Schedule
• Convolutional Neural Networks - Introduction
• MNIST Handwritten Digit Recognition Dataset
• MNIST Multi-Layer Perceptron Model Development
• Convolutional Neural Network Model using MNIST
• Large CNN using MNIST
• Load and Predict using the MNIST CNN Model
• Introduction to Image Augmentation using Keras
• Augmentation using Sample Wise Standardization
• Augmentation using Feature Wise Standardization & ZCA Whitening
• Augmentation using Rotation and Flipping
• Saving Augmentation
• CIFAR-10 Object Recognition Dataset - Understanding and Loading
• Simple CNN using CIFAR-10 Dataset
• Train and Save CIFAR-10 Model
• Load and Predict using CIFAR-10 CNN Model
• RECOMENDED READINGS

• Unlimited access to the course for forever
• Digital Certificate, Transcript, student ID all included in the price
• Absolutely no hidden fees
• Directly receive CPD accredited qualifications after course completion
• Receive one to one assistance on every weekday from professionals
• Immediately receive the PDF certificate after passing
• Receive the original copies of your certificate and transcript on the next working day
• Easily learn the skills and knowledge from the comfort of your home