Deep Learning & Neural Networks Python - Keras | Online Course & Certification

Imagine transforming your career with cutting-edge knowledge in Deep Learning and Neural Networks using Python and Keras. This comprehensive course delves into the intricate world of artificial intelligence, providing a robust foundation in both theory and practical application. You’ll explore essential concepts like multi-layer perceptrons, convolutional neural networks, and diverse datasets including Pima Indian Diabetes, Iris Flower, and MNIST Handwritten Digit Recognition.

Through a structured curriculum, the course guides you from the basics of Python to advanced neural network models. You will learn to install and use libraries like Theano and TensorFlow, develop and evaluate models, and implement performance improvements. Techniques such as dropout regularisation and learning rate schedules will enhance your skills, ensuring you can handle real-world AI challenges effectively.

By the end of the course, you’ll be equipped to load, predict, and improve models across various datasets. With practical examples and step-by-step instructions, you will gain the expertise to save and load models, apply image augmentation, and use checkpointing for neural network improvements. This course is an invaluable resource for anyone looking to excel in the AI and machine learning industry.

Learning Outcomes

Master the fundamentals and advanced concepts of deep learning and neural networks using Python and Keras.
Develop proficiency in using Theano and TensorFlow libraries for model development and evaluation.
Gain expertise in handling and improving model performance using dropout regularisation and learning rate schedules.
Acquire skills to implement and evaluate models on various datasets including Pima Indian Diabetes, Iris Flower, and MNIST.
Learn to save, load, and predict using trained models with practical hands-on examples and step-by-step guidance.

Course Introduction and Table of Contents
	Course Introduction and Table of Contents	00:11:00
Deep Learning Overview
	Deep Learning Overview – Theory Session – Part 1	00:06:00
	Deep Learning Overview – Theory Session – Part 2	00:07:00
Choosing Between ML or DL for the next AI project - Quick Theory Session
	Choosing Between ML or DL for the next AI project – Quick Theory Session	00:09:00
Preparing Your Computer
	Preparing Your Computer – Part 1	00:07:00
	Preparing Your Computer – Part 2	00:06:00
Python Basics
	Python Basics – Assignment	00:09:00
	Python Basics – Flow Control	00:09:00
	Python Basics – Functions	00:04:00
	Python Basics – Data Structures	00:12:00
Theano Library Installation and Sample Program to Test
	Theano Library Installation and Sample Program to Test	00:11:00
TensorFlow library Installation and Sample Program to Test
	TensorFlow library Installation and Sample Program to Test	00:09:00
Keras Installation and Switching Theano and TensorFlow Backends
	Keras Installation and Switching Theano and TensorFlow Backends	00:10:00
Explaining Multi-Layer Perceptron Concepts
	Explaining Multi-Layer Perceptron Concepts	00:03:00
Explaining Neural Networks Steps and Terminology
	Explaining Neural Networks Steps and Terminology	00:10:00
First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset
	First Neural Network with Keras – Understanding Pima Indian Diabetes Dataset	00:07:00
Explaining Training and Evaluation Concepts
	Explaining Training and Evaluation Concepts	00:11:00
Pima Indian Model - Steps Explained
	Pima Indian Model – Steps Explained – Part 1	00:09:00
	Pima Indian Model – Steps Explained – Part 2	00:07:00
Coding the Pima Indian Model
	Coding the Pima Indian Model – Part 1	00:11:00
	Coding the Pima Indian Model – Part 2	00:09:00
Pima Indian Model - Performance Evaluation
	Pima Indian Model – Performance Evaluation – Automatic Verification	00:06:00
	Pima Indian Model – Performance Evaluation – Manual Verification	00:08:00
Pima Indian Model - Performance Evaluation - k-fold Validation - Keras
	Pima Indian Model – Performance Evaluation – k-fold Validation – Keras	00:10:00
Pima Indian Model - Performance Evaluation - Hyper Parameters
	Pima Indian Model – Performance Evaluation – Hyper Parameters	00:12:00
Understanding Iris Flower Multi-Class Dataset
	Understanding Iris Flower Multi-Class Dataset	00:08:00
Developing the Iris Flower Multi-Class Model
	Developing the Iris Flower Multi-Class Model – Part 1	00:09:00
	Developing the Iris Flower Multi-Class Model – Part 2	00:06:00
	Developing the Iris Flower Multi-Class Model – Part 3	00:09:00
Understanding the Sonar Returns Dataset
	Understanding the Sonar Returns Dataset	00:07:00
Developing the Sonar Returns Model
	Developing the Sonar Returns Model	00:10:00
Sonar Performance Improvement - Data Preparation - Standardization
	Sonar Performance Improvement – Data Preparation – Standardization	00:15:00
Sonar Performance Improvement - Layer Tuning for Smaller Network
	Sonar Performance Improvement – Layer Tuning for Smaller Network	00:07:00
Sonar Performance Improvement - Layer Tuning for Larger Network
	Sonar Performance Improvement – Layer Tuning for Larger Network	00:06:00
Understanding the Boston Housing Regression Dataset
	Understanding the Boston Housing Regression Dataset	00:07:00
Developing the Boston Housing Baseline Model
	Developing the Boston Housing Baseline Model	00:08:00
Boston Performance Improvement by Standardization
	Boston Performance Improvement by Standardization	00:07:00
Boston Performance Improvement by Deeper Network Tuning
	Boston Performance Improvement by Deeper Network Tuning	00:05:00
Boston Performance Improvement by Wider Network Tuning
	Boston Performance Improvement by Wider Network Tuning	00:04:00
Save & Load the Trained Model as JSON File (Pima Indian Dataset)
	Save & Load the Trained Model as JSON File (Pima Indian Dataset) – Part 1	00:09:00
	Save & Load the Trained Model as JSON File (Pima Indian Dataset) – Part 2	00:08:00
Save and Load Model as YAML File - Pima Indian Dataset
	Save and Load Model as YAML File – Pima Indian Dataset	00:05:00
Load and Predict using the Pima Indian Diabetes Model
	Load and Predict using the Pima Indian Diabetes Model	00:09:00
Load and Predict using the Iris Flower Multi-Class Model
	Load and Predict using the Iris Flower Multi-Class Model	00:08:00
Load and Predict using the Sonar Returns Model
	Load and Predict using the Sonar Returns Model	00:10:00
Load and Predict using the Boston Housing Regression Model
	Load and Predict using the Boston Housing Regression Model	00:08:00
An Introduction to Checkpointing
	An Introduction to Checkpointing	00:06:00
Checkpoint Neural Network Model Improvements
	Checkpoint Neural Network Model Improvements	00:10:00
Checkpoint Neural Network Best Model
	Checkpoint Neural Network Best Model	00:04:00
Loading the Saved Checkpoint
	Loading the Saved Checkpoint	00:05:00
Plotting Model Behavior History
	Plotting Model Behavior History – Introduction	00:06:00
	Plotting Model Behavior History – Coding	00:08:00
Dropout Regularization - Visible Layer
	Dropout Regularization – Visible Layer – Part 1	00:11:00
	Dropout Regularization – Visible Layer – Part 2	00:06:00
Dropout Regularization - Hidden Layer
	Dropout Regularization – Hidden Layer	00:06:00
Learning Rate Schedule using Ionosphere Dataset - Intro
	Learning Rate Schedule using Ionosphere Dataset	00:06:00
Time Based Learning Rate Schedule
	Time Based Learning Rate Schedule – Part 1	00:07:00
	Time Based Learning Rate Schedule – Part 2	00:12:00
Drop Based Learning Rate Schedule
	Drop Based Learning Rate Schedule – Part 1	00:07:00
	Drop Based Learning Rate Schedule – Part 2	00:08:00
Convolutional Neural Networks - Introduction
	Convolutional Neural Networks – Part 1	00:11:00
	Convolutional Neural Networks – Part 2	00:06:00
MNIST Handwritten Digit Recognition Dataset
	Introduction to MNIST Handwritten Digit Recognition Dataset	00:06:00
	Downloading and Testing MNIST Handwritten Digit Recognition Dataset	00:10:00
MNIST Multi-Layer Perceptron Model Development
	MNIST Multi-Layer Perceptron Model Development – Part 1	00:11:00
	MNIST Multi-Layer Perceptron Model Development – Part 2	00:06:00
Convolutional Neural Network Model using MNIST
	Convolutional Neural Network Model using MNIST – Part 1	00:13:00
	Convolutional Neural Network Model using MNIST – Part 2	00:12:00
Large CNN using MNIST
	Large CNN using MNIST	00:09:00
Load and Predict using the MNIST CNN Model
	Load and Predict using the MNIST CNN Model	00:14:00
Introduction to Image Augmentation using Keras
	Introduction to Image Augmentation using Keras	00:11:00
Augmentation using Sample Wise Standardization
	Augmentation using Sample Wise Standardization	00:10:00
Augmentation using Feature Wise Standardization & ZCA Whitening
	Augmentation using Feature Wise Standardization & ZCA Whitening	00:04:00
Augmentation using Rotation and Flipping
	Augmentation using Rotation and Flipping	00:04:00
Saving Augmentation
	Saving Augmentation	00:05:00
CIFAR-10 Object Recognition Dataset - Understanding and Loading
	CIFAR-10 Object Recognition Dataset – Understanding and Loading	00:12:00
Simple CNN using CIFAR-10 Dataset
	Simple CNN using CIFAR-10 Dataset – Part 1	00:09:00
	Simple CNN using CIFAR-10 Dataset – Part 2	00:06:00
	Simple CNN using CIFAR-10 Dataset – Part 3	00:08:00
Train and Save CIFAR-10 Model
	Train and Save CIFAR-10 Model	00:08:00
Load and Predict using CIFAR-10 CNN Model
	Load and Predict using CIFAR-10 CNN Model	00:16:00
RECOMENDED READINGS
	Recomended Readings	00:00:00

Deep Learning & Neural Networks Python - Keras

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Deep Learning & Neural Networks Python - Keras

Learning Outcomes

Why buy this Deep Learning & Neural Networks Python – Keras?

Certification

Who is this course for?

Prerequisites

Career path

Course Curriculum

Related Courses

Grab This Course for

Online Solutions To Comprehensive Training And Learning For Everyone; 7 Days A Week, 365 Days A Year, 24 Hours A Day.

Certificate Validator

EXTRA LINKS

PROGRAMS

CONTACT

© 2024 Studyhub

https://studyhub.org.uk/wp-content/uploads/2021/03/Studyhub-logo-1-1.png