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Course Curriculum

Course Curriculum

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 00:06:00
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 00:09:00
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 – Deep Learning & Neural Networks Python – Keras 00:00:00

Deep Learning & Neural Networks Python - Keras

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Duration: 11.15 Hours
$34.75
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  • Course Highlights

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step training course. This Deep Learning & Neural Networks Python – Keras has been specially designed to help learners gain a good command of Deep Learning & Neural Networks Python – Keras, providing them with a solid foundation of knowledge to become a qualified professional.

Through this Deep Learning & Neural Networks Python – Keras, you will gain both practical and theoretical understanding of Deep Learning & Neural Networks Python – Keras that will increase your employability in this field, help you stand out from the competition and boost your earning potential in no time.

Not only that, but this training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. This qualification is fully accredited, broken down into several manageable modules, ideal for aspiring professionals.

  • Learning outcome
  • Get a deep understanding of the Deep Learning & Neural Networks Python – Keras just in hours not years
  • Familiar yourself with the recent development and updates of the relevant industry
  • Know how to use your theoretical and practical knowledge to adapt in any working environment
  • Get help from our expert tutors anytime you need
  • Access to course contents that are designed and prepared by industry professionals
  • Study at your convenient time and from wherever you want
  • Requirements
  • No formal qualifications required, anyone from any academic background can take this course.
  • Access to any internet-enabled smart device.
  • Why should I take this course?
  • Affordable premium-quality E-learning content, you can learn at your own pace.
  • You will receive a completion certificate upon completing the course.
  • Internationally recognized Accredited Qualification will boost up your resume.
  • You will learn the researched and proven approach adopted by successful people to transform their careers.
  • You will be able to incorporate various practical techniques successfully and understand your customers better.

Testimonial

Hear what our happy students want to say
The modules are easy to understand and there is help available if required. Besides great collection of exciting topics which make you come back for more.
It was a pleasure to do a course in SkillUp. The course is well organized and I could study at my own pace.
Super varied course content. From Healthcare and fitness to Therapeutic courses. What I particularly like are the course modules and the way they are arranged gradually. With my nutritional background, I have come to know and love completely different perspectives.
Recommendable! A unique experience, the courses have every detail covered. Enjoyed the course through and through.
The price is reasonable, and you know upfront of your costs. I would recommend Skill Up to learners who want to excel their skills or looking for self-development.
The modules are easy to understand and there is help available if required. Besides great collection of exciting topics which make you come back for more.
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