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Deep Learning & Neural Networks Python - Keras

Course Features

Overview

By enroling in Deep Learning & Neural Networks Python – Keras, you can kickstart your vibrant career and strengthen your profound knowledge. You can learn everything you need to know about the topic.

The Deep Learning & Neural Networks Python – Keras course includes all of the most recent information to keep you abreast of the employment market and prepare you for your future. The curriculum for this excellent Deep Learning & Neural Networks Python – Keras course includes modules at all skill levels, from beginner to expert. You will have the productivity necessary to succeed in your organisation once you have completed our Deep Learning & Neural Networks Python – Keras Program.

So enrol in our Deep Learning & Neural Networks Python – Keras course right away if you’re keen to envision yourself in a rewarding career.

Description

Enroling in this Deep Learning & Neural Networks Python – Keras course can improve your Deep Learning & Neural Networks Python – Keras perspective, regardless of your skill levels in the Deep Learning & Neural Networks Python – Keras topics you want to master. If you’re already a Deep Learning & Neural Networks Python – Keras expert, this peek under the hood will provide you with suggestions for accelerating your learning, including advanced Deep Learning & Neural Networks Python – Keras insights that will help you make the most of your time. This Deep Learning & Neural Networks Python – Keras course will act as a guide for you if you’ve ever wished to excel at Deep Learning & Neural Networks Python – Keras.

Why Choose Us?​​

Certificate of Achievement

Upon successful completion, you will qualify for the UK and internationally-recognised CPD certificate and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £4.99 and Hardcopy Certificate for £9.99.

Who Is This Course For?​

This Deep Learning & Neural Networks Python – Keras course is a great place to start if you’re looking to start a new career in Deep Learning & Neural Networks Python – Keras field. This training is for anyone interested in gaining in-demand Deep Learning & Neural Networks Python – Keras proficiency to help launch a career or their business aptitude. 

Requirements​

The Deep Learning & Neural Networks Python – Keras course requires no prior degree or experience. All you require is English proficiency, numeracy literacy and a gadget with stable internet connection. Learn and train for a prosperous career in the thriving and fast-growing industry of Deep Learning & Neural Networks Python – Keras, without any fuss.

Career Path​

This Deep Learning & Neural Networks Python – Keras training will assist you develop your Deep Learning & Neural Networks Python – Keras ability, establish a personal brand, and present a portfolio of relevant talents. It will help you articulate a  Deep Learning & Neural Networks Python – Keras professional story and personalise your path to a new career. Furthermore, developing this Deep Learning & Neural Networks Python – Keras skillset can lead to numerous opportunities for high-paying jobs in a variety of fields.

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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 00:07:00
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
Order Your Certificate
Order Your Certificate 00:00:00

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