Name: Deep Learning & Neural Networks Python – Keras
Price: 25 GBP

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Curriculum

Overview:

Welcome to the “Deep Learning & Neural Networks Python – Keras” course! This comprehensive program is designed to provide participants with a solid foundation in deep learning and neural networks using the Python programming language and the Keras library. Deep learning has emerged as a powerful tool for solving complex problems in various domains, including image recognition, natural language processing, and predictive analytics. Through this course, participants will explore the principles, algorithms, and applications of deep learning, with a focus on building and training neural networks using Keras.

Interactive video lectures by industry experts
Instant e-certificate and hard copy dispatch by next working day
Fully online, interactive course with Professional voice-over
Developed by qualified first aid professionals
Self paced learning and laptop, tablet, smartphone friendly
24/7 Learning Assistance
Discounts on bulk purchases

Main Course Features:

Introduction to deep learning concepts, including neural networks, activation functions, and gradient descent optimization
Hands-on tutorials and coding exercises using Python and the Keras deep learning framework
Exploration of various neural network architectures, including feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs)
Practical projects and case studies in image classification, text generation, and time series prediction
Guidance on model evaluation, hyperparameter tuning, and regularization techniques to improve model performance
Access to a library of resources, including video lectures, code examples, and supplementary materials
Expert insights and best practices from industry professionals and researchers in the field of deep learning
Opportunities for networking and collaboration with peers through online forums, discussion groups, and project work

Who Should Take This Course:

Data scientists and machine learning engineers interested in deepening their understanding of neural networks and Keras
Python developers looking to expand their skill set into the field of deep learning and artificial intelligence
Students and researchers seeking to explore advanced topics in deep learning and apply them to real-world problems
Professionals working in industries such as healthcare, finance, and technology, where deep learning has significant applications
Anyone interested in mastering the principles and techniques of deep learning using the Python programming language and Keras framework

Learning Outcomes:

Gain a solid understanding of deep learning principles, architectures, and algorithms
Develop proficiency in building and training neural networks using the Keras library
Learn how to apply deep learning techniques to solve a variety of real-world problems
Explore advanced topics in deep learning, including CNNs, RNNs, and autoencoders
Acquire practical skills in evaluating, tuning, and deploying deep learning models
Build a portfolio of deep learning projects showcasing various applications and domains
Stay updated on the latest advancements and trends in deep learning and neural networks
Demonstrate proficiency in implementing deep learning solutions using Python and Keras through hands-on projects and assessments.

Certification

Once you’ve successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Assessment

At the end of the Course, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven’t, there’s no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself.

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	00:00:00