Description
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.
Curriculum
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
