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. 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.
We guarantee that all our online courses will meet or exceed your
expectations. If you are not fully satisfied with a course - for
any reason at all - simply request a full refund. We guarantee no
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Course Curriculum
| Course Introduction and Table of Contents | |||
| Course Introduction and Table of Contents | |||
| Deep Learning Overview | |||
| Deep Learning Overview – Theory Session – Part 1 | |||
| Deep Learning Overview – Theory Session – Part 2 | |||
| 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 | |||
| Preparing Your Computer | |||
| Preparing Your Computer – Part 1 | |||
| Preparing Your Computer – Part 2 | |||
| Python Basics | |||
| Python Basics – Assignment | |||
| Python Basics – Flow Control | |||
| Python Basics – Functions | |||
| Python Basics – Data Structures | |||
| Theano Library Installation and Sample Program to Test | |||
| Theano Library Installation and Sample Program to Test | |||
| TensorFlow library Installation and Sample Program to Test | |||
| TensorFlow library Installation and Sample Program to Test | |||
| Keras Installation and Switching Theano and TensorFlow Backends | |||
| Keras Installation and Switching Theano and TensorFlow Backends | |||
| Explaining Multi-Layer Perceptron Concepts | |||
| Explaining Multi-Layer Perceptron Concepts | |||
| Explaining Neural Networks Steps and Terminology | |||
| Explaining Neural Networks Steps and Terminology | |||
| First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset | |||
| First Neural Network with Keras – Understanding Pima Indian Diabetes Dataset | |||
| Explaining Training and Evaluation Concepts | |||
| Explaining Training and Evaluation Concepts | |||
| Pima Indian Model - Steps Explained | |||
| Pima Indian Model – Steps Explained – Part 1 | |||
| Pima Indian Model – Steps Explained – Part 2 | |||
| Coding the Pima Indian Model | |||
| Coding the Pima Indian Model – Part 1 | |||
| Coding the Pima Indian Model – Part 2 | |||
| Pima Indian Model - Performance Evaluation | |||
| Pima Indian Model – Performance Evaluation – Automatic Verification | |||
| Pima Indian Model – Performance Evaluation – Manual Verification | |||
| Pima Indian Model - Performance Evaluation - k-fold Validation - Keras | |||
| Pima Indian Model – Performance Evaluation – k-fold Validation – Keras | |||
| Pima Indian Model - Performance Evaluation - Hyper Parameters | |||
| Pima Indian Model – Performance Evaluation – Hyper Parameters | |||
| Understanding Iris Flower Multi-Class Dataset | |||
| Understanding Iris Flower Multi-Class Dataset | |||
| Developing the Iris Flower Multi-Class Model | |||
| Developing the Iris Flower Multi-Class Model – Part 1 | |||
| Developing the Iris Flower Multi-Class Model – Part 2 | |||
| Developing the Iris Flower Multi-Class Model – Part 3 | |||
| Understanding the Sonar Returns Dataset | |||
| Understanding the Sonar Returns Dataset | |||
| Developing the Sonar Returns Model | |||
| Developing the Sonar Returns Model | |||
| Sonar Performance Improvement - Data Preparation - Standardization | |||
| Sonar Performance Improvement – Data Preparation – Standardization | |||
| Sonar Performance Improvement - Layer Tuning for Smaller Network | |||
| Sonar Performance Improvement – Layer Tuning for Smaller Network | |||
| Sonar Performance Improvement - Layer Tuning for Larger Network | |||
| Sonar Performance Improvement – Layer Tuning for Larger Network | |||
| Understanding the Boston Housing Regression Dataset | |||
| Understanding the Boston Housing Regression Dataset | |||
| Developing the Boston Housing Baseline Model | |||
| Developing the Boston Housing Baseline Model | |||
| Boston Performance Improvement by Standardization | |||
| Boston Performance Improvement by Standardization | |||
| Boston Performance Improvement by Deeper Network Tuning | |||
| Boston Performance Improvement by Deeper Network Tuning | |||
| Boston Performance Improvement by Wider Network Tuning | |||
| Boston Performance Improvement by Wider Network Tuning | |||
| Save & Load the Trained Model as JSON File (Pima Indian Dataset) | |||
| Save & Load the Trained Model as JSON File (Pima Indian Dataset) – Part 1 | |||
| Save & Load the Trained Model as JSON File (Pima Indian Dataset) – Part 2 | |||
| Save and Load Model as YAML 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 Pima Indian Diabetes Model | |||
| Load and Predict using the Iris Flower Multi-Class Model | |||
| Load and Predict using the Iris Flower Multi-Class Model | |||
| Load and Predict using the Sonar Returns Model | |||
| Load and Predict using the Sonar Returns Model | |||
| Load and Predict using the Boston Housing Regression Model | |||
| Load and Predict using the Boston Housing Regression Model | |||
| An Introduction to Checkpointing | |||
| An Introduction to Checkpointing | |||
| Checkpoint Neural Network Model Improvements | |||
| Checkpoint Neural Network Model Improvements | |||
| Checkpoint Neural Network Best Model | |||
| Checkpoint Neural Network Best Model | |||
| Loading the Saved Checkpoint | |||
| Loading the Saved Checkpoint | |||
| Plotting Model Behavior History | |||
| Plotting Model Behavior History – Introduction | |||
| Plotting Model Behavior History – Coding | |||
| Dropout Regularization - Visible Layer | |||
| Dropout Regularization – Visible Layer – Part 1 | |||
| Dropout Regularization – Visible Layer – Part 2 | |||
| Dropout Regularization - Hidden Layer | |||
| Dropout Regularization – Hidden Layer | |||
| Learning Rate Schedule using Ionosphere Dataset - Intro | |||
| Learning Rate Schedule using Ionosphere Dataset | |||
| Time Based Learning Rate Schedule | |||
| Time Based Learning Rate Schedule – Part 1 | |||
| Time Based Learning Rate Schedule – Part 2 | |||
| Drop Based Learning Rate Schedule | |||
| Drop Based Learning Rate Schedule – Part 1 | |||
| Drop Based Learning Rate Schedule – Part 2 | |||
| Convolutional Neural Networks - Introduction | |||
| Convolutional Neural Networks – Part 1 | |||
| Convolutional Neural Networks – Part 2 | |||
| MNIST Handwritten Digit Recognition Dataset | |||
| Introduction to MNIST Handwritten Digit Recognition Dataset | |||
| Downloading and Testing MNIST Handwritten Digit Recognition Dataset | |||
| MNIST Multi-Layer Perceptron Model Development | |||
| MNIST Multi-Layer Perceptron Model Development – Part 1 | |||
| MNIST Multi-Layer Perceptron Model Development – Part 2 | |||
| Convolutional Neural Network Model using MNIST | |||
| Convolutional Neural Network Model using MNIST – Part 1 | |||
| Convolutional Neural Network Model using MNIST – Part 2 | |||
| Large CNN using MNIST | |||
| Large CNN using MNIST | |||
| Load and Predict using the MNIST CNN Model | |||
| Load and Predict using the MNIST CNN Model | |||
| Introduction to Image Augmentation using Keras | |||
| Introduction to Image Augmentation using Keras | |||
| Augmentation using Sample Wise Standardization | |||
| Augmentation using Sample Wise Standardization | |||
| Augmentation using Feature Wise Standardization & ZCA Whitening | |||
| Augmentation using Feature Wise Standardization & ZCA Whitening | |||
| Augmentation using Rotation and Flipping | |||
| Augmentation using Rotation and Flipping | |||
| Saving Augmentation | |||
| Saving Augmentation | |||
| CIFAR-10 Object Recognition Dataset - Understanding and Loading | |||
| CIFAR-10 Object Recognition Dataset – Understanding and Loading | |||
| Simple CNN using CIFAR-10 Dataset | |||
| Simple CNN using CIFAR-10 Dataset – Part 1 | |||
| Simple CNN using CIFAR-10 Dataset – Part 2 | |||
| Simple CNN using CIFAR-10 Dataset – Part 3 | |||
| Train and Save CIFAR-10 Model | |||
| Train and Save CIFAR-10 Model | |||
| Load and Predict using CIFAR-10 CNN Model | |||
| Load and Predict using CIFAR-10 CNN Model | |||
| RECOMENDED READINGS | |||
| Recomended Readings | |||