Hallos

Deep Learning & Neural Networks Python – Keras

Deep Learning & Neural Networks Python – Keras

  • 11 hours
  • Self-paced online
  • Certificate on completion

Part of IT & Software courses

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

  1. Course Introduction and Table of Contents
  2. Deep Learning Overview
  3. Choosing Between ML or DL for the next AI project - Quick Theory Session
  4. Preparing Your Computer
  5. Python Basics
  6. Theano Library Installation and Sample Program to Test
  7. TensorFlow library Installation and Sample Program to Test
  8. Keras Installation and Switching Theano and TensorFlow Backends
  9. Explaining Multi-Layer Perceptron Concepts
  10. Explaining Neural Networks Steps and Terminology
  11. First Neural Network with Keras - Understanding Pima Indian Diabetes Dataset
  12. Explaining Training and Evaluation Concepts
  13. Pima Indian Model - Steps Explained
  14. Coding the Pima Indian Model
  15. Pima Indian Model - Performance Evaluation
  16. Pima Indian Model - Performance Evaluation - k-fold Validation - Keras
  17. Pima Indian Model - Performance Evaluation - Hyper Parameters
  18. Understanding Iris Flower Multi-Class Dataset
  19. Developing the Iris Flower Multi-Class Model
  20. Understanding the Sonar Returns Dataset
  21. Developing the Sonar Returns Model
  22. Sonar Performance Improvement - Data Preparation - Standardization
  23. Sonar Performance Improvement - Layer Tuning for Smaller Network
  24. Sonar Performance Improvement - Layer Tuning for Larger Network
  25. Understanding the Boston Housing Regression Dataset
  26. Developing the Boston Housing Baseline Model
  27. Boston Performance Improvement by Standardization
  28. Boston Performance Improvement by Deeper Network Tuning
  29. Boston Performance Improvement by Wider Network Tuning
  30. Save & Load the Trained Model as JSON File (Pima Indian Dataset)
  31. Save and Load Model as YAML File - Pima Indian Dataset
  32. Load and Predict using the Pima Indian Diabetes Model
  33. Load and Predict using the Iris Flower Multi-Class Model
  34. Load and Predict using the Sonar Returns Model
  35. Load and Predict using the Boston Housing Regression Model
  36. An Introduction to Checkpointing
  37. Checkpoint Neural Network Model Improvements
  38. Checkpoint Neural Network Best Model
  39. Loading the Saved Checkpoint
  40. Plotting Model Behavior History
  41. Dropout Regularization - Visible Layer
  42. Dropout Regularization - Hidden Layer
  43. Learning Rate Schedule using Ionosphere Dataset - Intro
  44. Time Based Learning Rate Schedule
  45. Drop Based Learning Rate Schedule
  46. Convolutional Neural Networks - Introduction
  47. MNIST Handwritten Digit Recognition Dataset
  48. MNIST Multi-Layer Perceptron Model Development
  49. Convolutional Neural Network Model using MNIST
  50. Large CNN using MNIST
  51. Load and Predict using the MNIST CNN Model
  52. Introduction to Image Augmentation using Keras
  53. Augmentation using Sample Wise Standardization
  54. Augmentation using Feature Wise Standardization & ZCA Whitening
  55. Augmentation using Rotation and Flipping
  56. Saving Augmentation
  57. CIFAR-10 Object Recognition Dataset - Understanding and Loading
  58. Simple CNN using CIFAR-10 Dataset
  59. Train and Save CIFAR-10 Model
  60. Load and Predict using CIFAR-10 CNN Model
  61. RECOMENDED READINGS