This course is designed for those interested to learn the basics of Deep Learning and Neural Networks, how to implement them using Python and several libraries, and how to create real-world Deep Learning models. Read more.
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Get Started with All Access PassBuy Only This CourseAbout This Course
Who this course is for:
- Programmers
- Data Scientists
- Data Engineers
What you’ll learn:Â
- Theory sessions to see an overview about the Deep Learning and Neural Networks
- The basics of Python
- Theano, Tensorflow and Keras, the best and most popular Deep Learning libraries
- Multi-Layer perceptrons, the basic element of the Deep Learning Neural Network
- creating real-world deep learning models using several datasets
- Convolutional Neural Networks
- Object recognition in image data
- Object recognition in photographs
Requirements:Â
- No prior knowledge is required to take this course
The world has been revolving much around the terms “Machine Learning” and “Deep Learning” recently. With or without our knowledge, every day we are using these technologies ranging from Google suggestions, translations, ads, movie recommendations, friend suggestions, sales, customer experience, so on and so forth. There are tons of other applications, too. No wonder why “Deep Learning” and “Machine Learning along with Data Science” are the most sought after talent in the technology world nowadays.
But the problem is that, when you think about learning these technologies, a misconception that lots of maths, statistics, complex algorithms and formulas need to be studied. It’s just like someone tries to make you believe that you should learn the working of an internal combustion engine before you learn how to drive a car. The fact is that, to drive a car, we just only need to know how to use the user-friendly control pedals extending from the engine like clutch, brake, accelerator, steering wheel, etc. And with a bit of experience, you can easily drive a car.
The basic know-how about the internal working of the engine is of course an added advantage while driving a car, but it’s not mandatory. Just like that, in our Deep Learning course, we have a perfect balance between learning the basic concepts along the implementation of the built-in Deep Learning classes and functions from the Keras Library using the Python Programming Language. These classes, functions, and APIs are just like the control pedals from the car engine, which we can use easily to build an efficient deep learning model.
Overall, this is a basic to advanced crash course in Deep Learning Neural Networks and Convolutional Neural Networks using Keras and Python, which I am sure once you completed will sky rocket your current career prospects as this is the most wanted skill nowadays and of course this is the technology of the future.Â
There is a day in the near future itself, when the deep learning models will outperform human intelligence. So, be ready and let’s dive into the world of thinking machines.
Our Promise to You
By the end of this course, you will have learned how to implement Deep Learning and use Neural Networks.
10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.
Get started today and learn more about Deep Learning And Neural Networks.
Course Curriculum
| Section 1 - Introduction | |||
| Course Introduction And Table Of Contents | 00:00:00 | ||
| Course Resources | 00:00:00 | ||
| Deep Learning Overview - Theory Session - Part 1 | 00:00:00 | ||
| Deep Learning Overview - Theory Session - Part 2 | 00:00:00 | ||
| Choosing Between ML Or DL For The Next Ai Project - Quick Theory Session | 00:00:00 | ||
| Preparing Your Computer - Part 1 | 00:00:00 | ||
| Important: TensorFlow Incompatibility | 00:00:00 | ||
| Preparing Your Computer - Part 2 | 00:00:00 | ||
| Section 2 - Python Basics | |||
| Python Basics - Assignment | 00:00:00 | ||
| Python Basics - Flow Control | 00:00:00 | ||
| Python Basics - Functions | 00:00:00 | ||
| Python Basics - Data Structures | 00:00:00 | ||
| Section 3 - Libraries | |||
| Theano Library Installation And Sample Program To Test | 00:00:00 | ||
| TensorFlow Library Installation And Sample Program To Test | 00:00:00 | ||
| Keras Installation And Switching Theano And TensorFlow Backends | 00:00:00 | ||
| Section 4 - Multi-Layer Perceptron Concepts | |||
| Explaining Multi-Layer Perceptron Concepts | 00:00:00 | ||
| Section 5 - Neural Networks Steps And Terminology | |||
| Explaining Neural Networks Steps And Terminology | 00:00:00 | ||
| Section 6 - Pima Indian Diabetes Dataset | |||
| First Neural Network With Keras - Understanding Pima Indian Diabetes Dataset | 00:00:00 | ||
| Explaining Training And Evaluation Concepts | 00:00:00 | ||
| Pima Indian Model - Steps Explained - Part 1 | 00:00:00 | ||
| Pima Indian Model - Steps Explained - Part 2 | 00:00:00 | ||
| Coding The Pima Indian Model - Part 1 | 00:00:00 | ||
| Coding The Pima Indian Model - Part 2 | 00:00:00 | ||
| Performance Evaluation - Automatic Verification | 00:00:00 | ||
| Performance Evaluation - Manual Verification | 00:00:00 | ||
| Performance Evaluation - K-Fold Validation - Keras | 00:00:00 | ||
| Performance Evaluation - Hyper Parameters | 00:00:00 | ||
| Section 7 - Iris Flower Multi-Class Dataset | |||
| Understanding Iris Flower Multi-Class Dataset | 00:00:00 | ||
| Developing The Iris Flower Multi-Class Model - Part 1 | 00:00:00 | ||
| Developing The Iris Flower Multi-Class Model - Part 2 | 00:00:00 | ||
| Developing The Iris Flower Multi-Class Model - Part 3 | 00:00:00 | ||
| Section 8 - Sonar Returns Dataset | |||
| Understanding The Sonar Returns Dataset | 00:00:00 | ||
| Developing The Sonar Returns Model | 00:00:00 | ||
| Sonar Performance Improvement - Data Preparation - Standardization | 00:00:00 | ||
| Sonar Performance Improvement - Layer Tuning For Smaller Network | 00:00:00 | ||
| Sonar Performance Improvement - Layer Tuning For Larger Network | 00:00:00 | ||
| Section 9 - Boston Housing Regression Dataset | |||
| Understanding The Boston Housing Regression Dataset | 00:00:00 | ||
| Developing The Boston Housing Baseline Model | 00:00:00 | ||
| Boston Performance Improvement By Standardization | 00:00:00 | ||
| Boston Performance Improvement By Deeper Network Tuning | 00:00:00 | ||
| Boston Performance Improvement By Wider Network Tuning | 00:00:00 | ||
| Section 10 - Save, Load, And Predict | |||
| Save And Load The Trained Model As JSON File (Pima Indian Dataset) - Part 1 | 00:00:00 | ||
| Save And Load The Trained Model As JSON File (Pima Indian Dataset) - Part 2 | 00:00:00 | ||
| Save And Load Model As YAML File - Pima Indian Dataset | 00:00:00 | ||
| Load And Predict Using The Pima Indian Diabetes Model | 00:00:00 | ||
| Load And Predict Using The Iris Flower Multi-Class Model | 00:00:00 | ||
| Load And Predict Using The Sonar Returns Model | 00:00:00 | ||
| Load And Predict Using The Boston Housing Regression Model | 00:00:00 | ||
| Section 11 - Checkpoint | |||
| Introduction To Checkpointing | 00:00:00 | ||
| Checkpoint Neural Network Model Improvements | 00:00:00 | ||
| Checkpoint Neural Network Best Model | 00:00:00 | ||
| Loading The Saved Checkpoint | 00:00:00 | ||
| Section 12 - Plotting Model Behavior History | |||
| Plotting Model Behavior History - Introduction | 00:00:00 | ||
| Plotting Model Behavior History - Coding | 00:00:00 | ||
| Section 13 - Dropout Regularization | |||
| Dropout Regularization - Visible Layer - Part 1 | 00:00:00 | ||
| Dropout Regularization - Visible Layer - Part 2 | 00:00:00 | ||
| Dropout Regularization - Hidden Layer | 00:00:00 | ||
| Section 14 - Learning Rate Schedule | |||
| Learning Rate Schedule Using Ionosphere Dataset | 00:00:00 | ||
| Time Based Learning Rate Schedule - Part 1 | 00:00:00 | ||
| Time Based Learning Rate Schedule - Part 2 | 00:00:00 | ||
| Drop Based Learning Rate Schedule - Part 1 | 00:00:00 | ||
| Drop Based Learning Rate Schedule - Part 2 | 00:00:00 | ||
| Section 15 - Convolutional Neural Networks | |||
| Convolutional Neural Networks - Part 1 | 00:00:00 | ||
| Convolutional Neural Networks - Part 2 | 00:00:00 | ||
| Section 16 - MNIST Handwritten Digit Recognition Dataset | |||
| Introduction To MNIST Handwritten Digit Recognition Dataset | 00:00:00 | ||
| Downloading And Testing MNIST Handwritten Digit Recognition Dataset | 00:00:00 | ||
| MNIST Multi-Layer Perceptron Model Development - Part 1 | 00:00:00 | ||
| MNIST Multi-Layer Perceptron Model Development - Part 2 | 00:00:00 | ||
| Convolutional Neural Network Model Using MNIST - Part 1 | 00:00:00 | ||
| Convolutional Neural Network Model Using MNIST - Part 2 | 00:00:00 | ||
| Large CNN Using MNIST | 00:00:00 | ||
| Load And Predict Using The MNIST CNN Model | 00:00:00 | ||
| Section 17 - Image Augmentation | |||
| Introduction To Image Augmentation Using Keras | 00:00:00 | ||
| Augmentation Using Sample Wise Standardization | 00:00:00 | ||
| Augmentation Using Feature Wise Standardization And ZCA Whitening | 00:00:00 | ||
| Augmentation Using Rotation And Flipping | 00:00:00 | ||
| Saving Augmentation | 00:00:00 | ||
| Section 18 - CIFAR-10 Dataset | |||
| CIFAR-10 Object Recognition Dataset - Understanding And Loading | 00:00:00 | ||
| Simple CNN Using CIFAR-10 Dataset - Part 1 | 00:00:00 | ||
| Simple CNN Using CIFAR-10 Dataset - Part 2 | 00:00:00 | ||
| Simple CNN Using CIFAR-10 Dataset - Part 3 | 00:00:00 | ||
| Train And Save CIFAR-10 Model | 00:00:00 | ||
| Load And Predict Using CIFAR-10 CNN Model | 00:00:00 | ||
About This Course
Who this course is for:
- Programmers
- Data Scientists
- Data Engineers
What you’ll learn:Â
- Theory sessions to see an overview about the Deep Learning and Neural Networks
- The basics of Python
- Theano, Tensorflow and Keras, the best and most popular Deep Learning libraries
- Multi-Layer perceptrons, the basic element of the Deep Learning Neural Network
- creating real-world deep learning models using several datasets
- Convolutional Neural Networks
- Object recognition in image data
- Object recognition in photographs
Requirements:Â
- No prior knowledge is required to take this course
The world has been revolving much around the terms “Machine Learning” and “Deep Learning” recently. With or without our knowledge, every day we are using these technologies ranging from Google suggestions, translations, ads, movie recommendations, friend suggestions, sales, customer experience, so on and so forth. There are tons of other applications, too. No wonder why “Deep Learning” and “Machine Learning along with Data Science” are the most sought after talent in the technology world nowadays.
But the problem is that, when you think about learning these technologies, a misconception that lots of maths, statistics, complex algorithms and formulas need to be studied. It’s just like someone tries to make you believe that you should learn the working of an internal combustion engine before you learn how to drive a car. The fact is that, to drive a car, we just only need to know how to use the user-friendly control pedals extending from the engine like clutch, brake, accelerator, steering wheel, etc. And with a bit of experience, you can easily drive a car.
The basic know-how about the internal working of the engine is of course an added advantage while driving a car, but it’s not mandatory. Just like that, in our Deep Learning course, we have a perfect balance between learning the basic concepts along the implementation of the built-in Deep Learning classes and functions from the Keras Library using the Python Programming Language. These classes, functions, and APIs are just like the control pedals from the car engine, which we can use easily to build an efficient deep learning model.
Overall, this is a basic to advanced crash course in Deep Learning Neural Networks and Convolutional Neural Networks using Keras and Python, which I am sure once you completed will sky rocket your current career prospects as this is the most wanted skill nowadays and of course this is the technology of the future.Â
There is a day in the near future itself, when the deep learning models will outperform human intelligence. So, be ready and let’s dive into the world of thinking machines.
Our Promise to You
By the end of this course, you will have learned how to implement Deep Learning and use Neural Networks.
10 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.
Get started today and learn more about Deep Learning And Neural Networks.
Course Curriculum
| Section 1 - Introduction | |||
| Course Introduction And Table Of Contents | 00:00:00 | ||
| Course Resources | 00:00:00 | ||
| Deep Learning Overview - Theory Session - Part 1 | 00:00:00 | ||
| Deep Learning Overview - Theory Session - Part 2 | 00:00:00 | ||
| Choosing Between ML Or DL For The Next Ai Project - Quick Theory Session | 00:00:00 | ||
| Preparing Your Computer - Part 1 | 00:00:00 | ||
| Important: TensorFlow Incompatibility | 00:00:00 | ||
| Preparing Your Computer - Part 2 | 00:00:00 | ||
| Section 2 - Python Basics | |||
| Python Basics - Assignment | 00:00:00 | ||
| Python Basics - Flow Control | 00:00:00 | ||
| Python Basics - Functions | 00:00:00 | ||
| Python Basics - Data Structures | 00:00:00 | ||
| Section 3 - Libraries | |||
| Theano Library Installation And Sample Program To Test | 00:00:00 | ||
| TensorFlow Library Installation And Sample Program To Test | 00:00:00 | ||
| Keras Installation And Switching Theano And TensorFlow Backends | 00:00:00 | ||
| Section 4 - Multi-Layer Perceptron Concepts | |||
| Explaining Multi-Layer Perceptron Concepts | 00:00:00 | ||
| Section 5 - Neural Networks Steps And Terminology | |||
| Explaining Neural Networks Steps And Terminology | 00:00:00 | ||
| Section 6 - Pima Indian Diabetes Dataset | |||
| First Neural Network With Keras - Understanding Pima Indian Diabetes Dataset | 00:00:00 | ||
| Explaining Training And Evaluation Concepts | 00:00:00 | ||
| Pima Indian Model - Steps Explained - Part 1 | 00:00:00 | ||
| Pima Indian Model - Steps Explained - Part 2 | 00:00:00 | ||
| Coding The Pima Indian Model - Part 1 | 00:00:00 | ||
| Coding The Pima Indian Model - Part 2 | 00:00:00 | ||
| Performance Evaluation - Automatic Verification | 00:00:00 | ||
| Performance Evaluation - Manual Verification | 00:00:00 | ||
| Performance Evaluation - K-Fold Validation - Keras | 00:00:00 | ||
| Performance Evaluation - Hyper Parameters | 00:00:00 | ||
| Section 7 - Iris Flower Multi-Class Dataset | |||
| Understanding Iris Flower Multi-Class Dataset | 00:00:00 | ||
| Developing The Iris Flower Multi-Class Model - Part 1 | 00:00:00 | ||
| Developing The Iris Flower Multi-Class Model - Part 2 | 00:00:00 | ||
| Developing The Iris Flower Multi-Class Model - Part 3 | 00:00:00 | ||
| Section 8 - Sonar Returns Dataset | |||
| Understanding The Sonar Returns Dataset | 00:00:00 | ||
| Developing The Sonar Returns Model | 00:00:00 | ||
| Sonar Performance Improvement - Data Preparation - Standardization | 00:00:00 | ||
| Sonar Performance Improvement - Layer Tuning For Smaller Network | 00:00:00 | ||
| Sonar Performance Improvement - Layer Tuning For Larger Network | 00:00:00 | ||
| Section 9 - Boston Housing Regression Dataset | |||
| Understanding The Boston Housing Regression Dataset | 00:00:00 | ||
| Developing The Boston Housing Baseline Model | 00:00:00 | ||
| Boston Performance Improvement By Standardization | 00:00:00 | ||
| Boston Performance Improvement By Deeper Network Tuning | 00:00:00 | ||
| Boston Performance Improvement By Wider Network Tuning | 00:00:00 | ||
| Section 10 - Save, Load, And Predict | |||
| Save And Load The Trained Model As JSON File (Pima Indian Dataset) - Part 1 | 00:00:00 | ||
| Save And Load The Trained Model As JSON File (Pima Indian Dataset) - Part 2 | 00:00:00 | ||
| Save And Load Model As YAML File - Pima Indian Dataset | 00:00:00 | ||
| Load And Predict Using The Pima Indian Diabetes Model | 00:00:00 | ||
| Load And Predict Using The Iris Flower Multi-Class Model | 00:00:00 | ||
| Load And Predict Using The Sonar Returns Model | 00:00:00 | ||
| Load And Predict Using The Boston Housing Regression Model | 00:00:00 | ||
| Section 11 - Checkpoint | |||
| Introduction To Checkpointing | 00:00:00 | ||
| Checkpoint Neural Network Model Improvements | 00:00:00 | ||
| Checkpoint Neural Network Best Model | 00:00:00 | ||
| Loading The Saved Checkpoint | 00:00:00 | ||
| Section 12 - Plotting Model Behavior History | |||
| Plotting Model Behavior History - Introduction | 00:00:00 | ||
| Plotting Model Behavior History - Coding | 00:00:00 | ||
| Section 13 - Dropout Regularization | |||
| Dropout Regularization - Visible Layer - Part 1 | 00:00:00 | ||
| Dropout Regularization - Visible Layer - Part 2 | 00:00:00 | ||
| Dropout Regularization - Hidden Layer | 00:00:00 | ||
| Section 14 - Learning Rate Schedule | |||
| Learning Rate Schedule Using Ionosphere Dataset | 00:00:00 | ||
| Time Based Learning Rate Schedule - Part 1 | 00:00:00 | ||
| Time Based Learning Rate Schedule - Part 2 | 00:00:00 | ||
| Drop Based Learning Rate Schedule - Part 1 | 00:00:00 | ||
| Drop Based Learning Rate Schedule - Part 2 | 00:00:00 | ||
| Section 15 - Convolutional Neural Networks | |||
| Convolutional Neural Networks - Part 1 | 00:00:00 | ||
| Convolutional Neural Networks - Part 2 | 00:00:00 | ||
| Section 16 - MNIST Handwritten Digit Recognition Dataset | |||
| Introduction To MNIST Handwritten Digit Recognition Dataset | 00:00:00 | ||
| Downloading And Testing MNIST Handwritten Digit Recognition Dataset | 00:00:00 | ||
| MNIST Multi-Layer Perceptron Model Development - Part 1 | 00:00:00 | ||
| MNIST Multi-Layer Perceptron Model Development - Part 2 | 00:00:00 | ||
| Convolutional Neural Network Model Using MNIST - Part 1 | 00:00:00 | ||
| Convolutional Neural Network Model Using MNIST - Part 2 | 00:00:00 | ||
| Large CNN Using MNIST | 00:00:00 | ||
| Load And Predict Using The MNIST CNN Model | 00:00:00 | ||
| Section 17 - Image Augmentation | |||
| Introduction To Image Augmentation Using Keras | 00:00:00 | ||
| Augmentation Using Sample Wise Standardization | 00:00:00 | ||
| Augmentation Using Feature Wise Standardization And ZCA Whitening | 00:00:00 | ||
| Augmentation Using Rotation And Flipping | 00:00:00 | ||
| Saving Augmentation | 00:00:00 | ||
| Section 18 - CIFAR-10 Dataset | |||
| CIFAR-10 Object Recognition Dataset - Understanding And Loading | 00:00:00 | ||
| Simple CNN Using CIFAR-10 Dataset - Part 1 | 00:00:00 | ||
| Simple CNN Using CIFAR-10 Dataset - Part 2 | 00:00:00 | ||
| Simple CNN Using CIFAR-10 Dataset - Part 3 | 00:00:00 | ||
| Train And Save CIFAR-10 Model | 00:00:00 | ||
| Load And Predict Using CIFAR-10 CNN Model | 00:00:00 | ||
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