About 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.

This course will cover:

  • 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

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. 

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.

30 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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