This plan includes
- Limited free courses access
- Play & Pause Course Videos
- Video Recorded Lectures
- Learn on Mobile/PC/Tablet
- Quizzes and Real Projects
- Lifetime Course Certificate
- Email & Chat Support
What you'll learn?
- Deep Learning and Convolutional Neural Networks using Python for Beginners.
Course Overview
Pre-requisites
- A medium configuration computer and the willingness to indulge in the world of Deep Learning.
Target Audience
- Beginners who are interested in Deep Learning using Python.
Curriculum 45 Lectures 06:17:24
-
Section 1 : Course Intro and Table of Contents.
- Lecture 2 :
- Course Intro and Table of Contents - Short.
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Section 2 : Deep Learning Overview.
- Lecture 1 :
- Deep Learning Overview - Part 1
- Lecture 2 :
- Deep Learning Overview - Part 2
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Section 3 : Chosing ML or DL for your project.
- Lecture 1 :
- Chosing ML or DL for your project.
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Section 4 : Chosing ML or DL for your project.
- Lecture 1 :
- Chosing ML or DL for your project.-Part 1
- Lecture 2 :
- Chosing ML or DL for your project.-Part 2
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Section 5 : Python Basics .
- Lecture 1 :
- Python Basics - Assignment.
- Lecture 2 :
- Python Basics - Flow Control.
- Lecture 3 :
- Python Basics - Data Structures.
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Section 6 : Installing Theano Library and Sample Program to Test - New.
- Lecture 1 :
- Installing Theano Library and Sample Program to Test - New.
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Section 7 : TensorFlow library Installation and Sample Program to Test.
- Lecture 1 :
- TensorFlow library Installation and Sample Program to Test.
- Lecture 2 :
- Keras Installation and Switching Theano and TensorFlow Backends
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Section 8 : Multi-Layer Perceptron Concepts.
- Lecture 1 :
- Multi-Layer Perceptron Concepts.
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Section 9 : Pima Indian Model.
- Lecture 1 :
- Pima Indian Model - Steps Explained - Part 1
- Lecture 2 :
- Pima Indian Model - Steps Explained - Part 2
- Lecture 3 :
- Pima Indian Model Coding - Part 1
- Lecture 4 :
- Pima Indian Model - Perfomance Evaluation - Manual Verification.
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Section 10 : Developing the Iris Flower Model.
- Lecture 1 :
- Developing the Iris Flower Model - Part 1
- Lecture 2 :
- Developing the Iris Flower Model - Part 2
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Section 11 : Understanding the Sonar Returns Dataset.
- Lecture 1 :
- Understanding the Sonar Returns Dataset.
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Section 12 : Sonar Model Perfomance Improvement - Layer Tuning For Larger Network.
- Lecture 1 :
- Sonar Model Perfomance Improvement - Layer Tuning For Larger Network.
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Section 13 : Understanding the Boston Housing Dataset.
- Lecture 1 :
- Understanding the Boston Housing Dataset.
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Section 14 : Boston Performance Improvement.
- Lecture 1 :
- Boston Performance Improvement by Standardization.
- Lecture 2 :
- Boston Performance Improvement by Deeper Network Tuning.
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Section 15 : Load and Predict using the Pima Indian Model.
- Lecture 1 :
- Load and Predict using the Pima Indian Model.
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Section 16 : Save Load and Predict using.
- Lecture 1 :
- Save Load and Predict using Iris Flower Dataset.
- Lecture 2 :
- Save Load and Predict using Boston Dataset.
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Section 17 : Checkpointing Introduction.
- Lecture 1 :
- Checkpointing Introduction.
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Section 18 : Checkpoint Neural Network Model Improvements.
- Lecture 1 :
- Checkpoint Neural Network Model Improvements.
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Section 19 : Loading Saved Checkpoints.
- Lecture 1 :
- Loading Saved Checkpoints.
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Section 20 : Learning Rate Schedule Coding.
- Lecture 1 :
- Learning Rate Schedule Coding.
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Section 21 : Drop Based Learning Rate Schedule - Part 2
- Lecture 1 :
- Drop Based Learning Rate Schedule - Part 2
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Section 22 : Convolutional Neural Networks - Introduction - Part 2
- Lecture 1 :
- Convolutional Neural Networks - Introduction - Part 2
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Section 23 : Downloading the MNIST Handwritten Digit Dataset.
- Lecture 1 :
- Downloading the MNIST Handwritten Digit Dataset.
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Section 24 : Multi Layer Perceptron Model using MNIST.
- Lecture 1 :
- Multi Layer Perceptron Model using MNIST.
- Lecture 2 :
- Multi Layer Perceptron Model using MNIST - Part 2
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Section 25 : Convolutional Neural Network Model using MNIST.
- Lecture 1 :
- Convolutional Neural Network Model using MNIST.
- Lecture 2 :
- Convolutional Neural Network Model using MNIST- Part 2
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Section 26 : Large CNN using MNIST.
- Lecture 1 :
- Large CNN using MNIST.
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Section 27 : Introduction to Image Augmentation using Keras.
- Lecture 1 :
- Introduction to Image Augmentation using Keras.
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Section 28 : Saving Augmentation for MNIST.
- Lecture 1 :
- Saving Augmentation for MNIST.
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Section 29 : Simple CNN using CIFAR-10 Dataset.
- Lecture 1 :
- Simple CNN using CIFAR-10 Dataset - Part 2
- Lecture 2 :
- Simple CNN using CIFAR-10 Dataset - Coding.
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Section 30 : Load and Predict using CIFAR-10 CNN Model.
- Lecture 1 :
- Load and Predict using CIFAR-10 CNN Model.
Our learners work at
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