Data Science and Machine Learning Series: Managing Large Datasets using Convolutional Neural Networks (CNNs)

Data Science and Machine Learning Series: Managing Large Datasets using Convolutional Neural Networks (CNNs)
Data Science and Machine Learning Series: Managing Large Datasets using Convolutional Neural Networks (CNNs)
English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 2h 16m | 372 MB

Follow along with machine learning expert Advait Jayant through a combination of lecture and hands-on to master working with large datasets using Convolutional Neural Networks (CNNs).

The following nine topics will be covered in this Data Science and Machine Learning course:

  • Introducing Google Colaboratory (Colab). Become proficient with Google Colaboratory (Colab) in this first topic in the Data Science and Machine Learning Series. Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud. Colaboratory provides free GPUs and TPUs. Follow along with Advait and practice working with Colab using a complex dataset containing four types of images.
  • Using Image Data Augmentation with Large Datasets. Use image data augmentation with large datasets in this second topic in the Data Science and Machine Learning Series. Follow along with Advait and see how to minimize model overfitting using augmentation.
  • Creating a Validation Set from the Training Set. Create a validation set from the training set in this third topic in the Data Science and Machine Learning Series.
  • Training the Model for the Validation Set. Train the model for the validation set in this fourth topic in the Data Science and Machine Learning Series. Follow along with Advait and build a validation generator and create a visualization of the validation set during this session.
  • Building an Image Pipeline to Perform Data Augmentation. Build an image pipeline to perform data augmentation in this fifth topic in the Data Science and Machine Learning Series. Follow along with Advait and perform data augmentation on the fly using the Tiny Imagenet dataset containing 200 classes.
  • Handling Validation Data in the Image Pipeline. Handle validation data in the image pipeline in this sixth topic in the Data Science and Machine Learning Series. Follow along with Advait and practice working with data when there is no structure present.
  • AlexNet. Become proficient with the AlexNet Convolutional Neural Network (CNN) architecture in this seventh topic in the Data Science and Machine Learning Series. The most popular CNN architectures are introduced including AlexNet, ZF-Net, VGG, Resnet, Inception, Inception-Resnets, and Mobilenets. Follow along with Advait and implement the AlexNet CNN.
  • ZFNet and VGG. Become proficient with the ZFNet and VGG Convolutional Neural Network (CNN) architectures in this eighth topic in the Data Science and Machine Learning Series. Follow along with Advait and witness the improvements these two CNN architecture made over AlexNet.
  • GoogleNet and the Inception Module. Master GoogleNet and the Inception Module in this ninth topic in the Data Science and Machine Learning Series. Follow along with Advait and practice the practical aspects of image data augmentation.
Table of Contents

1 Introducing Google Colaboratory (Colab)
2 Using Image Data Augmentation with Large Datasets
3 Creating a Validation Set from the Training Set
4 Training the Model for the Validation Set
5 Building an Image Pipeline to Perform Data Augmentation
6 Handling Validation Data in the Image Pipeline
7 AlexNet
8 ZFNet and VGG
9 GoogleNet and the Inception Module