The Complete Convolutional Neural Network with Python 2022

Deep Learning Convolutional Neural Network (CNN) – Keras & TensorFlow 2

Interested in the field of Machine Learning, Deep Learning, and Artificial Intelligence? Then this course is for you!

What you’ll learn

  • DeepDream.
  • Data augmentation.
  • VGG.
  • Inception.
  • Data augmentation.
  • Con2D.
  • MaxPooling2D.
  • EarlyStopping.
  • Matplotlib.
  • Confusion matrix.
  • Pandas.
  • Numpy.
  • MinMaxScaler.
  • Google Colab.
  • Deep Learning..
  • Training Neural Network..
  • Splitting Data into Training Set and Test Set..
  • Testing Accuracy..
  • Confusion Matrix..
  • Make a Prediction..
  • Model compilation..

Course Content

  • Introduction –> 3 lectures • 5min.
  • Convolutional Neural Network (CNN) Fundamental –> 7 lectures • 1hr 19min.
  • CIFAR-10 Project –> 5 lectures • 55min.
  • Clothing Image Project –> 8 lectures • 1hr 54min.
  • Advanced Implementation of CNN –> 7 lectures • 1hr 43min.
  • Thank you –> 1 lecture • 1min.

The Complete Convolutional Neural Network with Python 2022

Requirements

Interested in the field of Machine Learning, Deep Learning, and Artificial Intelligence? Then this course is for you!

This course has been designed by a software engineer. I hope with the experience and knowledge I did gain throughout the years, I can share my knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.

I will walk you step-by-step into Deep Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time, we dive deep into Recurrent Neural Network. Throughout the brand new version of the course, we cover tons of tools and technologies including:

  • Deep Learning.
  • Google Colab
  • Keras.
  • Matplotlib.
  • Splitting Data into Training Set and Test Set.
  • Training Neural Network.
  • Model building.
  • Analyzing Results.
  • Model compilation.
  • Make a Prediction.
  • Testing Accuracy.
  • Confusion Matrix.
  • Autoencoder.
  • Numpy.
  • Pandas.
  • Tensorflow.
  • Sentiment Analysis.
  • Matplotlib.
  • DeepDream
  • Inception
  • Data Augmentation
  • Conv2D
  • MaxPooling2D
  • Early Stopping
  • VGG
  • MinMaxScaler.

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are several projects for you to practice and build up your knowledge. These projects are listed below:

  • CIFAR-10.
  • Image Combination.
  • Movie Review sentiment.
  • Clothing Image.

 

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