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Applied Tags : Matplotlib

  • Iris Flowers Classification using Deep Learning & Keras

    Topic
    8 Concepts | 1 Question | 8 Assessments | 163 Learners

    Welcome to this project on Classifying Flowers in Iris dataset with Deep Neural Network using Keras. In this project, you will use Python and Keras to build a Deep Neural Network, and apply it to predict the classes of Flowers in the Iris dataset.

    Keras is one of the most extensively used APIs in the world of Deep Learning. It provides an amazing developer-friendly deep learning framework to build deep learning models with wide-ranging features to support high scalability, because of which it is not only widely used in academics but also in organizations to build state-of-the-art research models. In …

  • Getting Started with Matplotlib

    Topic
    8 Concepts | 12 Assessments | 127 Learners

    Welcome to this project on Getting Started with Matplotlib. In this project, you will understand how to use Matplotlib, one of the most famous visualizing libraries in Python.

    Data visualization is one of the most prominent ways of analyzing the data. It presents visually appealing ways to detect the patterns, noise, outliers, and many other insights, which would assist the data scientists to understand, transform, and refine the data to build better comprehensive models. This project would help you build data visualization skills on top of your existing Python programming skills. You will understand how to use Matplotlib to depict …

  • Image Stitching using OpenCV and Python (Creating Panorama Project)

    Topic
    12 Concepts | 11 Assessments | 97 Learners

    Welcome to this project on Image Stitching using OpenCV. In this project, we will use OpenCV with Python and Matplotlib in order to merge two images and form a panorama.

    As you know, the Google photos app has stunning automatic features like video making, panorama stitching, collage making, and many more. In this exercise, we will understand how to make a panorama stitching using OpenCV with Python.

    Skills you will develop:

    • OpenCV
    • Python
    • Matplotlib
    Instructor: Vagdevi K
  • Welcome to the project on Building a Neural Network for Image Classification with TensorFlow. In this project, we would learn how to develop a neural network classifier from very scratch, using TensorFlow 2.

    We would build and train a dense neural network on the Fashion MNIST dataset and evaluate its performance with some test samples. This project aims to impart the knowledge of the basic steps involved in building a neural network, working with TensorFlow 2, training a neural network, and make the learner comfortable with the cutting-edge technology - TensorFlow 2.

    Skills you will develop:

    1. TensorFlow 2
    2. Matplotlib …
    Instructor: Vagdevi K
  • Welcome to this project on the Neural Style Transfer. In this project, you will use TensorFlow 2 to generate an image that is an artistic blend of a content image and style image.

    Neural Artistic Style Transfer finds a wide range of applications to fancily modify images. This field has so much influenced the technical world that many apps, such as Prisma, have received great craze amongst the users. In recent days, decent work has also been done in this area, which served as a holy grail to our project. The heart of this capability is the convolutional neural network …

    Instructor: Vagdevi K
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    Project - Mask R-CNN with OpenCV for Object Detection

    Topic
    2 Concepts | 6 Assessments | 70 Learners

    Welcome to the project on Mask R-CNN with OpenCV for Object Detection. In this project, we will learn how to read a pre-trained TensorFlow model for object detection using OpenCV.

    The real-world scenarios have a lot of applications based on object detection. For example, object detection models are used in self-driving cars to recognize where the pedestrians are, where the are vehicles located, where the signals are, etc in the given frame of view. So, it is very important to develop an understanding of how to use a pre-trained object detection model so that we could later customize it based …

    Instructor: Vagdevi K
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    Project - Autoencoders for MNIST Fashion

    Topic
    6 Assessments | 32 Learners

    Welcome to this project on Autoencoders for MNIST Fashion. In this project, we will understand how to implement Autoencoders using TensorFlow 2.

    We will be understanding how to practically implement the autoencoder, stacking an encoder and decoder using TensorFlow 2. We will also depict the reconstructed output images by the autoencoder model.

    Skills you will develop:

    1. TensorFlow 2

    2. scikit-learn

    3. Matplotlib

    4. Numpy

    Instructor: Vagdevi K