#NoPayJan Offer - Access all CloudxLab Courses for free between 1st to 31st Jan

  Enroll Now >>
  • 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
  • P

    Project - Mask R-CNN with OpenCV for Object Detection

    Topic
    2 Concepts | 6 Assessments | 62 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
  • 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
  • P

    Project - Image Classification with Pre-trained Keras models

    Topic
    1 Concept | 4 Assessments | 60 Learners

    Welcome to this project on Image Classification with Pre-trained Keras models. This project aims to impart the knowledge of how to access the pre-trained models(here we get pre-trained ResNet model) from Keras of TensorFlow 2, and appreciate its powerful classification capacity by making the model predict the class of an input image.

    Understanding the pre-trained models is very important because this forms the basis of transfer learning. one of the most appreciated techniques to perform the classification of a different task thus reducing the training time, the number of iterations, and resource consumption. Learning about the pre-trained models and …

    Instructor: Vagdevi K
  • P

    Project - Training from Scratch vs Transfer Learning

    Topic
    1 Concept | 7 Assessments | 45 Learners

    Welcome to the project on Training from Scratch vs Transfer Learning. In this exercise, we will understand how to train a neural network from scratch to classify data using TensorFlow 2. We would also learn how to use the weights of an already trained model to achieve classification to another set of data.

    We will train a neural network (say model A) on data related to 6 of the classes, and we will train another neural network (say model B) on the remaining 2 classes. Then, we would use the pre-trained weights of model A and tune the last layer …

    Instructor: Vagdevi K
  • H

    How to Host an Image Classification App on Heroku

    Topic
    2 Concepts | 6 Assessments | 37 Learners

    Welcome to the project on Hosting an Image Classification App on Heroku. In this project, we will get a basic understanding of how to deploy a web app on Heroku, a Platform as a Service.

    Heroku is a cloud platform for the deployment and management purposes of web applications. It could be considered as one of the best solutions for hosting web-apps very quickly, thus allowing the developer to concentrate more on development.

    Instructor: Vagdevi K
  • P

    Predicting Noisy Images using KNN Classifier

    Topic
    1 Concept | 9 Assessments | 36 Learners

    In this project, we will learn how to predict images from their noisy version. We will use the MNIST dataset for this project. First, we will load the dataset, explore it, and they we will learn how to introduce noise to an image. Next we will train a KNN Classifier to predict the original image from it's noisy version.

    Skills you will develop:

    1. scikit-learn
    2. Python
    3. KNN Classification
    4. Machine Learning
    5. Pandas
  • P

    Project - Working with Custom Loss Function

    Topic
    6 Assessments | 32 Learners

    Welcome to the project on Working with Custom Loss Function. This project aims to provide an understanding of how we could use the custom defined loss functions along with TensorFlow 2.

    Though TensorFlow 2 already provides us with a variety of loss functions, knowing how to use a user-defined loss function would be crucial for a machine learning aspirant because often times in real-world industries, it is expected to experiment with various custom defined functions. This exercise is designed to achieve that goal.

    Skills you will develop:

    1. TensorFlow 2
    2. Defining Custom Loss Function
    3. Python Programming
    4. scikit-learn
    Instructor: Vagdevi K
  • L

    Loading and Preprocessing Data with TensorFlow

    Topic
    9 Questions | 24 Learners

    Learn how to load and preprocess data in Tensorflow.

  • P

    Project - Autoencoders for MNIST Fashion

    Topic
    6 Assessments | 23 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