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Applied Tags : Keras

  • Welcome to this project on NYSE Closing Price Prediction. In this project, you will use Pandas, Keras, and Python in order to build a predictive model and apply it to predict the closing prices.

    Time-series modeling has a huge demand in today's numbers-filled world. It has a wide variety of applications in sales s forecasting, prediction of meteorological elements like rainfall, economic forecasting in the financial worlds, and many more.

    In this exercise, we shall understand how to predict stock market closing prices for a firm using GRU, a state-of-art deep learning algorithm for sequential data. We shall focus …

    Instructor: Vagdevi K
  • Iris Flowers Classification using Deep Learning & Keras

    Topic
    8 Concepts | 1 Question | 8 Assessments | 182 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 …

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    Project - Introduction to Transfer Learning (Cat vs Non-cats Project)

    Topic
    14 Concepts | 2 Questions | 10 Assessments | 105 Learners

    Welcome to this project on Cat vs Non-cat Classifier using Transfer Learning. In this project, you will use Python and Keras to apply the Transfer Learning technique in order to build an image classifier, and apply it to predict the class of an input image - whether it is a cat or a non-cat.

    Deep Learning is computationally intensive, often demanding powerful computational resources to yield reasonable accuracies in the real world. The idea of Transfer Learning has become a boon for the computer vision and deep learning community as it has reduced the hunger of Deep Learning algorithms for powerful …

    Instructor: Vagdevi K
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    Project - Image Classification with Pre-trained Keras models

    Topic
    1 Concept | 4 Assessments | 100 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
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    Project - Training from Scratch vs Transfer Learning

    Topic
    1 Concept | 7 Assessments | 84 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
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    Project - Working with Custom Loss Function

    Topic
    6 Assessments | 61 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