Deep Learning Practice Exercise

The following Deep Learning Practice Exercise comprises important deep learning concepts. As a matter of fact, implementing a deep learning algorithm becomes easy with the help of certain python packages. Furthermore. TensorFlow and Keras are two such libraries that you can use in deep learning.

While TensorFlow is an open-source library that offers both high-level and low-level APIs, Keras has only low-level APIs. Apart from TensorFlow, and Keras there are other libraries for deep learning such as PyTorch, Caffe, spark-deep-learning, and so on. The following Deep Learning Practice Exercise involves the use of TensorFlow and Keras library.

Deep Learning Practice Exercise for Implementing Deep Learning Algorithms

  1. To begin with, write the code to read a dataset using the appropriate python library and display it.
  2. Furthermore, write code to read an image dataset.
  3. In order to understand the working of TensorFlow, write a program to create a computational graph using TensorFlow.
  4. Since, Keras library provides functionality for implementing neural networks, use it to write a program for creating a perceptron.
  5. Also, write a program to implement multi-layer perceptron using TensorFlow.
  6. Furthermore, write code to apply multi-layer perceptron (MLP) on the Iris dataset.
  7. Basically, MNIST is a large dataset that you can use in various image processing applications for training purposes. Write a program to perform digit classification using the MNIST dataset in Keras.
  8. In fact, an ANN can be a feed-forward network or a backpropagation network. As soon as the output is generated from the network and an error occurs, the backpropagation network sends back the error information. Therefore, the weights can be adjusted. So, write the code to implement a backpropagation network.
  9. Also, write a program to demonstrate different activation functions.
  10. For the purpose of understanding loss functions, write code in TensorFlow to demonstrate Loss functions.

Implementing CNN

  1. In fact, the Keras API provides a number of methods for implementing a CNN. So, write code to implement a Convolution Neural Network in Keras.
  2. Once, a CNN is trained, we can use it to make predictions. Therefore, write a program to perform predictions using Keras.
  3. Basically, CIFAR-10 is an image dataset that also you can use in image processing applications. In fact, we can also implement an image classifier with this dataset using CNN. Therefore, write code to build an Image Classifier with CIFAR-10 Data.

Implementing RNN and LSTM

  1. Basically, a Recurrent Neural Network (RNN) has applications in Natural Language Processing (NLP). Also, it is one of the important concepts in machine learning. So, write code to implement a Recurrent Neural Network
  2. Since LSTM is a type of RNN, understanding its implementation s important. So, write a program to implement LSTM.
  3. Furthermore, write a program to perform time series analysis using LSTM
  4. Since sometimes we need to make predictions using insufficient data, so we need to use transfer learning. Therefore, write a program to demonstrate Transfer Learning
  5. Another important application of deep learning is object detection. So, write code to perform object detection using Deep Learning

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