Deep Learning

3. Build the Image classification model by dividing the model into following 4 stages: a. Loading and preprocessing the image data b. Defining the model’s architecture c. Training the model d. Estimating the model’s performance  Click here for program details Click Here to download the Writeup

 1. Study of Deep learning Packages: Tensorflow, Keras, Theano and PyTorch. Document the distinct features and functionality of the packages. Download The Write-up Here

  Download The Write-Up Here. # # Title of Assignment-2: # Implementing Feedforward neural networks with Keras and TensorFlow # a. Import the necessary packages # b. Load the training and testing data (MNIST) # c. Define the network architecture using Keras # d. Train the model using SGD # e. Evaluate the network # f. Plot the training loss and accuracy # # # Importing libraries # In[1]: #importing necessary libraries import tensorflow as tf from tensorflow import keras # In[2]: import pandas as pd import numpy as np import matplotlib . pyplot as plt import random get_ipython (). run_line_magic ( 'matplotlib' , 'inline' ) # # Loading and preparing the data # MNIST stands for “Modified National Institute of Standards and Technology”. # It is a dataset of 70,000 handwritten images. Each image is of 28x28 pixels # i.e. about 784 features. Each feature represents only one pixel’s intensity i.e. from 0(white) to 255(blac...