Project 2

In machine learning, When we want to train our ML model we split our entire dataset into training_set and test_set using train_test_split() class present in sklearn. Then we train our model on training_set and test our model on test_set. The problems that we are going to face in this method are: Whenever we change the…

  Back Propagation (Gradient computation) The backpropagation learning algorithm can be divided into two phases: propagation and weight update.   Phase 1: PropagationEach propagation involves the following steps: Forward propagation of a training pattern's input through the neural network in order to generate…

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import plotly.express as px import nltk import warnings warnings.simplefilter(action='ignore') df = pd.read_csv('../input/sms-spam-collection-dataset/spam.csv',usecols=['v1','v2'],encoding='cp1252') df.head(2) df.shape df.info()…

import numpy as np import pandas as pd import pandas as pd df = pd.read_csv("/kaggle/input/creditcardfraud/creditcard.csv") df.head() features = df.drop("Class",axis=1) labels = df.loc[:,['Class']] labels.head() from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(features,…

The vanishing gradient problem was discovered by Sepp Hochreiter, a German computer scientist who has had an influential role in the development of recurrent neural networks in deep learning. the vanishing gradient problem is related to deep learning gradient descent algorithms. Recall that a gradient descent algorithm…

import os from collections import defaultdict import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns from PIL import Image from tqdm import tqdm_notebook as tqdm root = '../input/vehicle/train/train/' data = [] for category in sorted(os.listdir(root)): for file in sorted(os.listdir(os.path.join(root,…

Padding: Problem with Simple Convolution Layers For a gray scale (n x n) image and (f x f) filter/kernel, the dimensions of the image resulting from a convolution operation is (n – f + 1) x (n – f + 1). For example, for an (8 x 8) image and (3 x 3) filter, the output resulting after convolution operation…

import numpy as np import pandas as pd import tensorflow as tf import matplotlib.pyplot as plt import missingno as msno import seaborn as sns df=pd.read_csv('machine_failure_data.csv') msno.matrix(df) values_list = list() cols_list = list() for col in df.columns: pct_missing = np.mean(df[col].isnull())*100 cols_list.append(col)…

2. Gradient Descent The work of the gradient descent algorithm is to update the weights in such a way that the loss function is minimum. The updation rule is: Now from the image above, we can calculate the values of Z1, Z2, etc, and all the derivates like in the images below.     Activation functions are used…

2. b) Mini-Batch Gradient Descent: Algorithm-   Let theta = model parameters and max_iters = number of epochs. for itr = 1, 2, 3, …, max_iters:      for mini_batch (X_mini, y_mini): Forward Pass on the batch X_mini: Make predictions on the mini-batch Compute error in predictions (J(theta)) with…

Boosting is an ensemble modeling technique that attempts to build a strong classifier from the number of weak classifiers. It is done by building a model by using weak models in series. Firstly, a model is built from the training data. Then the second model is built which tries to correct the errors present in the…

Let us consider a dataset where we have a value of response y for every feature x:  For generality, we define:x as feature vector, i.e x = [x_1, x_2, …., x_n],y as response vector, i.e y = [y_1, y_2, …., y_n]for n observations (in above example, n=10).A scatter plot of the above dataset…

In linear regression, the model targets to get the best-fit regression line to predict the value of y based on the given input value (x). While training the model, the model calculates the cost function which measures the Root Mean Squared error between the predicted value (pred) and true value (y). The model targets to…

A)Importing the necessary libraries and the raw data import pandas as pd import numpy as np import matplotlib.pyplot as plt df = pd.read_csv('auto.csv') df.head() print(df.dtypes)B)Basic insight from dataprint(df.dtypes)In the above picture, the make is the brand name of the car it is supposed to be an object.But bore…

import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import numpy as np data1=pd.read_csv('Stock_file_1.csv') data2=pd.read_csv('stock_file_2.txt') unique1,counts1=np.unique(data1['date'],return_counts=ture) unique2,counts2=np.unique(data2['date'],return_counts=ture) data_merged=pd.concat([data1,data2],axis=0,join='inner',ignore_index=true)…

1. We can classify measures in several ways, based on: (i) the manner in which they fill the entries of the similarity matrix, (ii) whether more weight is a function of the frequency of the attribute values, (iii) the arguments used to propose the measure (probabilistic,information-theoretic, etc.).  we will describe…

1. import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.metrics import accuracy_score from sklearn.metrics import plot_confusion_matrix #Dataframe df df= pd.read_csv('fault.csv')…

1. Advantages of support vector machine :   Support vector machine works comparably well when there is an understandable margin of dissociation between classes. It is more productive in high-dimensional spaces. It is effective in instances where the number of dimensions is larger than the number of specimens. Support…

1. Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of  Machine Learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another. Artificial…

1. The main difference between population variance and sample variance relates to calculation of variance. Variance is calculated in five steps. First mean is calculated, then we calculate deviations from the mean, and thirdly the deviations are squared, fourthly the squared deviations are summed up and finally this…

1. Gradient descent is an optimization technique that can find the minimum of an objective function. It is a greedy technique that finds the optimal solution by taking a step in the direction of the maximum rate of decrease of the function. import numpy as np import matplotlib.pyplot as plt def mean_squared_error(y_true,…

1.   2. If proability of occurance of event ka equal then we can compute with simple mean. but if occurance of event are not given or it mat be more than one times the in this situation considered as the expected mean overcome the simple mean.       3. In the Real life distributions are commonly skewed.…