ahmadmustafaanis

ahmadmustafaanis

interviews_details = {
    "https://www.youtube.com/watch?v=OT91E6_Qm1A&list=PLkDaE6sCZn6FcbHlDzbVzf3TVgxzxK7lr&index=2&ab_channel=DeepLearningAI": "Ruslan Salakhutdinov",
    "https://www.youtube.com/watch?v=dwFcodBz_2I&list=PLkDaE6sCZn6FcbHlDzbVzf3TVgxzxK7lr&index=3&ab_channel=DeepLearningAI": "Yuanqing Lin",
    "https://www.youtube.com/watch?v=dqwx-F7Eits&list=PLkDaE6sCZn6FcbHlDzbVzf3TVgxzxK7lr&index=4&ab_channel=DeepLearningAI": "Ian GoodFellow",
    "https://www.youtube.com/watch?v=LpAiPYNnxW0&list=PLkDaE6sCZn6FcbHlDzbVzf3TVgxzxK7lr&index=6&ab_channel=DeepLearningAI": "Pieter Abeel",
    "https://www.youtube.com/watch?v=oJFShOfCZiA&list=PLkDaE6sCZn6FcbHlDzbVzf3TVgxzxK7lr&index=5&ab_channel=DeepLearningAI": "Youshua Bengio",
    "https://www.youtube.com/watch?v=xxu4IqwKw0w&list=PLkDaE6sCZn6FcbHlDzbVzf3TVgxzxK7lr&index=7&ab_channel=DeepLearningAI": "Andrej Karpathy",
    "https://www.youtube.com/watch?v=JS12eb1cTLE&list=PLkDaE6sCZn6FcbHlDzbVzf3TVgxzxK7lr&index=8&ab_channel=DeepLearningAI": "Yann Lecunn",

ahmadmustafaanis

from fpdf import FPDF
for hero_num in range(len(self.all_interviews_results)):
    pdf = FPDF()  # Creates a new PDF File for each hero
    pdf.add_page() 
    pdf.set_font("Arial", "B", 16) # sets font for title
    pdf.cell(0,0,txt=f"Interview of {list(interviews_details.values())[hero_num]}", ln=1, align="C") # Adds the Title to the PDF, title is the name of the hero.
    pdf.ln()
    pdf.set_font("Arial", size=12) # Font for other text
    for line in range(len(self.all_interviews_results[hero_num]["segments"])):                
        pdf.cell(200, 10,txt =self.all_interviews_results[hero_num]["segments"][line]["text"], ln=1,)  # Adds each seperate line to PDF 

ahmadmustafaanis

ahmadmustafaanis

import numpy as np
import matplotlib.pyplot as plt
import cv2

def template_detection(image, template):

    methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
                'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']

ahmadmustafaanis

import numpy as np
from fastapi import FastAPI, Form
import pandas as pd
from starlette.responses import HTMLResponse 
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
import tensorflow as tf
import re

def preProcess_data(text): #cleaning the data

ahmadmustafaanis

model2 = Sequential([
    Dense(512, activation='tanh', input_shape = X_train[0].shape, kernel_regularizer='l1'), #Only change is here where we add kernel_regularizer
    Dense(512//2, activation='tanh'),
    Dense(512//4, activation='tanh'),
    Dense(512//8, activation='tanh'),
    Dense(32, activation='relu'),
    Dense(3, activation='softmax')
])

model2.compile(optimizer='sgd',loss='categorical_crossentropy', metrics=['acc', 'mse'])

ahmadmustafaanis

model1 = Sequential([
    Dense(512, activation='tanh', input_shape = X_train[0].shape),
    Dense(512//2, activation='tanh'),
    Dense(512//4, activation='tanh'),
    Dense(512//8, activation='tanh'),
    Dense(32, activation='relu'),
    Dense(3, activation='softmax')
])
print(model1.summary())
model1.compile(optimizer='sgd',loss='categorical_crossentropy', metrics=['acc', 'mse'])

ahmadmustafaanis

from sklearn.datasets import load_iris
from tensorflow.keras.models import Sequential
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.layers import Dense
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
iris = load_iris()
X = iris.data
y = iris.target
y = to_categorical(y) #converting output to one-hot vector

ahmadmustafaanis

#include "BST.h"

node* BST::search(int x)
{
	if (x == root->data)
	{
		return root;
	}
	node* temp = root;
	while (temp)