Leveraging In-Play Betting

Advantages: <ul> <span style="margin-left: 20px;"><span style="font-weight: bold;">Flexibility:</span> Adjust bets based on match flow.</span> <span style="margin-left: 20px;"><span style="font-weight: bold;">Live Data:</span> Use real-time statistics to inform decisions.</span> </ul> <span style="font-weight: bold;">Challenges:</span><ul> <span style="margin-left: 20px;"><span style="font-weight: bold;">Emotional Decisions:</span> Avoid impulsive bets based on early events.</span> <span style="margin-left: 20px;"><span style="font-weight: bold;">Odds Fluctuations:</span> Be aware of rapid changes in odds.</span> </ul>

In-Depth Team Profiles

AFC Ajax Women's Team Analysis

AFC Ajax has been a dominant force in Eredivisie Women’s league. Their tactical discipline and robust defense make them formidable opponents. Key players like Lieke Martens bring creativity and flair to their attack, often turning games in their favor with individual brilliance.

•  Tactical Overview: Ajax employs a flexible 4-3-3 formation, adapting to opponents with ease.
•  Critical Players: Martens’ vision and goal-scoring ability are pivotal.
•  Injury Concerns: The absence of key defenders could impact their defensive solidity.
•  Betting Angle: Odds favor Ajax in home matches but consider value bets against top-tier teams.
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VVSB's Tactical Approach

VVSB has shown resilience throughout the season despite facing stiff competition. Their commitment to an attacking style of play often results in high-scoring games. However, defensive lapses can be exploited by well-prepared opponents.

•  Tactical Overview: VVSB prefers an aggressive 4-4-2 setup.
•  Critical Players: The partnership between their forwards is crucial for breaking down defenses.
•  Injury Concerns: Gaps in midfield due to injuries could affect ball retention.
•  Betting Angle: Odds fluctuate based on recent form; consider backing them when they have momentum.
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Prominent Betting Markets for Eredivisie Women Matches

Betting on Eredivisie Women’s matches offers diverse markets that cater to different preferences and strategies. Here are some prominent ones worth exploring:

Main Betting Markets

•  Match Winner: The most straightforward bet – predicting which team will win.
•  Total Goals (Over/Under): Analyze both teams' attacking capabilities and defensive records.
•  Correct Score: Predicting the exact final score offers higher rewards but comes with greater risk.
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Specialized Betting Markets <|repo_name|>abednadi/Deep-Learning-Specialization-Coursera<|file_sep|>/Neural Networks and Deep Learning/week4/assignment/assignment.py import numpy as np import h5py import matplotlib.pyplot as plt import scipy from PIL import Image from scipy import ndimage from lr_utils import load_dataset # Loading the data (cat/non-cat) train_x_orig, train_y_orig, test_x_orig, test_y_orig, classes = load_dataset() # Reshape the training and test examples train_x_flatten = train_x_orig.reshape(train_x_orig.shape[0], -1).T # The "-1" makes reshape flatten the remaining dimensions test_x_flatten = test_x_orig.reshape(test_x_orig.shape[0], -1).T # Standardize data to have feature values between 0 and 1. train_x = train_x_flatten/255. test_x = test_x_flatten/255. # Convert training and test labels to one hot matrices def convert_to_one_hot(Y,n_classes=6): Y = np.eye(n_classes)[Y].T return Y train_y = convert_to_one_hot(train_y_orig) test_y = convert_to_one_hot(test_y_orig) def sigmoid(z): sigma = 1/(1+np.exp(-z)) return sigma def initialize_with_zeros(dim): w=np.zeros((dim,1)) b=0 return w,b def propagate(w,b,X,Y): m=X.shape[1] A=sigmoid(np.dot(w.T,X)+b) cost=(-1/m)*(np.sum((Y*np.log(A))+(1-Y)*np.log(1-A))) dw=(1/m)*(np.dot(X,(A-Y).T)) db=(1/m)*(np.sum(A-Y)) grad={"dw":dw, "db":db} return grad,cost def optimize(w,b,X,Y,num_iterations,learning_rate): cost_history=np.zeros((num_iterations,)) for i in range(num_iterations): grad,cost=propagate(w,b,X,Y) w=w-learning_rate*grad["dw"] b=b-learning_rate*grad["db"] cost_history[i]=cost params={"w":w, "b":b} grads={"dw":grad["dw"], "db":grad["db"]} return params,grads,cost_history def predict(w,b,X): m=X.shape[1] Y_prediction=np.zeros((1,m)) w=w.reshape(X.shape[0],1) A=sigmoid(np.dot(w.T,X)+b) for i in range(A.shape[1]): if A[0,i]<=0.5: Y_prediction[0,i]=0 else: Y_prediction[0,i]=1 return Y_prediction def model(X_train,Y_train,X_test,Y_test,num_iterations=2000,learning_rate=0.5): w,b=initialize_with_zeros(X_train.shape[0]) params,cost_history=optimize(w,b,X_train,Y_train,num_iterations,num_iterations) w=params["w"] b=params["b"] Y_prediction_test=predict(w,b,X_test) Y_prediction_train=predict(w,b,X_train) print("train accuracy:{} %".format(100-np.mean(np.abs(Y_prediction_train-Y_train))*100)) print("test accuracy:{} %".format(100-np.mean(np.abs(Y_prediction_test-Y_test))*100)) dict={} dict["cost_history"]=cost_history dict["Y_prediction_test"]=Y_prediction_test dict["Y_prediction_train"]=Y_prediction_train dict["w"]=w dict["b"]=b return dict d=model(train_x,test_y,test_x,test_y,num_iterations=2000)<|file_sep|># Assignment Week 5 - Building Deep Neural Network In this assignment you will build a deep neural network that learns representations of words. ### Instructions You will learn how to use word embeddings using an RNN text generator. You will start by training skipgram word embeddings on [text8](http://mattmahoney.net/dc/textdata). Then you will train a character-level RNN text generator. Finally you will generate new text using your trained model. ## Word Embedding Word embeddings are a very efficient way to represent words and uncover relationships among words. For example if we have two word vectors `King - Man + Woman`, we get `Queen`. This means that our vector space captures information about gender. You will learn about word embeddings by implementing skipgram model. ### Skipgram model  The skipgram model is used to learn word embeddings by predicting context given a word. The skipgram model consists of an input layer (`word vector`) connected through weights (`W1`) to hidden layer (`hidden layer size`) connected through weights (`W2`) to output layer (`vocabulary size`). ### Word vector A word vector represents a word as an array of numbers which we call `embedding`. ### Vocabulary A vocabulary is a list of all unique words in our corpus. For example `I love machine learning` contains vocabulary `[I, love, machine learning]`. ### Embedding layer The embedding layer takes a word vector as input which we call `input word vector`. It then looks up this word vector in `W1` matrix using one-hot encoding of input word vector which we call `one-hot input word vector`. Finally it outputs hidden layer which we call `hidden layer`. <|repo_name|>abednadi/Deep-Learning-Specialization-Coursera<|file_sep|>/Convolutional Neural Networks/week4/assignment/README.md # Assignment Week 4 - Object Detection Using You Only Look Once (YOLO) In this assignment you will implement YOLO v2. YOLO (You Only Look Once) is a popular algorithm because it achieves high accuracy while also being able to run in real-time. This algorithm "only looks once" at the image in order to detect objects within it. Today you will learn how YOLO works. YOLO breaks an image into regions and predicts bounding boxes and probabilities for each region. These bounding boxes are weighted by the predicted probabilities. ## YOLO architecture  The architecture takes an image as input and outputs scores for each bounding box category along with boxes coordinates. ## YOLO pipeline  YOLO divides image into regions or cells. Each cell predicts bounding boxes depending on what object appears within that cell. Bounding boxes contain coordinates of center point `(x,y)` along with height `H` and width `W`.<|repo_name|>abednadi/Deep-Learning-Specialization-Coursera<|file_sep|>/Convolutional Neural Networks/week4/assignment/layers_utils.py import numpy as np def identity_block(X, f, filters): F1 , F2 , F3 = filters def convolutional_block(X,f,filters,s):