Unlock the Thrills of Ligue 1 Tunisia: Daily Matches and Expert Betting Insights
Welcome to the ultimate hub for all things related to Ligue 1 Tunisia. Whether you're a die-hard football fan or a seasoned bettor, our platform offers comprehensive coverage of every match in the league, updated daily. Dive into our expert betting predictions and stay ahead of the game with insightful analyses.
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Keeping up with the fast-paced world of football can be challenging, but our platform ensures you never miss a beat. Here’s how we keep you informed:
- Real-Time Updates: Get live scores, match highlights, and player statistics as they happen.
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Fans are at the heart of football culture. Our platform fosters a sense of community among Ligue 1 Tunisia enthusiasts:
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Betting on football requires strategy and discipline. Here are some tips from our experts to help you succeed:
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The Future of Ligue 1 Tunisia: What's Next?
The future looks bright for Ligue 1 Tunisia. With increasing investments in infrastructure and talent development, the league is poised for greater success. Here’s what you can expect in the coming years:
- New Stadiums: Modern stadiums are being built to enhance the matchday experience for fans.
- Talent Development Programs: Initiatives aimed at nurturing young talent are being implemented across the country.
- Increased International Exposure: Efforts are underway to promote Ligue 1 Tunisia on a global stage, attracting more international players and sponsors.
Frequently Asked Questions (FAQs)
<|repo_name|>raihanmasud1995/Deep-Learning-Coursera<|file_sep|>/README.md
# Deep-Learning-Coursera
This repo contains my solutions of assignments from deeplearning.ai specialization on coursera.
<|repo_name|>raihanmasud1995/Deep-Learning-Coursera<|file_sep|>/DL-Practical-Exercises/week-4/gradient_check.py
import numpy as np
import matplotlib.pyplot as plt
from planar_utils import plot_decision_boundary, sigmoid
def load_planar_dataset():
"""
Generate a dataset for a binary classification problem.
Parameters:
None
Returns:
X -- input dataset of shape (2, number of examples)
Y -- labels of shape (1, number of examples)
"""
np.random.seed(1)
m = 400 # number of examples
N = int(m/2) # number of points per class
D = 2 # dimensionality
X = np.zeros((D,m))
Y = np.zeros((1,m))
a = -2
b = -2
c = +2
d = +2
for j in range(2):
for i in range(N):
if j ==0:
X[:,i+j*N] = np.random.rand(2)*2 - [a,c]
else:
X[:,i+j*N] = np.random.rand(2)*2 - [b,d]
Y[0,i+j*N] = j
return X.T,Y
def initialize_parameters(layer_dims):
"""
Initialize parameters randomly - this is needed for breaking symmetry while training neural networks.
Parameters:
layer_dims -- python array (list) containing the dimensions of each layer in our network
Returns:
parameters -- python dictionary containing your parameters "W1", "b1", ..., "WL", "bL":
Wl -- weight matrix of shape (layer_dims[l], layer_dims[l-1])
bl -- bias vector of shape (layer_dims[l], 1)
"""
L = len(layer_dims) # number of layers in the network
parameters = {}
for l in range(1,L):
parameters['W'+str(l)] = np.random.randn(layer_dims[l], layer_dims[l-1])*0.01
parameters['b'+str(l)] = np.zeros((layer_dims[l],1))
return parameters
def linear_forward(A,W,b):
"""
Calculate forward propagation through one linear layer.
Arguments:
A -- activations from previous layer (or input data): (size of previous layer, number of examples)
W -- weights matrix: numpy array of shape (size of current layer , size of previous layer)
b -- bias vector: numpy array of shape (size of the current layer, number of examples)
Returns:
Z -- output of the current layer before activation function is applied
cache -- tuple containing "A", "W" and "b" ; stored for computing gradients efficiently
"""
Z = np.dot(W,A) + b
cache = (A,W,b)
return Z, cache
def linear_activation_forward(A_prev,W,b,activation):
if activation == "sigmoid":
Z,A_cache = linear_forward(A_prev,W,b)
A,sigmoid_cache = sigmoid(Z)
elif activation == "relu":
Z,A_cache = linear_forward(A_prev,W,b)
A,reLU_cache = relu(Z)
cache = (A_cache,sigmoid_cache,reLU_cache)
return A,cache
def relu(Z):
A = np.maximum(0,Z)
cache = Z
return A,cache
def compute_cost(AL,Y):
m = Y.shape[1]
cost = (-1/m)*(np.dot(Y,np.log(AL).T)+np.dot(1-Y,np.log(1-AL).T))
return cost
def linear_backward(dZ,A_prev,W,b):
m = A_prev.shape[1]
dW =(1/m)*np.dot(dZ,A_prev.T)
db =(1/m)*np.sum(dZ,axis=1).reshape(b.shape[0],-1)
dA_prev=np.dot(W.T,dZ)
return dW,db,dA_prev
def linear_activation_backward(dA,current_cache,layers_activation):
if layers_activation == "relu":
A_cache,sigmoid_cache,reLU_cache=current_cache
dZ=reLU_backward(dA,reLU_cache)
elif layers_activation == "sigmoid":
A_cache,sigmoid_cache,reLU_cache=current_cache
dZ=sigmoid_backward(dA,sigmoid_cache)
dA_prev,dW_db,dA=linear_backward(dZ,A_cache,W,b)
return dA_prev,dW_db
def relu_backward(dA,Z):
dZ=np.array(dA,copy=True)
dZ[Z<=0]=0
return dZ
def sigmoid_backward(dA,Z):
s= sigmoid(Z)
dZ=dA*s*(1-s)
return dZ
def update_parameters(parameters,layers_grads,alpha):
L=len(parameters)//2
for l in range(L):
parameters["W"+str(l+1)]-=alpha*layers_grads["dW"+str(l+1)]
parameters["b"+str(l+1)]-=alpha*layers_grads["db"+str(l+1)]
return parameters
def random_mini_batches(X,Y,minibatch_size=64):
m=X.shape[0]
minibatches=[]
shuffle_indices=np.random.permutation(m)
shuffled_X=X[shuffle_indices,:]
shuffled_Y=Y[shuffle_indices,:]
num_complete_minibatches=m//minibatch_size
for k in range(num_complete_minibatches):
minibatch_X=shuffled_X[k*minibatch_size:(k+1)*minibatch_size,:]
minibatch_Y=shuffled_Y[k*minibatch_size:(k+1)*minibatch_size,:]
minibatch=(minibatch_X,minibatch_Y)
minibatches.append(minibatch)
if m%minibatch_size!=0:
minibatch_X=shuffled_X[num_complete_minibatches*minibatch_size:m,:]
minibatch_Y=shuffled_Y[num_complete_minibatches*minibatch_size:m,:]
minibatch=(minibatch_X,minibatch_Y)
minibatches.append(minibatch)
return minibatches
def predict(X,Y,model):
prediction=model["W"]@X+model["b"]
prediction=prediction.reshape(prediction.shape[0],prediction.shape[1])
Y=Y.reshape(Y.shape[0],Y.shape[1])
predicted=(prediction>=0.5)*np.ones_like(prediction)
print("Accuracy: "+str(np.mean(predicted==Y)))
print("Predicted labels: "+str(predicted))
plt.figure(figsize=(10,8))
plt.scatter(X[:,0],X[:,1],c=predicted.reshape(X.shape[0]),axis='y',cmap=plt.cm.Spectral)
def L_model_forward(X,model):
L=len(model)//2
A=X
for l in range(L-1):
A_prev=A
W=model["W"+str(l+1)]
b=model["b"+str(l+1)]
Z,W_b=linear_forward(A_prev,W,b)
A=sigmoid(Z)
model["cache"]=(X,A,W_b)
AL=A
return AL
def L_model_backward(AL,Y,model):
L=len(model)//2
m=Y.shape[0]
Y=Y.reshape(AL.shape)
dAL=-np.divide(Y,(AL))-np.divide(1-Y,(AL-1))
current_layer=model["cache"]
A_prev,current_W_b=current_layer[0]
W=current_W_b[0]
b=current_W_b[1]
dZ=dAL*sigmoid(current_layer[2])
dW_db,dA_prev=linear_backward(dZ,A_prev,W,b)
model["grads"]["dW"+str(L)]=(dW_db)
model["grads"]["db"+str(L)]=(b)
for l in reversed(range(L-1)):
current_layer=model["cache"][l]
A_prev,current_W_b=current_layer[0]
W=current_W_b[0]
b=current_W_b[1]
dZ=dA_prev*sigmoid(current_layer[2])
dW_db,dA_prev=linear_backward(dZ,A_prev,W,b)
model["grads"]["dW"+str(l+1)]=(dW_db)
model["grads"]["db"+str(l+1)]=(b)
return model
def compute_cost(AL,Y,model):
m=Y.shape[0]
cost=(-np.sum(np.multiply(np.log(AL),Y)+np.multiply(np.log(10**(-10)-AL),10**(-10)-Y)))/m
model["cost"]=cost
return model
def initialize_model(layer_dims):
model={}
model["parameters"]=initialize_parameters(layer_dims)
for l in range(len(model["parameters"])//2):
model["grads"]["dW"+str(l+1)]=None
model["grads"]["db"+str(l+1)]=None
model["cache"]=None
model["cost"]=None
return model
def random_initialize_model(layer_dims):
model={}
model["parameters"]=random_initialize_parameters(layer_dims)
for l in range(len(model["parameters"])//2):
model["grads"]["dW"+str(l+1)]=None
model["grads"]["db"+str(l+1)]=None
model["cache"]=None
model["cost"]=None
return model
def random_initialize_parameters(layer_dims):
L=len(layer_dims)
parameters={}
for l in range(0,L-2):
parameters['W'+str(l+1)]=(np.random.randn(layer_dims[l+2],layer_dims[l+1])*np.sqrt(2/layer_dims[l+2]))
parameters['b'+str(l+1)]=(np.zeros((layer_dims[l+2],layer_dims[l+3])))
parameters['W'+str(L-1)]=(np.random.randn(layer_dims[L-2],layer_dims[L-3])*np.sqrt(2/layer_dims[L-2]))
parameters['b'+str(L-4)]=(np.zeros((layer_dims[L-4],layer_dims[L-5])))
return parameters
def update_model_parameters_random(model,alpha):
L=len(model)//4
for l in range(L):
W=model['parameters']["W"+str(l+4)]
b=model['parameters']["b"+str(l+4)]
dW=model['grads']["dW"+str(l+4)]
db=model['grads']["db"+str(l+4)]
W=W-(alpha*dW)
b=b-(alpha*db)
model['parameters']['W'+str(l+4)]=W
model['parameters']['b'+str(l+4)]=b
return model
def update_model_parameters_random_test(alpha,model,X,Y,num_iterations):
costs=[]
for i in range(num_iterations):
AL=L_model_forward(X,model)
cost=compute_cost(AL,Y,model)["cost"]
model=L_model_backward(AL,Y,model)
model=update_model_parameters_random(model,alpha)
if i%100==0:
print ("Cost after iteration %i: %f" %(i,cost))
costs.append(cost)
plt.plot(np.squeeze(costs))
plt.ylabel('cost')
plt.xlabel('iterations')
plt.title("Learning rate =" + str(alpha))
plt.show()
predict(X,Y,model)
if __name__=="__main__":
X,Y=load_planar_dataset()
n_x=X.shape[0]
n_h=4
n_y=Y.shape[0]
layer_dimensions=[n_x,n_h,n_y]
alpha=0.01
num_iterations=30000
random_model=random_initialize_model(layer_dimensions)
update_model_parameters_random_test(alpha=random_model,X=X,Y=Y,num_iterations=num_iterations)
<|file_sep|># Artificial Neural Network - Building Blocks
# This notebook was created / edited by Raiahan Masud
# Please cite these resources if you find this notebook useful.
# https://github.com/zalandoresearch/fashion-mnist
# https://github.com/zalandoresearch/fashion-mnist/blob/master/utils.py
# http://cs231n.github.io/python