JannesKlaas

JannesKlaas

JannesKlaas

JannesKlaas

M&A

Basic Terminology

• Merger: Combination of two companies in which only one survives (A + B = A)
• Consolidation: Combination of two companies into one new one (A + B = C)
• Tender offer: Offer made directly to shareholders (usually in hostile takeover)
• Acquisition: Basically any deal
• Statutory merger: Merger under state laws under which acquirer is incorporated
• Subsidiary merger: Target is kept as a separate firm owned by acquirer
• Fairness opinion: External valuation
• Joint venture: Combine different firms resources into new firm for joint project

JannesKlaas

def train(model,epochs):
    # Train
    #Reseting the win counter
    win_cnt = 0
    # We want to keep track of the progress of the AI over time, so we save its win count history
    win_hist = []
    #Epochs is the number of games we play
    for e in range(epochs):
        loss = 0.
        #Resetting the game

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num_actions = 3  # [move_left, stay, move_right]
hidden_size = 100 # Size of the hidden layers
grid_size = 10 # Size of the playing field

def baseline_model(grid_size,num_actions,hidden_size):
    #seting up the model with keras
    model = Sequential()
    model.add(Dense(hidden_size, input_shape=(grid_size**2,), activation='relu'))
    model.add(Dense(hidden_size, activation='relu'))
    model.add(Dense(num_actions))

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class ExperienceReplay(object):
    """
    During gameplay all the experiences < s, a, r, s’ > are stored in a replay memory. 
    In training, batches of randomly drawn experiences are used to generate the input and target for training.
    """
    def __init__(self, max_memory=100, discount=.9):
        """
        Setup
        max_memory: the maximum number of experiences we want to store
        memory: a list of experiences