title: Backward Propagation - Gradient error [Python]
body: <p>I am working through Andrew Ng new deep learning Coursera course, week2.</p>
  
  <p>We are supposed to implement a logistic regression algorithm.<br>
  I am stuck at gradient code ( <code>dw</code> ) - giving me a syntax error.</p>
  
  <p>The algorithm is as follows:</p>
  
  <pre><code>import numpy as np
  
  def propagate(w, b, X, Y):
      m = X.shape[1]
  
      A = sigmoid(np.dot(w.T,X) + b )  # compute activation
      cost = -(1/m)*(np.sum(np.multiply(Y,np.log(A)) + np.multiply((1-Y),np.log(1-A)), axis=1)    
  
      dw =(1/m)*np.dot(X,(A-Y).T)
      db = (1/m)*(np.sum(A-Y))
      assert(dw.shape == w.shape)
      assert(db.dtype == float)
      cost = np.squeeze(cost)
      assert(cost.shape == ())
  
      grads = {"dw": dw,
               "db": db}
  
      return grads, cost
  </code></pre>
  
  <p>Any ideas why I keep on getting this syntax error?</p>
  
  <pre><code>File "&lt;ipython-input-1-d104f7763626&gt;", line 32
      dw =(1/m)*np.dot(X,(A-Y).T)
       ^
  SyntaxError: invalid syntax
  </code></pre>