▸ Introduction to deep learning : What does the analogy “AI is the new electricity” refer to? Click here to see solutions for all Machine Learning Coursera Assignments. The people of Peacetopia have a common characteristic: they are afraid of birds. True/False?What will be B.shape? :param X: matrix of features, shape [n_samples,2] (minibatch_X, minibatch_Y) = minibatch# Input vector that _must_ have 10 elements and integer type learning_rate -- learning rate of the optimization :param w: weight vector w of shape [6] for each of the expanded features#if np.absolute(costs[-1] - epoch_cost) < 1e-12: mini_batch_Y = shuffled_Y[:, num_complete_minibatches*mini_batch_size : ]"""draws classifier prediction with matplotlib magic"""# Tensorflow solves this with `tf.Variable` objects. If one example is equal to 0?11? and weight vector w [6], compute scalar loss function using formula above. You should understand:# moving average of gradient norm squared# * True to its name it can manage matrix derivatives# We shall train on a two-class MNIST datasetplt.plot(scalar_space, y_der_by_scalar, label= accuracy = tf.reduce_mean(tf.cast(correct_prediction, visualize(X_expanded[ind, :], y[ind], w, loss) Y -- placeholder for the input labels, of shape [n_y, None] and dtype "float"ans_part6 = compute_loss(X_expanded, y, w) X = tf.placeholder(tf.float32, [n_x, # Create a tf.constant equal to C (depth), name it 'C'.

I will try my best to answer it. Solutions to all quiz and all the programming assignments!!! Click here to see more codes for Raspberry Pi 3 and similar Family. 1 line)# In this programming assignment you will implement a linear classifier and train it using stochastic gradient descent modifications and numpy.# Implement RMSPROP algorithm, which use squared gradients to adjust learning rate:y_train_one_hot = one_hot_matrix(y_train, # Your assignment is to implement the logistic regressionProgramming Assignment: Logistic regression in TensorFlow30 min# * `s.run(output, {placeholder:value})`# * Tensorflow is based on computation graphs# $\sigma(x)$ is available via `tf.nn.sigmoid` and matrix multiplication via `tf.matmul`# Close the session (approx. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. parameter[‘b’ + str(i)] = np.random.randn(layers[i], Gradient descent for Neural Networks9 minWhen to change dev/test sets and metrics11 minWeek 1 Foundations of Convolutional Neural NetworksYour goal is to build an algorithm able to classify new images taken by security cameras from Peacetopia.Quiz: Bird recognition in the city of Peacetopia (case study)15 questionsWhat does this have to do with the brain?3 minWeight Initialization for Deep Networks6 minAI is transforming multiple industries. Summary: Online Courses Seeking for Machine Learning Engineering job Week 3 Hyperparameter tuning, Batch Normalization and Programming FrameworksThis course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. True/False?Programming Assignment: Planar data classification with a hidden layer(B) In this two-step approach, you would first (i) detect the traffic light in the image (if any), then (ii) determine the color of the illuminated lamp in the traffic light.In this course, you will learn the foundations of deep learning. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. [feature0, feature1, feature0^2, feature1^2, feature1*feature2, 1]ans_part5 = compute_loss(X_expanded, y, w)Practice Quiz: Multilayer perceptron4 questions Y -- input target, of shape (10, number of examples) parameters = sess.run(parameters)# to be able to rerun the model without overwriting tf variables# Warning! In fact, the number of examples during test/train is different.# * There's a tensorflow symbolic version for every numpy function# you can make submission with answers so far to check yourself at this stage Don't forget to use expand(X) function (where necessary) in this and subsequent functions. n_y -- scalar, number of classes (from 0 to 9, so -> 10) Creates the placeholders for the tensorflow session.# To make things more intuitive, let's solve a 2D classification problem with synthetic data.# A derivative of scalar_squared by my_scalar cost = -np.sum(cross_entropy)/float(l) s.run(optimizer, {input_X: X_train, input_y: y_train})X_test_flatten = X_test.reshape(X_test.shape[ num_epochs -- number of epochs of the optimization loop init = tf.global_variables_initializer()# In logistic regression the optimal parameters $w$ are found by cross-entropy minimization:# * please note that target `y` are `{0,1}` and not `{-1,1}` as in some formulaePeer-graded Assignment: Your very own neural network2h minibatches = random_mini_batches(X_train, Y_train, minibatch_size, seed)Review Your Peers: Your very own neural network# It can get you the derivative of any graph as long as it knows how to differentiate elementary operations# Just a small reminder of the relevant math: Z2 -- output of forward propagation (output of the last LINEAR unit), of shape (10, number of examples)parameters = model(X_train_flatten, y_train_one_hot, X_val_flatten, y_val_one_hot) Z2 = forward_propagation(X, parameters)# To make your "random" minibatches the same as ours# Yes, the X/y indices mistmach is intentional# We won't be giving you the exact formula this time — instead, try figuring out a derivative with pen and paper. )Which of these are reasons for Deep Learning recently taking off?

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