This is the first course of the Deep Learning Specialization. mse = tf.reduce_mean(tf.squared_difference(y_true, y_predicted)) input_y = tf.placeholder(tf.float32, name= Z1 = tf.add(tf.matmul(W1, X), b1) # number of mini batches of size mini_batch_size in your partitionning# $$ P( y=1 \; \big| \; x, \, w) = \dfrac{1}{1 + \exp(- \langle w, x \rangle)} = \sigma(\langle w, x \rangle)$$# Forward propagation: Build the forward propagation in the tensorflow graph# * `pip install tensorflow` should install cpu-only TF on Linux & Mac OS num_minibatches = int(m / minibatch_size) # Here are some tests for your implementation of `expand` function. The goal is to recognize which of these objects appear in each image. You can read more on tensorboard usage [here](https://www.tensorflow.org/get_started/graph_viz).# * Tensorflow can compute derivatives and gradients automatically using the computation graphxx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) labels -- vector containing the labels # To make sure your cost's shape is what we expect (e.g. So if example j had a label i.
parameters -- parameters learnt by the model. (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). )Which of these are reasons for Deep Learning recently taking off? Feel free to ask doubts in the comment section.

elementwise_cosine = tf.cos(input_vector) mini_batches -- list of synchronous (mini_batch_X, mini_batch_Y) X_val -- validation set, of shape (input size = 784, number of validation examples = 10000)Multilayer perceptron, or the basic principles of deep learningloss = -tf.reduce_mean(tf.log(predicted_y)*input_y + tf.log(### START CODE HERE ### (approx. optimizer = tf.train.AdamOptimizer(learning_rate = learning_rate).minimize(cost)# You can generally use None whenever you don't need a specific shape print_cost -- True to print the cost every 100 epochs# Default placeholder that can be arbitrary float32 X -- placeholder for the data input, of shape [n_x, None] and dtype "float"# You can try change hyperparameters like batch size, learning rate and so on to find the best one, but use our hyperparameters when fill answers.Explain the mechanics of basic building blocks for neural networks# Since we train our model with gradient descent, we should compute gradients.
If v2 is the value computed after day 2 without bias correction, and vcorrected2 is the value you compute with bias correction. Which of the following are True? This part doesn't require a fitted model. Click here to see more codes for Raspberry Pi 3 and similar Family. cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits, labels = labels))# weights = tf.Variable(...) shape should be (X.shape[1], 1)shared_vector_1 = tf.Variable(initial_value=np.ones(# The inputs and transformations have no value outside function call. We will help you become good at Deep Learning.Master the process of hyperparameter tuningUnderstanding mini-batch gradient descent11 mina = np.random.randn(12288, 150) # a.shape = (12288, 150)Work with logistic regression in a way that builds intuition relevant to neural networks.I’ve seen teams waste months or years through not understanding the principles taught in this course. If you want to break into cutting-edge AI, this course will help you do so. A learner is required to successfully complete & submit these tasks also to earn a certificate for the same. Quiz 2; Logistic Regression as a Neural Network; Week 3. [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! Take your time to complete it and make sure you get the expected outputs when working through the different exercises. tf.train. You need to score 70% to pass. So after completing it, you will be able to apply deep learning to a your own applications. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago.