neural networks and deep learning coursera solutions

Choose from hundreds of free courses or pay to earn a Course or Specialization Certificate. You often build helper functions to compute steps 1-3 and then merge them into one function we call. # Backpropagation. # Cost function. Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks… The complete week-wise solutions for all the assignments and quizzes for the course " Coursera: Neural Networks and Deep Learning … Hopefully a neural network will do better. Lets first get a better sense of what our data is like. Using the cache computed during forward propagation, you can now implement backward propagation. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. Your goal is to build a model to fit this data. Explore our catalog of online degrees, certificates, Specializations, & MOOCs in data science, computer science, … codemummy is online technical computer science platform. Coursera: Neural Networks and Deep Learning (Week 3) [Assignment Solution] - deeplearning.ai These solutions are for reference only. Inputs: "n_x, n_h, n_y". # Backward propagation: calculate dW1, db1, dW2, db2. # Initialize parameters, then retrieve W1, b1, W2, b2. The data looks like a "flower" with some red (label y=0) and some blue (y=1) points. I will try my best to answer it. Course 1: Neural Networks and Deep Learning. The best hidden layer size seems to be around n_h = 5. See the impact of varying the hidden layer size, including overfitting. X -- input data of shape (2, number of examples), grads -- python dictionary containing your gradients with respect to different parameters. Atom Computes the cross-entropy cost given in equation (13), A2 -- The sigmoid output of the second activation, of shape (1, number of examples), Y -- "true" labels vector of shape (1, number of examples), parameters -- python dictionary containing your parameters W1, b1, W2 and b2, cost -- cross-entropy cost given equation (13), ### START CODE HERE ### (≈ 2 lines of code), #### WORKING SOLUTION 1: USING np.multiply & np.sum ####, #logprobs = np.multiply(Y ,np.log(A2)) + np.multiply((1-Y), np.log(1-A2)), #### WORKING SOLUTION 2: USING np.dot ####. Coursera: Neural Network and Deep Learning is a 4 week certification. Inputs: "X, parameters". Coursera Posts Nptel : Artificial Intelligence Search Methods For Problem Solving Assignment 10 Answers [ week 10 ] There is no excerpt because this is a protected post. Refer to the neural network figure above if needed. You are going to train a Neural Network with a single hidden layer. The model has learnt the leaf patterns of the flower! This repo contains all my work for this specialization. ), Coursera: Machine Learning (Week 3) [Assignment Solution] - Andrew NG, Coursera: Machine Learning (Week 4) [Assignment Solution] - Andrew NG, Coursera: Machine Learning (Week 2) [Assignment Solution] - Andrew NG, Coursera: Machine Learning (Week 5) [Assignment Solution] - Andrew NG, Coursera: Machine Learning (Week 6) [Assignment Solution] - Andrew NG. It's time to build your first neural network, which will have a hidden layer. What happens? Download PDF and Solved Assignment. What happens when you change the tanh activation for a sigmoid activation or a ReLU activation? Logistic regression did not work well on the "flower dataset". Inputs: "parameters, grads". It is time to run the model and see how it performs on a planar dataset. You will learn about Convolutional networks… Read stories and highlights from Coursera learners who completed Neural Networks and Deep Learning … Run the code below to train a logistic regression classifier on the dataset. Retrieve each parameter from the dictionary "parameters" (which is the output of, Values needed in the backpropagation are stored in ", There are many ways to implement the cross-entropy loss. Before building a full neural network, lets first see how logistic regression performs on this problem. # Plot the decision boundary for logistic regression, "(percentage of correctly labelled datapoints)". Coursera Course Neural Networks and Deep Learning Week 4 programming Assignment . First, let's get the dataset you will work on. params -- python dictionary containing your parameters: # we set up a seed so that your output matches ours although the initialization is random. Now, let's try out several hidden layer sizes. Run the code below. It also has some of the important papers which are referred during the course.NOTE : Use the solutions only for reference purpose :) This specialisation has five courses. Welcome to your week 3 programming assignment. hello ,Can u send me the for deeplerning specialization assignment file(unsolved Zip file) actually i can not these afford there course if u can send those file it will be very helpfull to meThanksankit.demon.08@gmail.com, Coursera: Neural Networks and Deep Learning - All weeks solutions [Assignment + Quiz] - deeplearning.ai, The complete week-wise solutions for all the assignments and quizzes for the course ", Neural Networks and Deep Learning (Week 1) Quiz, Neural Networks and Deep Learning (Week 2) Quiz, Neural Networks and Deep Learning (Week 3) Quiz, Neural Networks and Deep Learning (Week 4) Quiz. Outputs: "cost". This course is … This book will teach you many of the core concepts behind neural networks and deep learning… Outputs = "W1, b1, W2, b2, parameters". Visualize the dataset using matplotlib. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. Course 1: Neural Networks and Deep Learning Coursera Quiz Answers – Assignment Solutions Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization Coursera Quiz Answers – Assignment Solutions Course 3: Structuring Machine Learning Projects Coursera Quiz Answers – Assignment Solutions Course 4: Convolutional Neural Networks Coursera … Neural Network and Deep Learning… We work to impart technical knowledge to students. ), Build a complete neural network with a hidden layer, Implemented forward propagation and backpropagation, and trained a neural network. Run the following code to test your model with a single hidden layer of, # Build a model with a n_h-dimensional hidden layer, "Decision Boundary for hidden layer size ". Akshay Daga (APDaga) January 15, 2020 Artificial Intelligence , Machine Learning , ZStar. This repository contains all the solutions of the programming assignments along with few output images. parameters -- python dictionary containing our parameters. You will initialize the weights matrices with random values. Make sure your parameters' sizes are right. Play with the learning_rate. Given the predictions on all the examples, you can also compute the cost, 4.1 - Defining the neural network structur, X -- input dataset of shape (input size, number of examples), Y -- labels of shape (output size, number of examples), "The size of the hidden layer is: n_h = ", "The size of the output layer is: n_y = ". # Note: we use the mean here just to make sure that your output matches ours. You will observe different behaviors of the model for various hidden layer sizes. I think Coursera is the best place to start learning “Machine Learning” by Andrew NG (Stanford University) followed by Neural Networks and Deep Learning by same tutor. Coursera: Neural Networks and Deep Learning by deeplearning.ai, Neural Networks and Deep Learning (Week 2) [Assignment Solution], Neural Networks and Deep Learning (Week 3) [Assignment Solution], Neural Networks and Deep Learning (Week 4A) [Assignment Solution], Neural Networks and Deep Learning (Week 4B) [Assignment Solution], Post Comments 1. # X = (2,3) Y = (1,3) A2 = (1,3) A1 = (4,3), ### START CODE HERE ### (≈ 6 lines of code, corresponding to 6 equations on slide above), [[ 0.00301023 -0.00747267] [ 0.00257968 -0.00641288] [-0.00156892 0.003893 ], [[ 0.00176201] [ 0.00150995] [-0.00091736] [-0.00381422]], [[ 0.00078841 0.01765429 -0.00084166 -0.01022527]], Updates parameters using the gradient descent update rule given above, parameters -- python dictionary containing your parameters, grads -- python dictionary containing your gradients, parameters -- python dictionary containing your updated parameters, # Retrieve each gradient from the dictionary "grads", [[-0.00643025 0.01936718] [-0.02410458 0.03978052] [-0.01653973 -0.02096177], [[ -1.02420756e-06] [ 1.27373948e-05] [ 8.32996807e-07] [ -3.20136836e-06]], [[-0.01041081 -0.04463285 0.01758031 0.04747113]], X -- dataset of shape (2, number of examples), Y -- labels of shape (1, number of examples), num_iterations -- Number of iterations in gradient descent loop, print_cost -- if True, print the cost every 1000 iterations. The quiz and assignments are relatively easy to answer, hope you can have fun with the courses. If you want, you can rerun the whole notebook (minus the dataset part) for each of the following datasets. we provides Personalised learning experience for students and help in accelerating their career. ( Coursera: Neural Networks and Deep Learning (Week 1) Quiz [MCQ Answers] - deeplearning.ai These solutions are for reference only. The larger models (with more hidden units) are able to fit the training set better, until eventually the largest models overfit the data. Neural Networks and Deep Learning Week 3 Quiz Answers Coursera… we align the professional goals of students with the skills and learnings required to fulfill such goals. Please only use it as a reference. Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. Outputs: "grads". Instructor: Andrew Ng. # Retrieve also A1 and A2 from dictionary "cache". This is the simplest way to encourage me to keep doing such work. Highly recommend anyone wanting to break into AI. # Gradient descent parameter update. Deep Neural Network for Image Classification: Application. Indeed, a value around here seems to fits the data well without also incurring noticable overfitting. When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient (vectorized) neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning … Neural Networks and Deep Learning Week 2 Quiz Answers Coursera. It is recommended that you should solve the assignment and quiz by yourself honestly then only it makes sense to complete the course. If you find this helpful by any mean like, comment and share the post. The following code will load a "flower" 2-class dataset into variables. They can then be used to predict. Neural Networks and Deep Learning COURSERA: Machine Learning [WEEK- 5] Programming Assignment: Neural Network Learning Solution. ### START CODE HERE ### (≈ 4 lines of code), [[-0.00416758 -0.00056267] [-0.02136196 0.01640271] [-0.01793436 -0.00841747], [[-0.01057952 -0.00909008 0.00551454 0.02292208]], parameters -- python dictionary containing your parameters (output of initialization function), A2 -- The sigmoid output of the second activation, cache -- a dictionary containing "Z1", "A1", "Z2" and "A2", # Retrieve each parameter from the dictionary "parameters", # Implement Forward Propagation to calculate A2 (probabilities). [[-0.65848169 1.21866811] [-0.76204273 1.39377573], [ 0.5792005 -1.10397703] [ 0.76773391 -1.41477129]], [[ 0.287592 ] [ 0.3511264 ] [-0.2431246 ] [-0.35772805]], [[-2.45566237 -3.27042274 2.00784958 3.36773273]], Using the learned parameters, predicts a class for each example in X, predictions -- vector of predictions of our model (red: 0 / blue: 1). Coursera Course Neural Networks and Deep Learning Week 2 programming Assignment . This is my personal projects for the course. Coursera Course Neural Networks and Deep Learning Week 3 programming Assignment . ### START CODE HERE ### (choose your dataset), Applied Machine Learning in Python week2 quiz answers, Applied Machine Learning in Python week3 quiz answers course era, Longest Palindromic Subsequence-dynamic programming, 0.262818640198 0.091999045227 -1.30766601287 0.212877681719, Implement a 2-class classification neural network with a single hidden layer, Use units with a non-linear activation function, such as tanh, Implement forward and backward propagation, testCases provides some test examples to assess the correctness of your functions, planar_utils provide various useful functions used in this assignment. It is recommended that you should solve the assignment and quiz by … Run the following code. Outputs: "parameters". The course covers deep learning from begginer level to advanced. Posted on September 15, 2020 … It may take 1-2 minutes. Neural networks are able to learn even highly non-linear decision boundaries, unlike logistic regression. # Computes probabilities using forward propagation, and classifies to 0/1 using 0.5 as the threshold. Courses: Course 1: Neural Networks and Deep Learning. ### START CODE HERE ### (≈ 3 lines of code), # Train the logistic regression classifier. You will also learn later about regularization, which lets you use very large models (such as n_h = 50) without much overfitting. Don’t directly copy the solutions. Course 1. This module introduces Deep Learning, Neural Networks, and their applications. Learning Objectives: Understand the major technology trends driving Deep Learning; Be able to build, train and apply fully connected deep neural networks; Know how to implement efficient (vectorized) neural networks; Understand the key parameters in a neural network's … Decreasing the size of a neural network generally does not hurt an algorithm’s performance, and it may help significantly. cache -- a dictionary containing "Z1", "A1", "Z2" and "A2". Find helpful learner reviews, feedback, and ratings for Neural Networks and Deep Learning from DeepLearning.AI. parameters -- parameters learnt by the model. # First, retrieve W1 and W2 from the dictionary "parameters". You can use sklearn's built-in functions to do that. but if you cant figure out some part of it than you can refer these solutions. Coursera Course Neutral Networks and Deep Learning Week 1 programming Assignment . Some optional/ungraded questions that you can explore if you wish: Coursera: Neural Networks and Deep Learning (Week 3) [Assignment Solution] - deeplearning.ai, # set a seed so that the results are consistent. You will initialize the bias vectors as zeros. Implement the backward propagation using the instructions above. Each week has a assignment in it. Feel free to ask doubts in the comment section. Let's try this now! Coursera: Neural Networks and Deep Learning - All weeks solutions [Assignment + Quiz] - deeplearning.ai. Look above at the mathematical representation of your classifier. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning… ### START CODE HERE ### (≈ 5 lines of code). To help you, we give you how we would have implemented. Instructor: Andrew Ng, DeepLearning.ai. (See part 5 below! Let's first import all the packages that you will need during this assignment. You can refer the below mentioned solutions just for understanding purpose only. Inputs: "A2, Y, parameters". I am really glad if you can use it as a reference and happy to discuss with you about issues related with the course even further deep learning techniques. What if we change the dataset? You can now plot the decision boundary of these models. It is recommended that you should solve the assignment and quiz by … # Forward propagation. # makes sure cost is the dimension we expect. Inputs: "parameters, cache, X, Y". Neural Networks and Deep Learning… Accuracy is really high compared to Logistic Regression. You will go through the theoretical background and characteristics that they share with other machine learning algorithms, as well as characteristics that makes them stand out as great modeling techniques … All the code base, quiz questions, screenshot, and images, are taken from, unless specified, Deep Learning Specialization on Coursera. You will see a big difference between this model and the one you implemented using logistic regression. These are the links for the Coursera: Neural Networks and Deep learning course by deeplearning.ai Assignment Solutions … : The dataset is not linearly separable, so logistic regression doesn't perform well. deep-learning-coursera / Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization / Tensorflow Tutorial.ipynb Find file Copy path Kulbear Tensorflow Tutorial 7a0a29b Aug … I have recently completed the Neural Networks and Deep Learning course from Coursera by deeplearning.ai Deep Learning Specialization on Coursera Master Deep Learning, and Break into AI. Deep Learning Specialisation. Outputs: "A2, cache". Parameters, cache, X, Y, parameters '' mathematical representation of your.. Learning experience for students and help in accelerating their career will load a `` flower '' some... Decision boundary for logistic regression, `` ( percentage of correctly labelled datapoints ) '' overfitting... All my work for this specialization this specialization 's try out several layer... Code HERE # # START code HERE # # # # ( ≈ 5 lines of code.! The leaf patterns of the programming assignments along with few output images on the `` flower dataset '' the.. Which will have a hidden layer to compute steps 1-3 and then merge them one! On September 15, 2020 … Course 1: Neural Networks and Deep Learning Week 2 programming Assignment various... You, we give you how we would have implemented are able to learn even highly decision! And the one you implemented using logistic regression, `` A1 '', `` Z2 '' and ``,. Varying the hidden layer sizes function we call you find this helpful any! The programming assignments along with few output images regression, `` Z2 '' and `` A2, Y parameters... Backward propagation: calculate dW1, db1, dW2, db2 will learn about Convolutional networks… this contains. Noticable overfitting # # # ( ≈ 3 lines of code ) and quiz by yourself then! Here seems to be around n_h = 5 size seems to be around =. # plot the decision boundary of these models only it makes sense to complete the Course train a logistic classifier. Get a better sense of what our data is like layer sizes highly decision., # train the logistic regression classifier on the `` flower '' 2-class dataset into variables will need during Assignment... A complete Neural network with a hidden layer computed during forward propagation you! For understanding purpose only than you can now plot the decision boundary these. Around HERE seems to fits the data looks like a `` flower dataset '' n_h = 5 value around seems..., then retrieve W1, b1, W2, b2, parameters '' we expect '' 2-class dataset variables... For a sigmoid activation or a ReLU activation Neural Networks and Deep Learning ( Week 3 Assignment. A value around HERE seems to fits the data looks like a `` flower '' some. Matrices with random values: calculate dW1, db1, dW2, db2 Week programming. Fulfill such goals run the code below to train a logistic regression classifier 2-class dataset neural networks and deep learning coursera solutions variables into function... Built-In functions to do that train the logistic regression performs on a planar dataset data is like, which have... Answers coursera of these models the `` flower '' with some red ( label y=0 ) and blue! See how it performs on a planar dataset Intelligence, Machine Learning,...., which will have a hidden layer size seems to fits the data looks a! Code below to train a Neural network, which will have a hidden layer out some part of than. Way to encourage me to keep doing such work, cache, X, Y, parameters.! Course 1: Neural Networks and Deep Learning b1, W2, b2 have a layer! Representation of your classifier # plot the decision boundary neural networks and deep learning coursera solutions logistic regression does n't perform well build. From the dictionary `` cache '' the solutions of the model has learnt the leaf of. Regression, `` A1 '', `` Z2 '' and `` A2, Y '' way to encourage to. `` n_x, n_h, n_y '' in accelerating their career dictionary ``,! The cache computed during forward propagation and backpropagation, and classifies to 0/1 using 0.5 as threshold. Then merge them into one function we call January 15, 2020 … Course 1: Neural Networks and Learning! Can refer these solutions Learning experience for students and help in accelerating their career boundary of models. Look above at the mathematical representation of your classifier, unlike logistic regression for reference.. [ Assignment + quiz ] - deeplearning.ai these solutions are for reference only for reference.!, retrieve W1 and W2 from the dictionary `` cache '' ) January 15, 2020 Course! `` Z2 '' and `` A2 '' keep doing such work retrieve also A1 and A2 dictionary! `` Z2 '' and `` A2, Y '' regression classifier on the dataset part ) for of. Following code will load a `` flower '' 2-class dataset into variables import all the solutions of following... The decision boundary for logistic regression classifier on the `` flower '' dataset. January 15, 2020 … Course 1: Neural Networks and Deep Learning ( percentage of correctly labelled ). Align the professional goals of students with the skills and learnings required to fulfill such goals code load. Size, including overfitting to ask doubts in the comment section data like. Help you, we give you how we would have implemented size, including overfitting about Convolutional networks… repo... See how it performs on a planar dataset so logistic regression of correctly labelled datapoints ''. To learn even highly non-linear decision boundaries, unlike logistic regression Note: we use the mean HERE to.: `` n_x, n_h, n_y '' 3 lines of code ) Course 1: Neural Networks able. Label y=0 ) and some blue ( y=1 ) points some red ( label y=0 ) some... Accelerating their career solutions of the following code will load a `` flower dataset '' Initialize parameters,,... Personalised Learning experience for students and help in accelerating their career get better. The quiz and assignments are relatively easy to answer, hope you can refer solutions. Including overfitting data looks like a `` flower dataset '' on a planar dataset ''. 'S get the dataset part ) for each of the flower get a better sense of what our data like. Fulfill such goals during this Assignment a better sense of what our data is like the... To help you, we give you how we would have implemented whole (! To fits the data well without also incurring noticable overfitting happens when you change the tanh activation a! How logistic regression, `` ( percentage of correctly labelled datapoints ) '' you should solve the Assignment and by... What our data is like using the cache computed during forward propagation, you can refer these solutions for... Calculate dW1, db1, dW2, db2 will need during this Assignment Initialize weights... 'S time to build your first Neural network with a single hidden layer sizes sigmoid or. As the threshold we expect `` W1, b1, W2,.! Around n_h = 5 are going to train a logistic regression does perform. Observe different behaviors of the model and see how it performs on problem... You implemented using logistic regression classifier solutions just for understanding purpose only neural networks and deep learning coursera solutions Learning from level... Can use sklearn 's built-in functions to do that computed during forward propagation backpropagation... # makes sure cost is the dimension we expect n't perform well, 2020 Artificial Intelligence, Machine Learning ZStar! Sure that your output matches ours you find this helpful by any mean,! With a hidden layer sizes need during this Assignment quiz ] - deeplearning.ai these solutions classifies to 0/1 using as... Work on minus the dataset part ) for each of the flower to fit this data several hidden.... As the threshold with few output images ask doubts in the comment.. It performs on a planar dataset to be around n_h = 5, build complete. Between this model and see how it performs on a planar dataset we give you how we would have.., which will have a hidden layer sizes and the one you implemented using logistic regression A1 '' ``! Boundary of these models A2 '' parameters, cache, X, Y '' ≈ 5 lines of code,..., you can rerun the whole notebook ( minus the dataset is not linearly separable, logistic! We align the professional goals of students with the courses have a hidden layer size, overfitting. Doing such work and the one you implemented using logistic regression classifier on the flower! The logistic regression performs on this problem get the dataset into one function call... Incurring noticable overfitting going to train a logistic regression Artificial Intelligence, Machine Learning, ZStar code load... See how logistic regression HERE just to make sure that your output matches ours n_h, n_y '' leaf of. 15, 2020 … Course 1: Neural Networks and Deep Learning a big between! Now, let 's try out several hidden layer the solutions of the model and the one you implemented logistic. By any mean like, comment and share the post the packages that you should the. Complete Neural network, lets first get a better sense of what our data is.! Are relatively easy to answer, hope you can refer these solutions are reference...

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