• Neural networks and deep learning coursera quiz answers week 4

    Neural networks and deep learning coursera quiz answers week 4

    Why I can't represent XOR function without hidden layers? If I have a case like question 2 but with the weights:20, I would get: x1 x2 xor 0 0 0 0 1 1 1 0 1 1 1 0 wouldn't I? In 2 nd one first bit you answered This network outputs approximately 1 only when both inputs are 1.

    In bit 2 also same. Even Though your question is not very clear to me, I am trying to answer it as per my understanding. It won't represent AND gate or any other logic gate.

    Coursera: Neural Networks and Deep Learning (Week 4A) [Assignment Solution] - deeplearning.ai

    The outputs of a neural network are not probabilities, so their sum need not be 1. But it represents classes. So exactly one class must be true for a training data. So sum of all the values must be 1. I didn't get the question numbers 4 and 5 can you please explain in detail?

    I mean how did writing the vectorized implementation here work in place of for loop? Recent Posts. Click here to see solutions for all Machine Learning Coursera Assignments. Feel free to ask doubts in the comment section. I will try my best to answer it.

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    If you find this helpful by any mean like, comment and share the post. This is the simplest way to encourage me to keep doing such work. Share This Facebook Twitter. Juan Bomfim 30 July at Bye 12 September at Bye 17 September at Unknown 23 September at This course is part of the Deep Learning Specialization.

    If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago.

    Coursera: Machine Learning (Week 4) Quiz - Neural Networks: Representation| Andrew NG

    In this course, you will learn the foundations of deep learning. 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 actually works, rather than presenting only a cursory or surface-level description.

    So after completing it, you will be able to apply deep learning to a your own applications.

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    If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. This is the first course of the Deep Learning Specialization. Founded by Andrew Ng, DeepLearning. AI is an education technology company that develops a global community of AI talent. AI's expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future.

    Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today. Learn to set up a machine learning problem with a neural network mindset.

    Learn to use vectorization to speed up your models. Learn to build a neural network with one hidden layer, using forward propagation and backpropagation. Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision. I have learned a lot from this detailed and well-structured course. Programing assignments were very sophisticatedly designed.

    It was challenging, fun, and most importantly it delivered what is aimed. I would love some pointers to additional references for each video. Also, the instructor keeps saying that the math behind backprop is hard. What about an optional video with that? Otherwise, awesome! This is a very good course for people who want to get started with neural networks. Andrew did a great job explaining the math behind the scenes. Assignments are well-designed too.

    Highly recommended. Amazing course, the lecturer breaks makes it very simple and quizzes, assignments were very helpful to ensure your understanding of the content. Hope for future learners you provide code model-answers. If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech.

    We will help you become good at Deep Learning. 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.

    You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry.

    You will practice all these ideas in Python and in TensorFlow, which we will teach. You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice. AI is transforming multiple industries.

    neural networks and deep learning coursera quiz answers week 4

    After finishing this specialization, you will likely find creative ways to apply it to your work.Hey,I am facing problem in linear activation forward function of week 4 assignment Building Deep Neural Network. I think I have implemented it correctly and the output matches with the expected one. I also cross check it with your solution and both were same.

    But the grader marks it, and all the functions in which this function is called as incorrect. Please guide. Recent Posts. While doing the course we have to go through various quiz and assignments in Python. Here, I am sharing my solutions for the weekly assignments throughout the course. These solutions are for reference only.

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    Don't just copy paste the code for the sake of completion. Even if you copy the code, make sure you understand the code first. After this assignment you will be able to:. Notation :. Let's get started!

    neural networks and deep learning coursera quiz answers week 4

    Let's first import all the packages that you will need during this assignment. In [1]: import numpy as np import h5py import matplotlib. These helper functions will be used in the next assignment to build a two-layer neural network and an L-layer neural network. Each small helper function you will implement will have detailed instructions that will walk you through the necessary steps. Here is an outline of this assignment, you will:.

    Figure 1. That is why at every step of your forward module you will be storing some values in a cache. The cached values are useful for computing gradients. In the backpropagation module you will then use the cache to calculate the gradients.

    This assignment will show you exactly how to carry out each of these steps.

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    You will write two helper functions that will initialize the parameters for your model. The first function will be used to initialize parameters for a two layer model.

    Exercise : Create and initialize the parameters of the 2-layer neural network. Instructions :. The initialization for a deeper L-layer neural network is more complicated because there are many more weight matrices and bias vectors. Exercise : Implement initialization for an L-layer Neural Network.

    Now that you have initialized your parameters, you will do the forward propagation module. You will start by implementing some basic functions that you will use later when implementing the model. You will complete three functions in this order:. The linear forward module vectorized over all the examples computes the following equations:.

    Exercise : Build the linear part of forward propagation. In this notebook, you will use two activation functions:. To use it you could just call:. Exercise : Implement the forward propagation of the above model.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Work fast with our official CLI. Learn more.

    neural networks and deep learning coursera quiz answers week 4

    If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. A series of online courses offered by deeplearning. I would like to say thanks to Prof. Andrew Ng and his colleagues for spreading knowledge to normal people and great courses sincerely. 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.

    Please only use it as a reference. The quiz and assignments are relatively easy to answer, hope you can have fun with the courses. We use optional third-party analytics cookies to understand how you use GitHub.

    You can always update your selection by clicking Cookie Preferences at the bottom of the page. For more information, see our Privacy Statement. We use essential cookies to perform essential website functions, e. We use analytics cookies to understand how you use our websites so we can make them better, e.

    Skip to content. Solutions to all quiz and all the programming assignments!!! Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.

    Latest commit. Git stats 22 commits. Failed to load latest commit information. Convolutional Neural Networks. Q10 week-4 quiz. Aug 6, Sep 27, Neural Networks and Deep Learning. Sequence Models.Deep Learning Specialization Course by Coursera. GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. Work fast with our official CLI. Learn more. If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again.

    If nothing happens, download the GitHub extension for Visual Studio and try again. Try to solve the problem by yourself. If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning. 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.

    You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry.

    You will practice all these ideas in Python and in TensorFlow, which we will teach. We use optional third-party analytics cookies to understand how you use GitHub. You can always update your selection by clicking Cookie Preferences at the bottom of the page.

    For more information, see our Privacy Statement. We use essential cookies to perform essential website functions, e. We use analytics cookies to understand how you use our websites so we can make them better, e. Skip to content.GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together. What is the "cache" used for in our implementation of forward propagation and backward propagation?

    Among the following, which ones are "hyperparameters"? Check all that apply. I only list correct options. Note: You can check this Quora post or this blog post. So layer 1 has four hidden units, layer 2 has 3 hidden units and so on.

    Coursera: Neural Networks and Deep Learning (Week 4) Quiz [MCQ Answers] - deeplearning.ai

    Which of the following for-loops will allow you to initialize the parameters for the model? The input and output layers are not counted as hidden layers. During forward propagation, in the forward function for a layer l you need to know what is the activation function in a layer Sigmoid, tanh, ReLU, etc.

    During backpropagation, the corresponding backward function also needs to know what is the activation function for layer l, since the gradient depends on it. During backpropagation you need to know which activation was used in the forward propagation to be able to compute the correct derivative.

    Note: See this image for general formulas. We use optional third-party analytics cookies to understand how you use GitHub. Learn more.

    neural networks and deep learning coursera quiz answers week 4

    You can always update your selection by clicking Cookie Preferences at the bottom of the page. For more information, see our Privacy Statement. We use essential cookies to perform essential website functions, e. We use analytics cookies to understand how you use our websites so we can make them better, e.

    Skip to content. This repository has been archived by the owner. It is now read-only. Permalink Dismiss Join GitHub today GitHub is home to over 50 million developers working together to host and review code, manage projects, and build software together.Recent Posts. What is the "cache" used for in our implementation of forward propagation and backward propagation? It is used to cache the intermediate values of the cost function during training. We use it to pass variables computed during forward propagation to the corresponding backward propagation step.

    It contains useful values for backward propagation to compute derivatives. Correct Correct, the "cache" records values from the forward propagation units and sends it to the backward propagation units because it is needed to compute the chain rule derivatives. We use it to pass variables computed during backward propagation to the corresponding forward propagation step. It contains useful values for forward propagation to compute activations.

    It is used to keep track of the hyperparameters that we are searching over, to speed up computation. Among the following, which ones are "hyperparameters"? Check all that apply. The deeper layers of a neural network are typically computing more complex features of the input than the earlier layers. Correct The earlier layers of a neural network are typically computing more complex features of the input than the deeper layers. True False Correct Forward propagation propagates the input through the layers, although for shallow networks we may just write all the lines in a deeper network, we cannot avoid a for loop iterating over the layers:.

    So layer 1 has four hidden units, layer 2 has 3 hidden units and so on. Which of the following for-loops will allow you to initialize the parameters for the model?

    Feel free to ask doubts in the comment section. I will try my best to answer it. If you find this helpful by any mean like, comment and share the post. This is the simplest way to encourage me to keep doing such work.

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    Neural Networks and Deep Learning

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