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

▸  deep neural network for image classification: application. i have recently completed the neural networks and deep learning course from coursera by deeplearning.ai, 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. >  it is recommended that you should solve the assignments by yourself honestly then only it makes sense to complete the course. >  but, in case you stuck in between, feel free to refer to the solutions provided by me., 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. click here : coursera: neural networks & deep learning (week 4a) scroll down  for coursera: neural networks & deep learning (week 4b) assignments. (adsbygoogle = window.adsbygoogle || []).push({});.

Recommended Machine Learning Courses: Coursera: Machine Learning    Coursera: Deep Learning Specialization Coursera: Machine Learning with Python Coursera: Advanced Machine Learning Specialization Udemy: Machine Learning LinkedIn: Machine Learning Eduonix: Machine Learning edX: Machine Learning Fast.ai: Introduction to Machine Learning for Coders

Deep Neural Network for Image Classification: Application

When you finish this, you will have finished the last programming assignment of week 4, and also the last programming assignment of this course you will use use the functions you'd implemented in the previous assignment to build a deep network, and apply it to cat vs non-cat classification. hopefully, you will see an improvement in accuracy relative to your previous logistic regression implementation. after this assignment you will be able to: build and apply a deep neural network to supervised learning. let's get started, 1 - packages.

  • numpy  is the fundamental package for scientific computing with Python.
  • matplotlib  is a library to plot graphs in Python.
  • h5py  is a common package to interact with a dataset that is stored on an H5 file.
  • PIL  and  scipy  are used here to test your model with your own picture at the end.
  • dnn_app_utils provides the functions implemented in the "Building your Deep Neural Network: Step by Step" assignment to this notebook.
  • np.random.seed(1) is used to keep all the random function calls consistent. It will help us grade your work.

2 - Dataset

deep learning assignment week 4

Check-out our free tutorials on IOT (Internet of Things):

3 - Architecture of your model

  • A 2-layer neural network
  • An L-layer deep neural network

3.1 - 2-layer neural network

deep learning assignment week 4

  • The input is a (64,64,3) image which is flattened to a vector of size  ( 12288 , 1 ) .
  • The corresponding vector:  [ x 0 , x 1 , . . . , x 12287 ] T  is then multiplied by the weight matrix  W [ 1 ]  of size  ( n [ 1 ] , 12288 ) .
  • You then add a bias term and take its relu to get the following vector:  [ a [ 1 ] 0 , a [ 1 ] 1 , . . . , a [ 1 ] n [ 1 ] − 1 ] T .
  • You then repeat the same process.
  • You multiply the resulting vector by  W [ 2 ]  and add your intercept (bias).
  • Finally, you take the sigmoid of the result. If it is greater than 0.5, you classify it to be a cat.

3.2 - L-layer deep neural network

deep learning assignment week 4

  • The input is a (64,64,3) image which is flattened to a vector of size (12288,1).
  • The corresponding vector:  [ x 0 , x 1 , . . . , x 12287 ] T  is then multiplied by the weight matrix  W [ 1 ] W [ 1 ]  and then you add the intercept  b [ 1 ] . The result is called the linear unit.
  • Next, you take the relu of the linear unit. This process could be repeated several times for each  ( W [ l ] , b [ l ] )  depending on the model architecture.
  • Finally, you take the sigmoid of the final linear unit. If it is greater than 0.5, you classify it to be a cat.

3.3 - General methodology

4 - two-layer neural network.

deep learning assignment week 4

5 - L-layer Neural Network

deep learning assignment week 4

6) Results Analysis

deep learning assignment week 4

  • Cat body in an unusual position
  • Cat appears against a background of a similar color
  • Unusual cat color and species
  • Camera Angle
  • Brightness of the picture
  • Scale variation (cat is very large or small in image)

7) Test with your own image (optional/ungraded exercise)

deep learning assignment week 4

  • for auto-reloading external module:  http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
  • coursera.org

deep learning assignment week 4

Hi sir , in week 4 assignment at 2 layer model I am getting an error as" cost not defined"and my code is looks pretty same as the one you have posted please can you tell me what's wrong in my code

yes even for me .. please suggest something what to do

deep learning assignment week 4

Have you tried running all the cell in proper given sequence. Because, In jupyter notebook a particular cell might be dependent on previous cell. I think, there in no problem in code. You are doing something wrong with the executing the code. Please check once.

fundamentals of scalable data science week 1 assignment in coursera solution I am finding some problem

Hi. i seen function predict(), but the articles not mention, thank sir

hi , im getting a " too many values to unpack : error and ive used your code only kindly help me get this

for which cell you are getting this error??

brother can u kindly whatsapp me on 9735255861 ? then i can share with u some screenshots of the error that we are facing. The old code is no more running without error in th week 4 assignments. kindly help me out . we cannot even pass the assignments. :|

Hi, In case the assignment question is changed for week 4 then I am now aware of that. But the question is same then I am sure about the solutions. I don't have access to the course now. So can't check it now.

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Deep-Learning-Specialization

Coursera deep learning specialization, convolutional neural networks.

This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.

  • Understand how to build a convolutional neural network, including recent variations such as residual networks.
  • Know how to apply convolutional networks to visual detection and recognition tasks.
  • Know to use neural style transfer to generate art.
  • Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data.

Week 1: Foundations of Convolutional Neural Networks

Key concepts of week 1.

  • Understand the convolution operation
  • Understand the pooling operation
  • Remember the vocabulary used in convolutional neural network (padding, stride, filter, …)
  • Build a convolutional neural network for image multi-class classification

Assignment of Week 1

  • Quiz 1: The basics of ConvNets
  • Programming Assignment: Convolutional Model: step by step
  • Programming Assignment: Convolutional Model: application

Week 2: Deep convolutional models

Key concepts of week 2.

  • Understand multiple foundational papers of convolutional neural networks
  • Analyze the dimensionality reduction of a volume in a very deep network
  • Understand and Implement a Residual network
  • Build a deep neural network using Keras
  • Implement a skip-connection in your network
  • Clone a repository from github and use transfer learning

Assignment of Week 2

  • Quiz 2: Deep convolutional models
  • Programming Assignment: Residual Networks

Week 3: Convolutional Neural Networks

Key concepts of week 3.

  • Understand the challenges of Object Localization, Object Detection and Landmark Finding
  • Understand and implement non-max suppression
  • Understand and implement intersection over union
  • Understand how we label a dataset for an object detection application
  • Remember the vocabulary of object detection (landmark, anchor, bounding box, grid, …)

Assignment of Week 3

  • Quiz 3: Detection algorithms
  • Programming Assignment: Car detection with YOLO

Week 4: Special applications: Face recognition & Neural style transfer

Discover how CNNs can be applied to multiple fields, including art generation and face recognition. Implement your own algorithm to generate art and recognize faces!

Assignment of Week 4

  • Quiz 4: Special applications: Face recognition & Neural style transfer
  • Programming Assignment: Art generation with Neural Style Transfer
  • Programming Assignment: Face Recognition

Course Certificate

Certificate

NPTEL Deep Learning – IIT Ropar Assignment 4 Answers 2023

NPTEL Deep Learning – IIT Ropar Assignment 4 Answers 2023:- In this post, We have provided answers of Deep Learning – IIT Ropar Assignment 4. We provided answers here only for reference. Plz, do your assignment at your own knowledge.

NPTEL Deep Learning – IIT Ropar Week 4 Assignment Answer 2023

1. Which step does Nesterov accelerated gradient de s cent perform before finding the update size?

  • Increase the momentum
  • Estimate the next position of the parameters
  • Adjust the learning rate
  • Decrease the st e p size

2. Select the parameter of vanilla gradient descent controls the step size in the direction of the gradient.

  • Learning rate
  • None of t h e above

3. What does the distance between two contour lines on a contour map represent?

  • The change in the output of function
  • The direction of the function
  • The rate of change of the function
  • None of the a b ove

4. Which of the following represents the contour plot of the function f(x,y) = x2−y?

5. What is the main advantage of using Adagrad over other optimization algorithms?

  • It converges faster than other optimization algorithms.
  • It is less sensitive to the choice of h yperparameters (learning rate).
  • It is more memory-efficient than other optimization algorithms.
  • It is less likely to get stuck in local optima than other optimization algorithms.

6. We are training a neural network using the vanilla gradient descent algorithm. We observe that the change in weights is small in successive iterations. Wh a t are the possible causes for the following phenomenon?

  • ∇w is s m all
  • ∇w is large

7. You are given labeled data which we call X where rows are data points and columns feature. One column has most of its values as 0. What algorithm should we use here for fa s ter convergence and achieve the optimal value of the loss function?

  • Stochastic gradient de s cent
  • Momentum-based gradient descent

8. What is the update rule for the ADAM optimizer?

  • wt=wt−1−lr∗(mt/(vt−− √ +ϵ))
  • wt=wt−1−lr∗m
  • wt=wt−1−lr∗(mt/(vt+ϵ))
  • wt=wt−1−lr∗(vt/(mt+ϵ))

9. What is the advantage of using mini-batch gradient descent over batch gradient descent?

  • Mini-batch gradient descent is more computationally efficient than batch gradient descent.
  • Mini-batch gradient descent leads to a more accurate estimate of the gradient than batch gradient descent.
  • Mini batch gradient descent gives us a better s o lution.
  • Mini-batch gradient descent can converge faster than batch gradient descent.

10. Which of the following is a variant of gradient desc e nt that uses an estimate of the next gradient to update the current position of the parameters?

  • Momentum optimization
  • Stochastic gradient descent
  • Nesterov accelerated gradi e nt descent

About Deep Learning IIT – Ropar

Deep Learning has received a lot of attention over the past few years and has been employed successfully by companies like Google, Microsoft, IBM, Facebook, Twitter etc. to solve a wide range of problems in Computer Vision and Natural Language Processing. In this course, we will learn about the building blocks used in these Deep Learning based solutions. Specifically, we will learn about feedforward neural networks, convolutional neural networks, recurrent neural networks and attention mechanisms. We will also look at various optimization algorithms such as Gradient Descent, Nesterov Accelerated Gradient Descent, Adam, AdaGrad and RMSProp which are used for training such deep neural networks. At the end of this course, students would have knowledge of deep architectures used for solving various Vision and NLP tasks CRITERIA TO GET A CERTIFICATE Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course. Exam score = 75% of the proctored certification exam score out of 100 Final score = Average assignment score + Exam score YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

NPTEL Deep Learning – IIT Ropar Assignment 4 Answers 2022

1. Consider the movement on the 3D error surface for Vannila Gradient Descent Algorithm. Select all the options that are TRUE. a. Smaller the gradient, slower the movement b. Larger the gradient, faster the movement c. Gentle the slope, smaller the gradient d. Steeper the slope, smaller the gradient

2. Pick out the drawback in Vannila gradient descent algorithm. a. Very slow movement on gentle slopes b. Increased oscillations before converging c. escapes minima because of long strides d. Very slow movement on steep slopes

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NPTEL Deep Learning - IIT Ropar Assignment 4 Answers 2023

3. Comment on the update at the t th  update in the Momentum-based Gradient Descent. a. weighted average of gradient b. Polynomial weighted average c. Exponential weighted average of gradient d. Average of recent three gradients

4. Given a horizontal slice of the error surface as shown in the figure below , if the error at the position p is 0.49 then what is the error at point q? a. 0.70 b. 0.69 c. 0.49 d. 0

5. Identify the update rule for Nesterov Accelerated Gradient Descent.

6. Select all the options that are TRUE for Line search. a. w is updated using different learning rates b. updated value of w always gives the minimum loss c. Involves minimum calculation d. Best value of Learning rate is used at every step

👇 For Week 05 Assignment Answers 👇

7. Assume you have 1,50,000 data points , Mini batch size being 25,000, one epoch implies one pass over the data, and one step means one update of the parameters, What is the number of steps in one epoch for Mini-Batch Gradient Descent? a. 1 b. 1,50,000 c. 6 d. 60

8. Which of the following learning rate methods need to tune two hyperparameters? I . step decay II. exponential decay III. 1/t decay a. I and II b. II and III c. I and III d. I, II and III

9. How can you reduce the oscillations and improve the stochastic estimates of the gradient that is estimated from one data point at a time? a. Mini-Batch b. Adam c. RMSprop d. Adagrad

10. Select all the statements that are TRUE. a. RMSprop is very aggressive when decaying the learning rate b. Adagrad decays the learning rate in proportion to the update history c. In Adagrad, frequent parameters will receive very large updates because of the decayed learning rate d. RMSprop has overcome the problem of Adagrad getting stuck when close to convergence

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Ans send cheyalee…bro

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Deep Learning | NPTEL | Week 4 answers

This set of MCQ(multiple choice questions) focuses on the Deep Learning NPTEL Week 4 answers

Course layout

Answers COMING SOON! Kindly Wait!

Week 1 : Assignment Answers Week 2: Assignment Answers Week 3: Assignment Answers Week 4: Assignment Answers Week 5: Assignment Answers Week 6: Assignment Answers Week 7: Assignment Answers Week 8: Assignment Answers Week 9: Assignment Answers Week 10: Assignment Answers Week 11: Assignment Answers Week 12: Assignment Answers

NOTE:  You can check your answer immediately by clicking show answer button. This set of “ Deep Learning NPTEL Week 4 answers ” contains 10 questions.

Now, start attempting the quiz.

Deep Learning NPTEL 2023 Week 4 Quiz Solutions

Q1. Which of the following cannot be realized with single layer perception (only input and output layer)?

a) AND b) OR c) NAND d) XOR

Answer: d) XOR

Q2. For a function f(θ 0 , θ 1 ), if θ 0 and θ 1 are initialized at a local minimum, then what should be the values of θ 0 and θ 1 after a single iteration of gradient descent:

a) θ 0 and θ 1 will update as per gradient descent rule b) θ 0 and θ 1 will remain same c) Depends on the values of θ 0 and θ 1 d) Depends onf the learning rate

Q3. Choose the correct option: i) Inability of a model to obtain sufficiently low training error is termed as overfitting ii) Inability of a model to reduce large margin between training and testing error is termed as Overfitting iii) Inability of a model to obtain sufficiently low training error is termed as Underfitting iv) Inability of a model to reduce large margin between training and testing error is termed as Underfitting

a) Only option (i) is correct b) Both options (ii) and (iii) are correct c) Both options (ii) and (iv) are correct d) Only option (iv) is correct

Deep Learning NPTEL week 4 Assignment Solutions

Q4. Suupose for a cost function J(θ) = 0.25θ 2 as shown in graph below, refer to this graph and choose the correct option regarding the Statements given below θ is plotted along horizontal axis.

a) Only Statement i is true b) Only Statement ii is true c) Both statement i and ii are true d) None of them are true

Q5. Choose the correct option. Gradient of a continuous and differentiable function is: i) is zero at a minimum ii) is non-zero at a maximum iii) is zero at a saddle point iv) magnitude decreases as you get closer to the minimum

a) Only option (i) is correct b) Options (i), (iii) and (iv) are correct c) Options (i) and (iv) are correct d) Only option (iii) is correct

Q6. Input to SoftMax activation function is [3,1,2]. What will be the output?

a) [0.58, 0.11, 0.31] b) [0.43, 0.24, 0.33] c) [0.60, 0.10, 0.30] d) [0.67, 0.09, 0.24]

Q7. If SoftMax if x f is denoted as σ(x i ) where x i is the j th element of the n-dimensional vector x i.e., X = [x i ,…,x j ,…,x n ], then derivate of σ(x i ) w.r.t. x i i.e., ƍσ(x i )/ƍx i is given by,

Q8. Which of the following options is true?

a) In Stochastic Gradient Descent, a small batch of sample is selected rawndomly instead of the whole data set for each iteration. Too large update of weight values leading to faster convergence. b) In Stochastic Gradient Descent, the whole data set is processed together for update in each iteration. c) Stochastic Gradient Descent considers only one sample for updates and has noiser updates. d) Stochastic Gradient Descent is a non-iterative process

Q9. What are the steps for using a gradient descent algorithm? 1. Calculate error between teh actual value and the predicted value 2. Re-iterate until you find best weights of network 3. Pass an input through the network and get values from output layer 4. Initialize random weight and bias 5. Go to each neurons which contributes to the error and change its respective values to reduce the error

a) 1, 2, 3, 4, 5 b) 5, 4, 3, 2, 1 c) 3, 2, 1, 5, 4 d) 4, 3, 1, 5, 2

Q10. J(θ) = 2θ 2 – 2θ + 2 is a given cost function? Find the correct weight update rule for gradient descent optimixation at step t+1? Consider α=0.01 to be the learning rate

a) θ t+1 = θ t – 0.01(2θ – 1) b) θ t+1 = θ t + 0.01(2θ – 1) c) θ t+1 = θ t – (2θ – 1) d) θ t+1 = θ t – 0.02(2θ – 1)

Deep Learning NPTEL 2023 Week 4 answers

deep learning assignment week 4

Q2. Which of the following activation function leads to sparse acitvation maps?

a) Sigmoid b) Tanh c) Linear d) ReLU

deep learning assignment week 4

Q4. Which logic function cannot be performed using a single-layered Neural Network?

a) AND b) OR c) XOR d) All

Q5. Which of the following options closely relate to the following graph? Green cross are the samples of Classs-A while mustard rings are samples of Class-B and the red line is the separating line between the two class.

a) High Bias b) Zero Bias c) Zero Bias and High Variance d) Zero Bians and Zero Variance

Deep Learning NPTEL Week 4 Answers

Q6. Which of the following statement is true?

a) L2 regularization lead to sparse activation maps b) L1 regularization lead to sparse activation maps c) Some of the weights are squashed to zero in L2 regularization d) L2 regularization is also known as Lasso

Q7. Which among the following options give the range for a tanh function?

a) -1 to 1 b) -1 to 0 c) 0 to 1 d) 0 to infinity

deep learning assignment week 4

Q9. When is gradient descent algorithm certain to find a global minima?

a) For convex cost plot b) For concave cost plot c) For union of 2 convex cost plot d) For union of 2 concave cost plot

Q10. Let X=[-1, 0, 3, 5] be the input of ith layer of a neural network. On this, we want to apply softmax function. What should be the output of it?

a) [0.368, 1, 20.09, 148,41] b) [0.002, 0.006, 0.118, 0.874] c) [0.3, 0.05, 0.6, 0.05] d) [0.04, 0, 0.06, 0.9]

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NPTEL Deep Learning – IIT Ropar Week 5 Assignment Answers 2024

1. Given below are the possible number of linearly independent eigenvectors for a 5×5 matrix. Choose the incorrect option.

NPTEL Deep Learning - IIT Ropar Week 5 Assignment Answers 2024

3. What is/are the limitations of PCA?

  • It is less interpretable than neural networks
  • It can only identify linear relationships in the data.
  • It can be sensitive to outliers in the data
  • It can only reduce the dimensionality of a dataset by a fixed amount.

NPTEL Deep Learning - IIT Ropar Week 5 Assignment Answers 2024

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Programming assignments from all courses in the Coursera Natural Language Processing Specialization offered by deeplearning.ai.

amanchadha/coursera-natural-language-processing-specialization

Folders and files, repository files navigation, natural language processing specialization on coursera (offered by deeplearning.ai).

Programming assignments from all courses in the Coursera Natural Language Processing Specialization offered by deeplearning.ai .

This repo contains my work for this specialization. The code base, quiz questions and diagrams are taken from the Natural Language Processing Specialization , unless specified otherwise.

The Natural Language Processing Specialization on Coursera contains four courses:

Course 1: Natural Language Processing with Classification and Vector Spaces

Course 2: natural language processing with probabilistic models, course 3: natural language processing with sequence models, course 4: natural language processing with attention models, specialization info.

Natural Language Processing (NLP) uses algorithms to understand and manipulate human language. This technology is one of the most broadly applied areas of machine learning. As AI continues to expand, so will the demand for professionals skilled at building models that analyze speech and language, uncover contextual patterns, and produce insights from text and audio.

By the end of this specialization, you will be ready to design NLP applications that perform question-answering and sentiment analysis, create tools to translate languages and summarize text, and even build chatbots. These and other NLP applications are going to be at the forefront of the coming transformation to an AI-powered future.

This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.

Topics Covered

This Specialization will equip you with the state-of-the-art deep learning techniques needed to build cutting-edge NLP systems:

Use logistic regression, naïve Bayes, and word vectors to implement sentiment analysis, complete analogies, and translate words, and use locality sensitive hashing for approximate nearest neighbors.

Use dynamic programming, hidden Markov models, and word embeddings to autocorrect misspelled words, autocomplete partial sentences, and identify part-of-speech tags for words.

Use dense and recurrent neural networks, LSTMs, GRUs, and Siamese networks in TensorFlow and Trax to perform advanced sentiment analysis, text generation, named entity recognition, and to identify duplicate questions.

Use encoder-decoder, causal, and self-attention to perform advanced machine translation of complete sentences, text summarization, question-answering and to build chatbots. Models covered include T5, BERT, transformer, reformer, and more! Enjoy!

Programming Assignments

  • Sentiment Analysis with Logistic Regression
  • Natural language Preprocessing
  • Visualizing word frequencies
  • Visualizing tweets and Logistic Regression models
  • Naive Bayes
  • Visualizing likelihoods and confidence ellipses
  • Word Embeddings: Hello Vectors
  • Linear algebra in Python with Numpy
  • Manipulating word embeddings
  • Another explanation about PCA
  • Word Translation
  • Rotation matrices in R2
  • Hash tables
  • Autocorrect
  • Building the vocabulary
  • Candidates from edits
  • Part of Speech Tagging
  • Working with text data
  • Working with tags and NumPy
  • Autocomplete
  • Corpus preprocessing for N-grams
  • Building the language model
  • Language model generalization
  • Word Embeddings
  • Data Preparation
  • Intro to CBOW model
  • Training the CBOW model
  • Word Embeddings Step by Step
  • Sentiment with Deep Neural Networks
  • Introduction to Trax
  • Classes and Subclasses
  • Data Generators
  • Deep N-grams
  • Hidden State Activation
  • Working with JAX NumPy and Calculating Perplexity
  • Vanilla RNNs, GRUs and the scan function
  • Creating a GRU model using Trax
  • Named Entity Recognition (NER)
  • Vanishing Gradients
  • Question duplicates
  • Creating a Siamese Model using Trax
  • Modified Triplet Loss
  • Evaluate a Siamese Model
  • NMT with Attention
  • Stack Semantics
  • Transformer Summarizer
  • The Transformer Decoder
  • Question Answering
  • SentencePiece and BPE
  • Reformer LSH

I recognize the hard time people spend on building intuition, understanding new concepts and debugging assignments. The solutions uploaded here are only for reference . They are meant to unblock you if you get stuck somewhere. Please do not copy any part of the code as-is (the programming assignments are fairly easy if you read the instructions carefully). Similarly, try out the quizzes yourself before you refer to the quiz solutions.

Contributors 3

@amanchadha

  • Jupyter Notebook 98.4%
  • Python 1.6%

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  1. GitHub

    Deep Learning Specialisation. Instructor: Andrew Ng. ... Week 4 - Programming Assignment 3 - Building your Deep Neural Network: Step by Step; Week 4 - Programming Assignment 4 - Deep Neural Network for Image Classification: Application; Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization.

  2. amanchadha/coursera-deep-learning-specialization

    Course 1: Neural Networks and Deep Learning. Week 2 - PA 1 - Python Basics with Numpy; Week 2 - PA 2 - Logistic Regression with a Neural Network mindset; Week 3 - PA 3 - Planar data classification with one hidden layer; Week 4 - PA 4 - Building your Deep Neural Network: Step by Step; Week 4 - PA 5 - Deep Neural Network for Image Classification ...

  3. haocai1992/Deep-Learning-Specialization

    Course 1: Neural Networks and Deep Learning. Week 1: Introduction to Deep Learning - notes, quizzes and assignments; Week 2: Neural Networks Basics ... My quiz and assignment solutions are for reference only. Please do not copy any part of the code/answer directly. About. Course notes, quizzes, and programming assignments for DeepLearning.AI's ...

  4. Coursera: Neural Networks and Deep Learning (Week 4A) [Assignment

    Welcome to your week 4 assignment (part 1 of 2)! You have previously trained a 2-layer Neural Network (with a single hidden layer). This week, you will build a deep neural network, with as many layers as you want! In this notebook, you will implement all the functions required to build a deep neural network. In the next assignment, you will use ...

  5. Deep Learning

    The availability of huge volume of Image and Video data over the internet has made the problem of data analysis and interpretation a really challenging task....

  6. Neural Networks and Deep Learning

    Week 4: Deep Neural Network. Forward & Backward propagation. Forward & Backward propagation. Forward propagation. Backward propagation. Summary. Parameters vs Hyperparameters. Neural Network ...

  7. NPTEL Week 4 Assignment: Machine Learning And Deep Learning ...

    Embark on an illuminating journey into the fundamentals and applications of Machine Learning and Deep Learning with our comprehensive guide to NPTEL's Week 4...

  8. Coursera: Neural Networks and Deep Learning (Week 4B) [Assignment

    Coursera: Neural Networks and Deep Learning (Week 4B) [Assignment Solution] - deeplearning.ai. Deep Neural Network for Image Classification: Application. 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.

  9. Convolutional Neural Networks

    Assignment of Week 3. Quiz 3: Detection algorithms; Programming Assignment: Car detection with YOLO; Week 4: Special applications: Face recognition & Neural style transfer. Discover how CNNs can be applied to multiple fields, including art generation and face recognition. Implement your own algorithm to generate art and recognize faces ...

  10. Deep Learning

    Deep Learning NPTEL 2024 || WEEK 4 ASSIGNMENT SOLUTION | |Your Queries : deep learningdeep learning nptel 2023deep learning - iit ropar week 11deep learning ...

  11. muhac/coursera-deep-learning-solutions

    Week 4 - Deep Neural Networks. Key concepts on Deep Neural Networks; Course B - Improving Deep Neural Networks Week 1 - Practical Aspects of Deep Learning. Practical aspects of deep learning; Week 2 - Optimization Algorithms. Optimization algorithms; Week 3 - Hyperparameter Tuning, Batch Normalization and Programming Frameworks. Hyperparameter ...

  12. PDF noc20 cs88 assignment Week 4

    Quiz : Weekly Quiz 4 Deep Learning for Computer Vision: Week 4 Feedback Form Programming assignment solutions Week 5 Week 6 Week 7 Week 8 Week 9 Week 10 Week 11 Week 12 Download Videos Live Session Text Transcripts Weekly Quiz 4 The due date for submitting this assignment has passed

  13. Coursera-Deep-Learning-Specialization-Assignments-Solutions ...

    Week 4 - Building your Deep Neural Network: Step by Step; Week 4 - Deep Neural Network for Image Classification: Application; Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization. Objectives: Understand industry best-practices for building deep learning applications.

  14. Deep Learning- IIT Roper week 4 assignment solution

    Deep Learning- IIT Ropar week-4 assignment solution#DeepLearning#week4#assignment#iitropar.

  15. Assignment 4 2022.pdf

    View Assignment_4_2022.pdf from COMPUTER S 251 at Pes College Of Engineering. NPTEL Online Certification Courses Indian Institute of Technology Kharagpur Deep Learning Assignment- Week 4 TYPE OF

  16. NPTEL Deep Learning

    NPTEL Deep Learning - IIT Ropar Week 4 Assignment Answer 2023. 1. Which step does Nesterov accelerated gradient de s cent perform before finding the update size? Increase the momentum. Estimate the next position of the parameters. Adjust the learning rate. Decrease the st e p size.

  17. Deep Learning

    Week 9: Assignment Answers. Week 10: Assignment Answers. Week 11: Assignment Answers. Week 12: Assignment Answers. NOTE: You can check your answer immediately by clicking show answer button. This set of "Deep Learning NPTEL Week 4 answers " contains 10 questions. Now, start attempting the quiz.

  18. Deep Learning Specialization on Coursera

    Programming Assignments. Course 1: Neural Networks and Deep Learning. Week 2 - PA 1 - Logistic Regression with a Neural Network mindset. Week 3 - PA 2 - Planar data classification with one hidden layer. Week 4 - PA 3 - Building your Deep Neural Network: Step by Step¶. Week 4 - PA 4 - Deep Neural Network for Image Classification: Application.

  19. Deep Learning-IIT Ropar Week 4 : Assignment 4 Answers || July-2023

    Deep Learning-IIT Ropar Week 4 : Assignment 4 Answers || July-2023 || NPTEL1. https://youtu.be/lS4LzUzvbzA2. Join telegram Channel -- https://t.me/doubttown...

  20. NPTEL Deep Learning

    NPTEL Deep Learning - IIT Ropar Week 5 Assignment Answers 2024. 22 February 2024 by BEU Bihar News. Join Our WhatsApp Group Join Now . Join Us On Telegram Join Now . NPTEL Deep Learning - IIT Ropar Week 5 Assignment Answers 2024. 1. Given below are the possible number of linearly independent eigenvectors for a 5×5 matrix. Choose the ...

  21. amanchadha/coursera-natural-language-processing-specialization

    This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and ...

  22. GDC 2024

    After State of Unreal, stick around at the Blue Shield of California Theater at YBCA or on our livestreams for deep dives into the latest technology, including a peek at what's coming to the animation toolset in Unreal Engine 5.4.

  23. Deep Learning -IIT Ropar Week 4 Assignment Answers ||Jan 2024|| NPTEL

    Deep Learning -IIT Ropar Week 4 Assignment Answers ||Jan 2024|| NPTEL1. Join telegram Channel -- https://t.me/doubttown 🚀 Welcome to Doubt Town, your one-st...

  24. Deep Learning

    Deep Learning - IIT Rorar - NPTEL 2024 || WEEK 4 ASSIGNMENT SOLUTION | |Your Queries : deep learningnptel 20242023 deep learning answersdeep learning answers...

  25. Deep Learning

    This video is for providing Quiz on Deep LearningThis video is for Education PurposeThis Course is provided by NPTEL - Online courses This video is made ...