deep learning week 1 assignment

Deep Learning Part 1 (IITM)

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Deep-Learning-Specialization

Coursera deep learning specialization, neural networks and deep learning.

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.

Week 1: Introduction to deep learning

Be able to explain the major trends driving the rise of deep learning, and understand where and how it is applied today.

• Quiz 1: Introduction to deep learning

Week 2: Neural Networks Basics

Learn to set up a machine learning problem with a neural network mindset. Learn to use vectorization to speed up your models.

• Quiz 2: Neural Network Basics
• Programming Assignment: Python Basics With Numpy
• Programming Assignment: Logistic Regression with a Neural Network mindset

Week 3: Shallow neural networks

Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.

• Quiz 3: Shallow Neural Networks
• Programming Assignment: Planar Data Classification with Onehidden Layer

Week 4: Deep Neural Networks

Understand the key computations underlying deep learning, use them to build and train deep neural networks, and apply it to computer vision.

• Quiz 4: Key concepts on Deep Neural Networks
• Programming Assignment: Building your Deep Neural Network Step by Step
• Programming Assignment: Deep Neural Network Application

Course Certificate

Deep Learning | Week 1

Course Name: Deep Learning

Course Link: Click Here

These are NPTEL Deep Learning Week 1 Assignment 1 Answers

Q1) From a pack of 52 cards, two cards are drawn together at random. What is the probability of both the cards being kings? a. 1/15 b. 25/57 c. 35/256 d. 1/221

Answer: d. 1/221

Q2) For a two class problem Bayes minimum error classifier follows which of following rule? (The two different classes are w₁ and w 2 , and input feature vector is x) a. Choose w₁ if P(w₁/x) > P(w 2 /x) b. Choose w₁ if P(w₁)>P(w 2 ) c. Choose w 2 if P(w₁)<P(w 2 ) d. Choose w 2 if P(w₁/x) > P(w 2 /x)

Answer: a. Choose w₁ if P(w₁/x) > P(w 2 /x)

Q3) The texture of the region provides measure of which of the following properties? a. Smoothness alone b. Coarseness alone c. Regularity alone d. Smoothness, coarseness and regularity

Answer: d. Smoothness, coarseness and regularity

Q4) Why convolution neural network is taking off quickly in recent times? (Check the options that are true.) a. Access to large amount of digitized data b. Integration of feature extraction within the training process. c. Availability of more computational power d. All of the above.

Answer: d. All of the above.

Q5) The bayes formula states : a. posterior = likelihood*prior/evidence b. posterior = likelihood*evidence/prior c. posterior = likelihood * prior d. posterior = likelihood * evidence

Answer: a. posterior = likelihood*prior/evidence

Q6) Suppose Fourier descriptor of a shape has K coefficient, and we remove last few coefficient and use only first m (m<K) number of coefficient to reconstruct the shape. What will be effect of using truncated Fourier descriptor on the reconstructed shape? a. We will get a smoothed boundary version of the shape. b. We will get only the fine details of the boundary of the shape. c. Full shape will be reconstructed without any loss of information. d. Low frequency component of the boundary will be removed from contour of the shape.

Answer: a. We will get a smoothed boundary version of the shape.

Q7) The plot of distance of the different boundary point from the centroid of the shape taken at various direction is known as a. Signature descriptor b. Polygonal descriptor c. Fourier descriptor. d. Convex Hull

Answer: a. Signature descriptor

Q8) If the larger values of gray co-occurrence matrix are concentrated around the main diagonal, then which one of the following will be true? a. The value of element difference moment will be high. b. The value of inverse element difference moment will be high. c. The value of entropy will be very low. d. None of the above.

Answer: b. The value of inverse element difference moment will be high.

Q9) Which of the following is a Co-occurrence matrix based descriptor a. Entropy b. Uniformity c. Signature d. Inverse Element difference moment. e. All of the above.

Answer: e. All of the above.

Q10) Consider two class Bayes’ Minimum Risk Classifier. Probability of classes W1 and W2 are, P (w₁) =0.3 and P (w₂) =0.7 respectively. P(x) = 0.55, P (x| w₁) = 0.75, P (x| w2) =0.45 and the loss matrix values are

Find the Risk R (α₂|x). a. 0.42 b. 0.61 c. 0.48 d. 0.39

Answer: a. 0.42

• Computer Science and Engineering
• NOC:Deep Learning (Video) 
• Co-ordinated by : IIT Kharagpur
• Available from : 2019-07-25
• Intro Video
• Lecture 01: Introduction
• Lecture 02: Feature Descriptor - I
• Lecture 03: Feature Descriptor - II
• Lecture 04: Bayesian Learning - I
• Lecture 05: Bayesian Learning - II
• Lecture 06: Discriminant Function - I
• Lecture 07: Discriminant Function - II
• Lecture 08: Discriminant Function - III
• Lecture 09: Linear Classifier
• Lecture 10: Linear Classifier - II
• Lecture 11: Support Vector Machine - I
• Lecture 12: Support Vector Machine - II
• Lecture 13: Linear Machine
• Lecture 14: Multiclass Support Vector Machine - I
• Lecture 15: Multiclass Support Vector Machine -II
• Lecture 16: Optimization
• Lecture 17: Optimization Techniques in Machine Learning
• Lecture 18: Nonlinear Functions
• Lecture 19: Introduction to Neural Network
• Lecture 20: Neural Network -II
• Lecture 21: Multilayer Perceptron
• Lecture 22: Multilayer Perceptron - II
• Lecture 23: Backpropagation Learning
• Lecture 24: Loss Function
• Lecture 25: Backpropagation Learning- Example
• Lecture 26: Backpropagation Learning- Example II
• Lecture 27: Backpropagation Learning- Example III
• Lecture 28: Autoencoder
• Lecture 29: Autoencoder Vs. PCA I
• Lecture 30: Autoencoder Vs. PCA II
• Lecture 31: Autoencoder Training
• Lecture 32: Autoencoder Variants I
• Lecture 33: Autoencoder Variants II
• Lecture 34: Convolution
• Lecture 35: Cross Correlation
• Lecture 36: CNN Architecture
• Lecture 37: MLP versus CNN, Popular CNN Architecture: LeNet
• Lecture 38: Popular CNN Architecture: AlexNet
• Lecture 39: Popular CNN Architecture: VGG16, Transfer Learning
• Lecture 40: Vanishing and Exploding Gradient
• Lecture 41 : GoogleNet
• Lecture 42 : ResNet, Optimisers: Momentum Optimiser
• Lecture 43 : Optimisers: Momentum and Nesterov Accelerated Gradient (NAG) Optimiser
• Lecture 44 : Optimisers: Adagrad Optimiser
• Lecture 45 : Optimisers: RMSProp, AdaDelta and Adam Optimiser
• Lecture 46: Normalization
• Lecture 47: Batch Normalization-I
• Lecture 48: Batch Normalization-II
• Lecture 49: Layer, Instance, Group Normalization
• Lecture 50: Training Trick, Regularization,Early Stopping
• Lecture 51 : Face Recognition
• Lecture 52 : Deconvolution Layer
• Lecture 53: Semantic Segmentation - I
• Lecture 54: Semantic Segmentation - II
• Lecture 55: Semantic Segmentation - III
• Lecture 56 : Image Denoising
• Lecture 57 : Variational Autoencoder
• Lecture 58 : Variational Autoencoder - II
• Lecture 59 : Variational Autoencoder - III
• Lecture 60 : Generative Adversarial Network
• Live Session 06-03-2020
• Week 1 - PMRF Live Session
• Week 2 - PMRF Live Session
• Week 3 - PMRF Live Session
• Week 4 - PMRF Live Session
• Watch on YouTube
• Assignments
• Download Videos
• Transcripts
• Lecture Notes (1)
• Handouts (3)

[Week 1] NPTEL Deep Learning – IIT Ropar Assignment Answers 2024

Nptel deep learning – iit ropar week 1 assignment answers 2024.

1. Which Boolean function with two inputs x1 and x2 is represented by the following decision boundary? (Points on boundary or right of the decision boundary to be classified 1)

2. Choose the correct input-output pair for the given MP Neuron.

3. Suppose we have a boolean function that takes 4 inputs x1, x2, x3, x4? We have an MP neuron with parameter θ=2. For how many inputs will this MP neuron give output y=1?

4. We are given the following data:

• None of These

6. Consider points shown in the picture. The vector w is (-1,0). As per this weight vector, the Perceptron algorithm will predict which classes for the data points x1 and x2.

7. Given an MP neuron with the inputs as x1,x2,x3,x4,x5 and threshold θ=3 where x5 is inhibitory input. For input (1,1,1,0,1) what will be the value of y?

• y=1 since θ≥3
• Insufficient information

8. An MP neuron takes two inputs x1 and x2. Its threshold is θ=0. Select all the boolean functions this MP neuron may represent.

10. What is the ”winter of AI” referring to in the history of artificial intelligence?

• The period during winter when AI technologies are least effective due to cold temperatures
• A phase marked by decreased funding and interest in AI research.
• The season when AI algorithms perform at their peak efficiency.
• A period characterized by rapid advancements and breakthroughs in AI technologies.

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[week 1-12] nptel deep learning assignment answers 2024.

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• Assignments

Spring 2024

Time and Location

Mon. & Wed. 12:30 PM - 1:20 PM Pacific Time Jordan Hall room 040 (420-040)

Week 1: Introduction and Acoustic Phonetics

Deliverables

• Assignment 1 released on Mon 4.1.24.

Lecture 1 (Mon 4.1.24)

Course introduction.

lecture slides

Lecture 2 (Wed 4.3.24)

Phonetics: Articulatory phonetics. Acoustics. ARPAbet transcription. Readings:

• J+M Draft Edition Appendix H: Phonetics, online pdf
• Fun read (optional). The Art of Language Invention . David J Peterson. 2015.

Week 2: Speech Synthesis / Text to Speech (TTS)

Course Project Overview released on Mon 4.8.24.

Lecture 3 (Mon 4.8.24)

Some history of ASR, TTS, and dialog. TTS Overview. Text normalization. Letter-to-sound. Prosody. Readings:

• J+M Draft Edition Chapter 16.6: TTS online pdf

Lecture 4 (Wed 4.10.24)

Foundations of TTS: Data collection. Evaluation. Signal processing. Concatenative and parametric approaches. Readings:

• J+M Draft Edition Chapter 16.6: TTS (cont’d) online pdf

Week 3: Course project + TTS with deep learning

• Assignment 1 due by Monday 4.15.24 11:59PM Pacific.
• Assignment 2 released on Monday 4.15.24.

Lecture 5 (Mon 4.15.24)

Course project overview and Q&A. Social meaning extraction as supervised machine learning. Readings:

• Rajesh Ranganath, Dan Jurafsky, and Daniel A. McFarland. . Detecting friendly, flirtatious, awkward, and assertive speech in speed-dates . Computer Speech and Language. 2013.

Lecture 6 (Wed 4.17.24)

Deep learning for TTS. Readings:

• Wang, Y., Skerry-Ryan, R.J., Stanton, D., Wu, Y., Weiss, R.J., Jaitly, N., Yang, Z., Xiao, Y., Chen, Z., Bengio, S. and Le, Q., Tacotron: Towards end-to-end speech synthesis . arXiv. 2017.
• Oord, A.V.D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A. and Kavukcuoglu, K. Wavenet: A generative model for raw audio . arXiv. 2016.
• Ren, Y., Ruan, Y., Tan, X., Qin, T., Zhao, S., Zhao, Z., and Liu, T. Y. Fastspeech: Fast, robust and controllable text to speech . Advances in Neural Information Processing Systems 32. 2019.

Deep Learning Preliminaries. Review on your own as needed depending on your experience so far with deep learning models:

• J+M Draft Edition Chapter 7: Neural Networks and Neural Language Models. pdf
• J+M Draft Edition Chapter 9: RNNs and LSTMs. pdf
• J+M Draft Edition Chapter 10: Transformers and Large Language Models. pdf
• The Illustrated Transformer – Jay Alammar – Visualizing machine learning one concept at a time

Week 4: Speech to Text / Automatic Speech Recognition (ASR)

• Course Project Proposal due by Wednesday 4.24.24 11:59PM Pacific.

Lecture 7 (Mon 4.22.24)

Speech recognition overview: Noisy channel model. Word error rate metrics. Hidden Markov models (HMMs). Readings:

• J+M Draft Edition Chapter 16.1, 16.2, 16.3, 16.5: Automatic Speech Recognition online pdf
• J+M Draft Edition. Appendix A pdf
• J+M Draft Edition. Appendix B pdf
• Koenecke, A., Nam, A., Lake, E., Nudell, J., Quartey, M., Mengesha, Z., Toups, C., Rickford, J.R., Jurafsky, D. and Goel, S. Racial disparities in automated speech recognition . Proceedings of the National Academy of Sciences. 2020.
• J+M Draft Edition Chapter 3: N-gram Language Models. pdf
• Lecture videos on introductory NLP including language modeling youtube

Lecture 8 (Wed 4.24.24)

Speech recognition: HMM-DNN systems. Connectionist Temporal Classification (CTC). End-to-end neural ASR. Readings:

• Hinton, Geoffrey, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal processing magazine. 2012.
• J+M Draft Edition Chapter 16.4: CTC online pdf
• Graves, A., and Jaitly, N. Towards end-to-end speech recognition with recurrent neural networks. ICML . 2014.
• Maas, A. , Xie, Z. , Jurafsky, D., & Ng, A. Lexicon-free conversational speech recognition with neural networks. ACL-HLT . 2015. (* indicates equal contribution)
• Chan, W., Jaitly, N., Le, Q.V. and Vinyals, O. Listen, attend and spell: A neural network for large vocabulary conversational speech recognition ICASSP. 2016. arXiv preprint.
• Prabhavalkar, R., Hori, T., Sainath, T. N., Schlüter, R., and Watanabe, S. End-to-end speech recognition: A survey . IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2023.

Week 5: State-of-the-art ASR and customizing ASR for products

• Assignment 2 due by Wednesday 5.1.24 11:59PM Pacific.

Lecture 9 (Mon 4.29.24)

State-of-the-art deep learning approaches for speech recognition. Conformer. Whisper. Fine tuning base models. Readings:

• Gulati, A., Qin, J., Chiu, C.C., Parmar, N., Zhang, Y., Yu, J., Han, W., Wang, S., Zhang, Z., Wu, Y. and Pang, R. Conformer: Convolution-augmented transformer for speech recognition . arXiv. 2020. 2.TBD

Lecture 10 (Wed 5.1.24)

Guest Lecture: Ello : A case study in building spoken language products. Readings:

• T. Bluche, M. Primet, & T. Gisselbrecht. Small-Footprint Open-Vocabulary Keyword Spotting with Quantized LSTM Networks .ArXiv. 2020.
• K. Audhkhasi, A. Rosenberg, A. Sethy, B. Ramabhadran, & B. Kingsbury. End-to-End ASR-free Keyword Search from Speech . IEEE J. Signal Processing 2017.
• N. Sacchi, A. Nanchen, M. Jaggi, & M. Cerňak. Open-Vocabulary Keyword Spotting with Audio and Text Embeddings . Interspeech 2019.

Week 6: Foundation models and non-English languages

• Assignment 3 released on Mon 5.6.24.

Lecture 11 (Mon 5.6.24)

Guest Lecture: Foundation models for spoken language. Dr. Karen Livescu Readings

• Baevski, A., Zhou, Y., Mohamed, A., & Auli, M. Wav2vec 2.0: A framework for self-supervised learning of speech representations . Advances in Neural Information Processing Systems, 33. 2020.

Lecture 12 (Wed 5.8.24)

Week 7: non-english spoken language understanding cont’d + project check-ins, lecture 13 (mon 5.13.24), lecture 14 (wed 5.15.24).

Project check-ins during class. Each group will speak for ~2 minutes about progress and planned work

Week 8: Introduction to spoken dialog + project check-ins

• Assignment 3 due by Monday 5.20.24 11:59PM Pacific.

Lecture 15 (Mon 5.20.24)

Lecture 16 (wed 5.22.24).

Overview of dialog: Human conversation. Task-oriented dialog. Dialog system design. GUS and frame-based dialog systems. Readings:

• J+M Draft Edition Chapter 15: Dialogue Systems and Chatbots, online pdf

Week 9: Spoken dialog with LLMs

• Course Project Milestone due by Monday 5.27.24 11:59PM Pacific.

Memorial Day. NO CLASS (Mon 5.27.24)

Lecture 17 (wed 5.29.24).

Guest lecture: Developing spoken dialog systems with LLMs. Gridspace .

Week 10 : Spoken dialog development & final poster session

Lecture 18 (mon 6.3.24).

Case study: Alexa Skills Kit in the era of LLMs. Readings:

• Understand Custom Skills . You do not need to cover adding visual components to skills.
• Interaction Model Design

Final project poster session (Wed 6.5.24)

Present posters at in-person session during lecture time. Location TBD

Course Project Report due by Saturday 6.8.24 by 11:59 PM Pacific. No late days allowed

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Solutions of Deep Learning Specialization by Andrew Ng on Coursera

muhac/coursera-deep-learning-solutions

Folders and files, repository files navigation, solutions manual deep learning andrew ng.

Solutions manual to accompany Deep Learning Specialization on Coursera .

Programming Assignments

Course a - neural networks and deep learning, week 2 - neural networks basics.

Python Basics with numpy (optional)

Logistic Regression with a Neural Network mindset

Week 3 - Shallow Neural Networks

• Planar data classification with a hidden layer

Week 4 - Deep Neural Networks

Building your Deep Neural Network: Step by Step

Deep Neural Network - Application

Course B - Improving Deep Neural Networks

Week 1 - practical aspects of deep learning.

Initialization

Regularization

Gradient Checking

Week 2 - Optimization Algorithms

• Optimization

Week 3 - Hyperparameter Tuning, Batch Normalization and Programming Frameworks

Course d - convolutional neural networks, week 1 - foundations of convolutional neural networks.

Convolutional Model: step by step

Convolutional Model: application

Week 2 - Deep Convolutional Models: case studies

Keras Tutorial (not graded)

Residual Networks

Week 3 - Object Detection

• Car detection with YOLO

Week 4 - Special Applications: Face Recognition & Neural Style Transfer

Art generation with Neural Style Transfer

Face Recognition

Course E - Sequence Models

Week 1 - recurrent neural networks.

Building a recurrent neural network - step by step

Dinosaur Island - Character-Level Language Modeling

Jazz improvisation with LSTM

Week 2 - Natural Language Processing & Word Embeddings

Operations on word vectors - Debiasing

Week 3 - Sequence Models & Attention Mechanism

Neural Machine Translation with Attention

Trigger word detection

Practice Questions

Week 1 - introduction to deep learning.

• Introduction to deep learning
• Neural Network Basics
• Shallow Neural Networks
• Key concepts on Deep Neural Networks
• Practical aspects of deep learning
• Optimization algorithms
• Hyperparameter tuning, Batch Normalization and Programming Frameworks

Course C - Structuring Machine Learning Projects

Week 1 - ml strategy (1).

• Bird recognition in the city of Peacetopia (case study)

Week 2 - ML Strategy (2)

• Autonomous driving (case study)
• The basics of ConvNets
• Deep convolutional models
• Detection algorithms
• Special applications: Face recognition & Neural style transfer
• Recurrent Neural Networks
• Natural Language Processing & Word Embeddings
• Sequence Models & Attention Mechanism

Contributors 2

• Jupyter Notebook 43.9%
• Python 1.2%