deep learning programming assignment

Deep-Learning-Specialization-Coursera

This repo contains the updated version of all the assignments/labs (done by me) of deep learning specialization on coursera by andrew ng. it includes building various deep learning models from scratch and implementing them for object detection, facial recognition, autonomous driving, neural machine translation, trigger word detection, etc., deep learning specialization coursera [updated version 2021].

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This repo contains all of the solved assignments of Coursera’s most famous Deep Learning Specialization of 5 courses offered by deeplearning.ai

Instructor: Prof. Andrew Ng

This Specialization was updated in April 2021 to include developments in deep learning and programming frameworks. One of the most major changes was shifting from Tensorflow 1 to Tensorflow 2. Also, new materials were added. However, Most of the old online repositories still don’t have old codes. This repo contains updated versions of the assignments. Happy Learning :)

Programming Assignments

Course 1: Neural Networks and Deep Learning

• W2A1 - Logistic Regression with a Neural Network mindset
• W2A2 - Python Basics with Numpy
• W3A1 - Planar data classification with one hidden layer
• W3A1 - Building your Deep Neural Network: Step by Step¶
• W3A2 - Deep Neural Network for Image Classification: Application

Course 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

• W1A1 - Initialization
• W1A2 - Regularization
• W1A3 - Gradient Checking
• W2A1 - Optimization Methods
• W3A1 - Introduction to TensorFlow

Course 3: Structuring Machine Learning Projects

• There were no programming assignments in this course. It was completely thoeretical.
• Here is a link to the course

Course 4: Convolutional Neural Networks

• W1A1 - Convolutional Model: step by step
• W1A2 - Convolutional Model: application
• W2A1 - Residual Networks
• W2A2 - Transfer Learning with MobileNet
• W3A1 - Autonomous Driving - Car Detection
• W3A2 - Image Segmentation - U-net
• W4A1 - Face Recognition
• W4A2 - Neural Style transfer

Course 5: Sequence Models

• W1A1 - Building a Recurrent Neural Network - Step by Step
• W1A2 - Character level language model - Dinosaurus land
• W1A3 - Improvise A Jazz Solo with an LSTM Network
• W2A1 - Operations on word vectors
• W2A2 - Emojify
• W3A1 - Neural Machine Translation With Attention
• W3A2 - Trigger Word Detection
• W4A1 - Transformer Network
• W4A2 - Named Entity Recognition - Transformer Application
• W4A3 - Extractive Question Answering - Transformer Application

I’ve uploaded these solutions here, only for being used as a help by those who get stuck somewhere. It may help them to save some time. I strongly recommend everyone to not directly copy any part of the code (from here or anywhere else) while doing the assignments of this specialization. The assignments are fairly easy and one learns a great deal of things upon doing these. Thanks to the deeplearning.ai team for giving this treasure to us.

Connect with me

Name: Abdur Rahman

Institution: Indian Institute of Technology Delhi

Find me on:

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

How to access the programming assignments after completing the course?

Hello everyone,

Two months ago I completed the Deep Learning specialization. Now, I’ve been wanting to do some ML projects of my own and I wanted to look at the programming assignments I did on the course. However, they’re blocked to me. Since I’ve completed the course and am no longer subscribed I no longer have access to them. Looking back I should have just downloaded the jupyter notebooks but I didn’t know they’d be blocked in the future.

So, is there any way to access the assignments again? Do I really have to buy a new subscription just for that?

Yes, the assignments are behind the paywall. There is a topic about that on the FAQ Thread , although it’s a little late for that now.

If you want your personal copies, I don’t know any way other than subscribing for one month. Then download everything before that one month expires, so that you don’t have to keep paying. And I guess there is no guarantee that they don’t delete your files when you unsubscribe. Sorry, but the mentors don’t have to deal with the subscription process so I’m not really sure how it works. You could ask Coursera. Try clicking the small blue square ? lower right on any of the course pages to get to Coursera Help and see if you can find an answer.

This happened to me as well. In my case, I wanted to review the notebooks I have completed several months ago and even though I had a valid subscription, I could not recovered my submitted work. I guess it is deleted after a while?

It would be ideal if one could go back and access the submitted work. Recently I have been saving local copies when I complete an assignment. A less ideal but still an improvement, would be to provide an option at the end of each course to download all the submitted work.

They don’t automatically delete your work, but what probably happened in your case is that the courses were completely updated between when you finished them and when you tried access them again. There was a major rewrite of everything that got published in April 2021. No previously completed work survived that transition.

Keeping your own copies is really the only way. You don’t trust your hard-drives never to fail (right?), so why would you assume big complex websites never lose anything.

The other thing that can happen is that just a normal bug fix or update to an assignment will move any completed ones aside (preserving them with the date and time interpolated into the file name). So if you just naively open the assignment, you’ll see a clean copy, but you could find and recover your old work by clicking “File → Open” and having a look around. There is a topic about this on the DLS FAQ Thread linked in my earlier reply on this thread.

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Contains Optional Labs and Solutions of Programming Assignment for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2023) by Prof. Andrew NG

A-sad-ali/Machine-Learning-Specialization

Folders and files, repository files navigation.

• Machine Learning Specialization

Skill you'll gain:

• Classification
• Recommendation System
• Artificial Neural Network
• ... And more!!!

What will you learn?

• Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression)
• Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods
• Apply best practices for ML development & use unsupervised learning techniques for unsupervised learning including clustering & anomaly detection
• Build recommender systems with a collaborative filtering approach & a content-based deep learning method & build a deep reinforcement learning model

Applied Learning Project

By the end of this Specialization, you will be ready to:

• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn.
• Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression.
• Build and train a neural network with TensorFlow to perform multi-class classification.
• Apply best practices for machine learning development so that your models generalize to data and tasks in the real world.
• Build and use decision trees and tree ensemble methods, including random forests and boosted trees.
• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection.
• Build recommender systems with a collaborative filtering approach and a content-based deep learning method.
• Build a deep reinforcement learning model.

Outline of Machine Learning Specialization Course

Course 1 - supervised machine learning: regression and classification:.

In the first course of the specialization, you'll:

• Have a good understanding of the concepts of Supervised Learning, Unsupervised Learning, Regression, Classification, Clustering, Gradient Descent,...
• Build simple machine learning models in Python using popular machine learning libraries NumPy & scikit-learn.
• Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression.

Course 2 - Advanced Learning Algorithms:

In the second course of the specialization, you'll able to:

Course 3 - Unsupervised Learning, Recommenders, Reinforcement Learning

In the last course of the specialization, you'll be able to:

• Use unsupervised learning techniques for unsupervised learning: including clustering and anomaly detection
• Build a deep reinforcement learning model
• Build recommender systems with a collaborative filtering approach and a content-based deep learning method

Certificates

• Jupyter Notebook 94.9%
• Python 5.1%