programming assignments of deep learning specialization

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:

• Office Hours

CS230 Deep Learning

Deep Learning is one of the most highly sought after skills in AI. In this course, 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 learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.

Instructors

Time and Location

Wednesday 9:30AM-11:20AM Zoom

Course Information

• This quarter (2023 Spring), CS230 meets for virtual in-class lecture Wednesday 9:30AM-11:20AM on Zoom.
• All class communication happens on the CS230 Ed forum . For private matters, please make a private note visible only to the course instructors. For longer discussions with TAs and to get help in person, we strongly encourage you to come to office hours.
• The course content and deadlines for all assignments are listed in our syllabus .
• For general inquiries, please contact [email protected] .
• Please DO NOT reach out to the instructors’ emails or individual teaching staff’s emails. Instead, please contact the teaching staff at [email protected] for the fastest response. Because of the size of the course, emails tend to get lost when reaching out to individuals in the teaching team. General inquiries to the mailing list ( [email protected] ) will help us get back to you in a timely manner.
• If you are interested in auditing the course, fill out this form .

Course Staff

Course Assistants

All course announcements take place through the CS230 Ed forum . Please make sure to join!

Class components

CS230 has the following components:

• In class (virtual) lecture - once a week (hosted on Zoom). You can access lectures by going to the “Zoom” tab of Canvas.
• Video lectures, programming assignments, and quizzes on Coursera
• A midterm covering material from the first half of the quarter
• The final project
• Weekly TA-led sections

The flipped classroom format

CS230 follows a flipped-classroom format, every week you will have:

• Virtual lectures on Wednesday 9:30AM-11:20AM: these lectures will be a mix of advanced lectures on a specific subject that hasn’t been treated in depth in the videos or guest lectures from industry experts. You can access these lectures on the Zoom tab on Canvas , and they will also be posted afterwards on Canvas .
• Two modules from the deeplearning.ai Deep Learning Specialization on Coursera. You will watch videos at home, solve quizzes and programming assignments hosted on online notebooks.
• TA-led sections on Fridays: Teaching Assistants will teach you hands-on tips and tricks to succeed in your projects, but also theorethical foundations of deep learning.
• Project meeting with your TA mentor: CS230 is a project-based class. Through personalized guidance, TAs will help you succeed in implementing a successful deep learning project within a quarter.

One module of the deeplearning.ai Deep Learning Specialization on Coursera includes:

• Lecture videos which are organized in “weeks”. You will have to watch around 10 videos (more or less 10min each) every week.
• Quizzes (≈10-30min to complete) at the end of every week to assess your understanding of the material.
• Programming assignments (≈2h per week to complete). The programming assignments will usually lead you to build concrete algorithms, you will get to see your own result after you’ve completed all the code. It’s gonna be fun! For both assignment and quizzes, follow the deadlines on the Syllabus page, not on Coursera.

Prerequisites

Students are expected to have the following background, and are invited to take the Workera technical assessments prior to the class to self-assess themselves prior to taking the class:

• Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. This corresponds to a Developing level (or badge) in the “Algorithmic Coding” section on Workera .
• Familiarity with the probability theory (CS 109 or STATS 116), which students can assess by taking the “Data Science” section on Workera .
• Familiarity with linear algebra (MATH 51), which students can assess by taking the “Mathematics” section on Workera .

Here’s more information about the class grade:

Below is the breakdown of the class grade:

• 40%: Final project (broken into proposal, milestone, final report and final video)
• 25%: Midterm
• 25%: Programming assignment
• 8%: Quizzes
• 2%: Meeting Attendance

Note: For project meetings, every group must meet 3 times throughout the quarter:

• Before the project proposal deadline to discuss and validate the project idea. This can be with any TA.
• Before the milestone deadline, with your assigned project TA .
• Before the final report deadline, again with your assigned project TA.

Every student is allowed to and encouraged to meet more with the TAs, but only the 3 meetings above count towards the final participation grade.

Submitting Assignments

From the Coursera sessions (accessible from the invite you receive by email), you will be able to watch videos, solve quizzes and complete programming assignments. Each quiz and programming assignment can be submitted directly from the session and will be graded by our autograders.

You will submit your project deliverables on Gradescope . You should be added to Gradescope automatically by the end of the first week. If you are not added by the first week of the course, please make a private post on Ed.

Late assignments

Each student will have a total of ten free late (calendar) days to use for programming assignments, quizzes, project proposal and project milestone. Each late day is bound to only one assignment and is per student.

For example , if one quiz and one programming assignment are submitted 3 hours after the deadline, this results in 2 late days being used.

For example , if a group submitted their project proposal 23 hours after the deadline, this results in 1 late day being used per student.

Once these late days are exhausted, any assignments turned in late will be penalized 20% per late day. However, no assignment will be accepted more than three days after its due date, and late days cannot be used for the final project and final presentation. Each 24 hours or part thereof that a homework is late uses up one full late day. Also, note that if you submit an assignment multiple times, only the last one will be taken into account, in which case the number of late days will be calculated based on the last submission.

Students with Documented Disabilities

Students who may need an academic accommodation based on the impact of a disability must initiate the request with the Office of Accessible Education (OAE). Professional staff will evaluate the request with required documentation, recommend reasonable accommodations, and prepare an Accommodation Letter for faculty. Unless the student has a temporary disability, Accommodation letters are issued for the entire academic year. Students should contact the OAE as soon as possible since timely notice is needed to coordinate accommodations. The OAE is located at 563 Salvatierra Walk (phone: 723-1066).

We strongly encourage students to form study groups. Students may discuss and work on programming assignments and quizzes in groups. However, each student must write down the solutions independently, and without referring to written notes from the joint session. In other words, each student must understand the solution well enough in order to reconstruct it by him/herself. In addition, each student should submit his/her own code and mention anyone he/she collaborated with. It is also an honor code violation to copy, refer to, or look at written or code solutions from a previous year, including but not limited to: official solutions from a previous year, solutions posted online, and solutions you or someone else may have written up in a previous year. Furthermore, it is an honor code violation to post your assignment solutions online, such as on a public git repo.

The Stanford Honor Code

The Stanford Honor Code as it pertains to CS courses

Looking to grow your skills and build a career in AI?

Join 1 million+ learners and #BeADeepLearner with the Deep Learning Specialization , a foundational online program by machine learning pioneer Andrew Ng.

You might be wondering if this is the right program for you, worried that you don’t have the time, or afraid that you won’t be able to keep up?

We understand that it can be daunting to start something new.

The Deep Learning Specialization

• Has clear, concise modules that allow for self-paced learning.
• Introduces practical techniques to help you get started on your AI projects and develop an industry portfolio.
• Has a 1 million-strong learner community that will support and guide you.
• Breaks down foundational concepts into easy-to-understand lectures and engaging assignments.
• Is up-to-date with the leading-edge in machine learning.
• Is rated 4.9 out of 5 by 120K+ learners and is among the most popular data science programs on Coursera

What Learners Are Saying

“After completing the Deep Learning Specialization, I got two promotions and an award and was able to work with the R&D team at work. I also got the opportunity to teach undergrad engineering students. These experiences, starting with DLS, have molded my career.” Sharob Sinha Learning Technologist, DeepLearning.AI

“I decided to try to understand this thing called AI that everyone was talking about and ended up doing the Deep Learning Specialization. I truly believe that this program should be given to senior students at universities as they’d get a valuable picture.” Jose Ramirez Assistant Professor, Yachay Tech

“The Deep Learning Specialization helped me build the fundamental knowledge as well as practical applications of deep learning. I think the Deep Learning Specialization is a great starting point if someone wants to get into the field.” Wonjin Kim Researcher

“The introductions to Convolutional Neural Networks, Yolo, NLP, among others, really helped me hit the ground running when I got on the job market. As I developed more experience, I transitioned from being a multi-project consultant to being the lead scientist of a startup.” Ludovic Alarie-Vezina Lead Data Scientist, Charm.io

“When my role as a software engineer at a big company started feeling claustrophobic, I quit without having another job in hand and enrolled in the Deep Learning Specialization. This fueled my knowledge appetite, and today, I work as a Machine Learning Engineer at Carted.” Sayak Pal Machine Learning Engineer, Carted

“After the Deep Learning Specialization, I realized that deep learning isn’t just for those with a math background and decided to become a machine learning engineer. The knowledge I’d gained helped me transition from analytics to an AI researcher role in an NLP research lab.” Samuel Cahyawijaya Ph.D. Student, Hong Kong University of Science and Technology

“The skills I acquired after completing the Deep Learning Specialization helped me get a better job. The insights it provided into the subject matter enabled me to develop new and innovative solutions to problems at work.” Krystof Chotas Machine Learning Engineer, CVEDIA

“The Deep Learning Specialization allowed me to understand diverse approaches to solve problems and helped by providing deeper insight into the field. After completing the program, I understood foundational principles better and was able to feel much more in control.” Jora De Jong

“Within a few minutes and a couple slides, I had the feeling that I could learn any concept. I felt like a superhero after this course. I didn’t know much about deep learning before, but I felt like I gained a strong foothold afterward.” Jan Zawadzki Data Scientist at Carmeq

“The whole specialization was like a one-stop-shop for me to decode neural networks and understand the math and logic behind every variation of it. I can say neural networks are less of a black box for a lot of us after taking the course.” Kritika Jalan Data Scientist at Corecompete Pvt. Ltd.

“During my Amazon interview, I was able to describe, in detail, how a prediction model works, how to select the data, how to train the model, and the use cases in which this model could add value to the customer.” Chris Morrow Sr. Product Manager at Amazon

Don’t Let the Machine Learning Revolution Pass You By

#BeADeepLearner with the Deep Learning Specialization.

Instructors

Kian Katanforoosh

Younes Bensouda Mourri

• > 2-6 months
• Intermediate

Skills you will gain

• Artificial Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• Transformers
• Python Programming
• Deep Learning
• Backpropagation
• Optimization
• Hyperparameter Tuning
• Machine Learning
• Transfer Learning
• Multi-Task Learning
• Object Detection and Segmentation
• Facial Recognition System
• Gated Recurrent Unit (GRU)
• Long Short Term Memory (LSTM)
• Attention Models
• Natural Language Processing

In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning.

By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications.

Week 1: Introduction to Deep Learning

Understand the significant technological trends driving deep learning development and where and how it’s applied.

Week 2: Neural Networks Basics

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

Week 3: Shallow Neural Networks

Build a neural network with one hidden layer using forward propagation and backpropagation.

Week 4: Deep Neural Networks

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

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically.

By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply various optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop, and Adam, and check for their convergence; and implement a neural network in TensorFlow.

Week 1: Practical Aspects of Deep Learning

Discover and experiment with various initialization methods, apply L2 regularization and dropout to avoid model overfitting, and use gradient checking to identify errors in a fraud detection model.

Week 2: Optimization Algorithms

Develop your deep learning toolbox by adding more advanced optimizations, random mini-batching, and learning rate decay scheduling to speed up your models.

Week 3: Hyperparameter tuning, Batch Normalization, and Programming Frameworks

Explore TensorFlow, a deep learning framework that allows you to build neural networks quickly and easily and train a neural network on a TensorFlow dataset.

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader.

By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning.

This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the “industry experience” that you might otherwise get only after years of ML work experience.

Week 1: ML Strategy (1)

Use a machine learning flight simulator to learn how machine learning achieves human-level performance.

Week 2: ML Strategy (2)

Become familiar with the concepts of end-to-end learning, transfer learning, and multi-task learning.

In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.

By the end, you will be able to build a convolutional neural network, including recent variations such as residual networks; apply convolutional networks to visual detection and recognition tasks; and use neural style transfer to generate art and apply these algorithms to a variety of image, video, and other 2D or 3D data.

Week 1: Foundations of Convolutional Neural Networks

Implement the foundational layers of CNNs (pooling, convolutions) and stack them properly in a deep network to solve multi-class image classification problems.

Week 2: Deep Convolutional Models: Case Studies

Discover practical techniques and methods used in research papers to apply transfer learning to your own deep CNN.

Week 3: Object Detection

Apply your knowledge of CNNs to computer vision: object detection and semantic segmentation using self-driving car datasets.

Week 4: Special Applications: Face Recognition and Neural Style Transfer

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

In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more.

By the end, you will be able to build and train Recurrent Neural Networks (RNNs) and commonly-used variants such as GRUs and LSTMs; apply RNNs to Character-level Language Modeling; gain experience with natural language processing and Word Embeddings; and use HuggingFace tokenizers and transformer models to solve different NLP tasks such as NER and Question Answering.

Week 1: Recurrent Neural Networks

Discover recurrent neural networks (RNNs) and several of their variants, including LSTMs, GRUs and Bidirectional RNNs, all models that perform exceptionally well on temporal data.

Week 2: Natural Language Processing and Word Embeddings

Use word vector representations and embedding layers to train recurrent neural networks with an outstanding performance across a wide variety of applications, including sentiment analysis, named entity recognition, and neural machine translation.

Week 3: Sequence Models and the Attention Mechanism

Augment your sequence models using an attention mechanism, an algorithm that helps your model decide where to focus its attention given a sequence of inputs, explore speech recognition and how to deal with audio data, and improve your sequence models with the attention mechanism.

Week 4: Transformers

Build the transformer architecture and tackle natural language processing (NLP) tasks such as attention models, named entity recognition (NER) and Question Answering (QA).

Course 1 : Neural Networks and Deep Learning

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

Course 3 : Structuring Machine Learning Projects

Course 4 : Convolutional Neural Networks

Course 5 : Sequence Models

Course slides.

You can download the annotated version of the course slides below.

Frequently Asked Questions

Review: A Look into Coursera’s Deep Learning Specialization

Deciding if Coursera’s Deep Learning Specialization is the right choice for your AI aspirations? In this deep learning specialization Coursera review, we cut through the noise to evaluate how Andrew Ng’s program stacks up in terms of content, practicality, and career advancement. Without any fluff, we delve into the experiences of over 120,000 learners to provide you with the insights needed to assess the specialization’s true value to your learning and career journey.

Key Takeaways

The Coursera Deep Learning Specialization, led by AI authority Andrew Ng, has a standout 4.9 rating and is tailored for those seeking a comprehensive understanding of deep learning, such as aspiring data scientists and machine learning enthusiasts.

Structured as a five-course journey, the specialization offers hands-on experience with practical assignments using TensorFlow and Keras; it’s designed to progress learners from basic to advanced deep learning concepts, offering both theoretical and practical machine learning skills.

This specialization emphasizes applied learning, preparing students for real-world AI challenges and industry careers; a significant number of completers report starting new careers or gaining promotion, underscoring its effectiveness and high return on investment.

Unveiling the Deep Learning Specialization

If you’re familiar with the field of AI, you’ve likely heard of Andrew Ng. He’s not just the co-founder of Coursera, but also a highly esteemed figure in AI and machine learning. He brings his wealth of knowledge and experience to the Deep Learning Specialization, a collaborative effort by deeplearning.ai designed to disseminate AI knowledge to a global audience.

This course isn’t just a blip on the radar; it’s a supernova illuminating the data science universe on Coursera. With a stellar rating of 4.9 out of 5, based on the reviews of over 120,000 learners, this specialization has firmly established itself as one of the most popular data science offerings on the platform.

You might ask, what are the benefits of investing your time and energy in this course? Well, the course’s unique value proposition stems significantly from Andrew Ng’s reputation and the comprehensive knowledge in deep learning it promises to deliver. Get ready to delve deeper, as we uncover the many layers of this course.

Introductory ⭐

• Harvard University Data Science: Learn R Basics for Data Science
• Standford University Data Science: Introduction to Machine Learning
• UC Davis Data Science: Learn SQL Basics for Data Science
• IBM Data Science: Professional Certificate in Data Science
• IBM Data Analysis: Professional Certificate in Data Analytics
• Google Data Analysis: Professional Certificate in Data Analytics
• IBM Data Science: Professional Certificate in Python Data Science
• IBM Data Engineering Fundamentals: Python Basics for Data Science

Intermediate ⭐⭐⭐

• Harvard University Learning Python for Data Science: Introduction to Data Science with Python
• Harvard University Computer Science Courses: Using Python for Research
• IBM Python Data Science: Visualizing Data with Python
• DeepLearning.AI Data Science and Machine Learning: Deep Learning Specialization

Advanced ⭐⭐⭐⭐⭐

• UC San Diego Data Science: Python for Data Science
• UC San Diego Data Science: Probability and Statistics in Data Science using Python
• Google Data Analysis: Professional Certificate in Advanced Data Analytics
• MIT Statistics and Data Science: Machine Learning with Python - from Linear Models to Deep Learning
• MIT Statistics and Data Science: MicroMasters® Program in Statistics and Data Science

🔎  Find Data Science Programs 👨‍💻 111,889 already enrolled

Disclaimer: Data Science Parichay is reader supported. When you purchase a course through a link on this site, we may earn a small commission at no additional cost to you. Earned commissions help support this website and its team of writers.

Target Audience Analysis

Who is the ideal learner for this deep learning treasure trove? The course is specifically designed for:

Individuals aiming to gain a comprehensive understanding and in-depth knowledge of deep learning

Aspiring data scientists

Machine learning enthusiasts looking to grow their expertise

If you fall into any of these categories, you are right in the sweet spot of the course’s target audience.

Busy professionals can also benefit from this course. The specialization is tailored for individuals who prefer flexible learning schedules, making it suitable for those who need to learn at their own pace.

To reap the full advantages of this course, having a foundational understanding in the following areas would be beneficial:

Basic machine learning knowledge, including general principles and deep learning

Probability and statistics

So, if you’ve ticked all these boxes, you’re all set to embark on this deep learning journey!

Getting Started with the Specialization

Eager to start? Simply visit the course page on Coursera and click the ‘Enroll’ button to begin the Deep Learning Specialization. While there are no strict prerequisites, a general understanding of software and computer systems will definitely give you a head start.

Coursera stands out for its user-friendly interface that accommodates various learning paces. You can adjust your learning schedule as needed, allowing you to learn at your own pace without feeling overwhelmed. So, buckle up for an exciting deep learning adventure!

Course Structure Breakdown

The Deep Learning Specialization is no ordinary course. It’s a meticulously designed program composed of 5 courses aimed at taking students from intermediate to advanced knowledge in machine learning and neural networks. The journey starts with the foundational knowledge in artificial neural networks in the first course, ‘Neural Networks and Deep Learning’ and gradually progresses through the following courses, including the highly sought-after “Convolutional Neural Networks Course”:

‘Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization’

‘Structuring Machine Learning Projects’

‘Convolutional Neural Networks,’ which covers the essentials of convolutional neural network architecture

‘Sequence Models,’ which covers NLP and sequence data handling.

The course extends beyond mere theoretical knowledge. Through the specialization, learners are immersed in deep learning concepts including:

Visual detection

Art generation with neural style transfer

Face recognition

Object detection and segmentation

Applications to word embeddings and attention models

This learning, which includes how to train recurrent neural networks, is solidified by practical assignments and use of tools such as Tensorflow and Keras.

The hallmark of this foundational online program is its systematic progression from introductory concepts to complex topics, demonstrating a well-structured learning path. So, whether you’re a novice or a seasoned professional, this course has something for everyone.

Learning Materials and Tools

The Deep Learning Specialization offers a variety of learning materials, designed to reinforce learned concepts and provide real-life applications. It includes:

Video lectures

Practical assignments

This combination ensures that you’re not just passively absorbing information, but actively applying what you learn.

The course isn’t afraid of practical, hands-on work. Learners gain hands-on experience with important machine learning tools like TensorFlow and Keras, which are widely used in professional deep learning projects. As a machine learning project leader, you will find the programming assignments enjoyable and instructive, providing practical experience with TensorFlow and Keras across various deep learning models such as binary classification and image recognition. This machine learning system knowledge will be invaluable in your future successful machine learning project.

Moreover, the course content is up-to-date with the latest advancements in machine learning techniques and practices, ensuring that learners are equipped with current knowledge in the field. This means you’ll be learning the most cutting-edge techniques and tools, making you a valuable asset in the evolving world of AI.

Course Content Quality

The course content of the Deep Learning Specialization is revered for its high-quality. It effectively breaks down foundational deep learning concepts into clear and understandable lectures, coupled with engaging assignments to reinforce learning. The specialization is renowned for offering not only theoretical knowledge but also practical insights into critical areas like machine learning strategy and optimization techniques.

However, no course is without its shortcomings. Despite its comprehensive coverage, the course has identifiable gaps, particularly in the hands-on practice of data preprocessing in its assignments, which is an essential skill in deep learning applications.

However, these minor shortcomings do not detract from the overall value of the course. The course’s high-quality lectures, engaging assignments, and practical insights make it a gem in the world of AI education.

Learning Experience

The Deep Learning Specialization recognizes that learning involves more than just information absorption. With a community of over 1 million learners, the program offers robust support and opportunities for interaction with peers. The teaching style of Andrew Ng, recognized for simplifying complex concepts and bolstering engagement through the use of real-world examples, enhances the learning experience.

The course also offers a user-friendly learning environment with familiar tools such as Python Jupyter notebooks. Plus, exposure to machine learning tools like Tensorflow and Keras is highly valued by professionals in the field.

However, there is potential for further enhancement in the course. While students appreciate the practical assignments for reinforcing concepts, there is a desire for more challenging tasks to deepen learning for advanced users. Furthermore, there is a need for more in-depth content and practical exercises, particularly in TensorFlow.

Despite these critiques, the course maintains a high overall rating, speaking volumes about its effectiveness.

Applied Learning

The Deep Learning Specialization emphasizes not just learning but application. The course emphasizes practical applications and equips learners with techniques to start AI projects and develop a portfolio relevant to the industry. Students gain practical experience in building and training a variety of neural network types, including recurrent neural networks, CNNs, and deep neural networks, for applications ranging from language modeling and natural language processing to visual recognition and neural style transfer. One of the key skills acquired in this course is the ability to train deep neural networks effectively.

The course prepares students to tackle real-world deep learning problems by teaching them about:

Pre-training models on large datasets

Handling unbalanced label distributions

Implementing practical strategies like cascading models and efficient inference.

And the proof is in the pudding. A whopping 41% of Specialization completers have started new careers in the AI industry, while 14% achieved promotions. This testifies to the course’s significant impact on their professional advancement.

Weighing the Worth

The crucial question is: Is the Deep Learning Specialization worth your investment? With ratings of 4.9 out of 5 by over 120,000 learners and enrollment of over 619,000 students, it’s safe to say that the course has been well-received.

Thanks to its comprehensive, high-quality content and potential for career growth and skill development, the Specialization is considered a valuable investment. Sure, there are cheaper or even free courses available, but they may not provide the same level of depth, structure, or recognized credentials as the Deep Learning Specialization on Coursera.

Coursera’s subscription model, while potentially a drawback for those preferring a one-time fee, offers flexibility and access to a broader range of courses and resources. So, while it’s not the cheapest option out there, the Deep Learning Specialization offers significant value for your investment.

In conclusion, the Deep Learning Specialization on Coursera is a comprehensive, high-quality, and well-structured program that equips learners with the knowledge and skills necessary to excel in the AI industry. From its engaging teaching style to its practical assignments and real-world applications, the course offers significant value for both beginners and advanced learners.

While there are areas for improvement, particularly in terms of more hands-on practice and deeper content, the course’s strengths outweigh its weaknesses. With its high ratings and positive learner feedback, the Deep Learning Specialization stands out as a valuable investment for anyone looking to dive deep into the world of AI.

Frequently Asked Questions

How much is the deep learning specialization on coursera.

The Deep Learning Specialization on Coursera costs $49 per month. This fee gives you access to all course materials, graded assignments, and a certificate upon completion.

What is deep learning specialization?

The deep learning specialization will help you understand and build neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and improve them with techniques like Dropout and BatchNorm. This will provide you with a solid foundation in deep learning.

Is deep learning AI course worth it?

Yes, a deep learning AI course is worth it because it provides an excellent resource for understanding neural networks and deep learning, explained in a way that’s easy for beginners to grasp. It’s a valuable investment in your learning.

Who is the target audience for the Deep Learning Specialization?

The Deep Learning Specialization is perfect for aspiring data scientists, machine learning enthusiasts, and professionals seeking to enhance their skills. It also caters to individuals who value flexible learning schedules.

What is the structure of the Deep Learning Specialization?

The Deep Learning Specialization consists of 5 courses that guide learners from intermediate to advanced knowledge in machine learning and neural networks. Dive in and take your understanding to the next level!

Deep-Learning-Specialization

Coursera deep learning specialization, sequence models.

This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others.

• Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
• Be able to apply sequence models to natural language problems, including text synthesis.
• Be able to apply sequence models to audio applications, including speech recognition and music synthesis.

Week 1: Sequence Models

Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section.

Assignment of Week 1

• Quiz 1: Recurrent Neural Networks
• Programming Assignment: Building a recurrent neural network - step by step
• Programming Assignment: Dinosaur Island - Character-Level Language Modeling
• Programming Assignment: Jazz improvisation with LSTM

Week 2: Natural Language Processing & Word Embeddings

Natural language processing with deep learning is an important combination. Using word vector representations and embedding layers you can train recurrent neural networks with outstanding performances in a wide variety of industries. Examples of applications are sentiment analysis, named entity recognition and machine translation.

Assignment of Week 2

• Quiz 2: Natural Language Processing & Word Embeddings
• Programming Assignment: Operations on word vectors - Debiasing
• Programming Assignment: Emojify

Week 3: Sequence models & Attention mechanism

Sequence models can be augmented using an attention mechanism. This algorithm will help your model understand where it should focus its attention given a sequence of inputs. This week, you will also learn about speech recognition and how to deal with audio data.

Assignment of Week 3

• Quiz 3: Sequence models & Attention mechanism
• Programming Assignment: Neural Machine Translation with Attention
• Programming Assignment: Trigger word detection

Course Certificate

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Gurupradeep/deeplearning.ai-Assignments

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Deep learning specialisation.

Instructor: Andrew Ng

This repository contains all the solutions of the programming assignments along with few output images. 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.

Course 1: Neural Networks and Deep Learning

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 architecture

Programming Assignments

• Week 2 - Programming Assignment 1 - Logistic Regression with a Neural Network mindset
• Week 3 - Programming Assignment 2 - Planar data classification with one hidden layer
• 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

• Understand industry best-practices for building deep learning applications.
• Be able to effectively use the common neural network "tricks", including initialization, L2 and dropout regularization, Batch normalization, gradient checking,
• Be able to implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence.
• Understand new best-practices for the deep learning era of how to set up train/dev/test sets and analyze bias/variance
• Be able to implement a neural network in TensorFlow.
• Week 1 - Programming Assignment 1 - Initialization
• Week 1 - Programming Assignment 2 - Regularization
• Week 1 - Programming Assignment 3 - Gradient Checking
• Week 2 - Programming Assignment 4 - Optimization Methods
• Week 3 - Programming Assignment 5 - TensorFlow Tutorial

Course 3: Structuring Machine Learning Projects

• Understand how to diagnose errors in a machine learning system, and
• Be able to prioritize the most promising directions for reducing error
• Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance
• Know how to apply end-to-end learning, transfer learning, and multi-task learning

This course doesn't have any programming assignments

Course 4: Convolutional Neural Networks

• 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 - Programming Assignment 1 - Convolution model Step by Step
• Week 1 - Programming Assignment 2 - Convolution model Application
• Week 2 - Programming Assignment 3 - Keras Tutorial Happy House
• Week 2 - Programming Assignment 4 - Residual Networks
• Week 3 - Programming Assignment 5 - Autonomous driving application - Car Detection
• Week 4 - Programming Assignment 6 - Face Recognition for Happy House
• Week 4 - Programming Assignment 7 - Art Generation with Neural Style transfer

Course 5: Sequence Models

• Understand how to build and train Recurrent Neural Networks (RNNs), and commonly-used variants such as GRUs and LSTMs.
• Be able to apply sequence models to natural language problems, including text synthesis.
• Be able to apply sequence models to audio applications, including speech recognition and music synthesis.
• Week1 - Programming Assignment 1 - Building a Recurrent Neural Network
• Week1 - Programming Assignment 2 - Character level Dinosaur Name generation
• Week1 - Programming Assignment 3 - Music Generation
• Week2 - Programming Assignment 1 - Operations on Word vectors
• Week2 - Programming Assignment 2 - Emojify
• Week3 - Programming Assignment 1 - Neural Machine translation with attention
• Week3 - Programming Assignment 2 - Trigger word detection

IMPORTANT PAPERS

• Neural Style Transfer

Contributors 2

• Jupyter Notebook 100.0%