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This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course hope you enjoy my notes too.
This repository contains my personal notes and summaries on DeepLearning.ai specialization courses. I've enjoyed every little bit of the course. Hope you enjoy my notes too.
DeepLearning.ai contains five courses which can be taken on Coursera. The five courses titles are:
This is by far the best course series on deep learning that I've taken. Enjoy!
If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning.
In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. You will work on case studies from healthcare, autonomous driving, sign language reading, music generation, and natural language processing. You will master not only the theory, but also see how it is applied in industry. You will practice all these ideas in Python and in TensorFlow, which we will teach.
You will also hear from many top leaders in Deep Learning, who will share with you their personal stories and give you career advice.
AI is transforming multiple industries. After finishing this specialization, you will likely find creative ways to apply it to your work.
We will help you master Deep Learning, understand how to apply it, and build a career in AI.
At last I've successfully completed the specialization and earned my certificate!
As DeepLearning.ai is one of the most popular courses in the field of AI/ML/DL, there are some good reviews regarding some or whole of the specialization courses.
The list of reviews includes:
A good Facebook group that discusses the courses are here: https://www.facebook.com/groups/DeepLearningAISpecialization/.
Group description:
This group is for current, past or future students of Prof Andrew Ng's deeplearning.ai class in Coursera. The purpose is for students to get to know each other, ask questions, and share insights. However, remember the Coursera Honor Code - please do not post any solution in the forum!
Taking fast.ai courses series as it focuses more on the practical works.
Thanks to VladKha, wangzhenhui1992, jarpit96, and other contributors for helping me revising and fixing mistakes in the notes.
Mahmoud Badry @ 2018
Selected from shared topics, language and repository description—not editorial ratings.
rj425 /
This repository contains all the programming exercises in Python for the Coursera course called "Machine Learning" by Adjunct Professor Andrew Ng at Stanford University.
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This repository contains Python Implementation of certain programming assignments of Andrew Ng’s Machine Learning Course on Coursera, created by Stanford University.
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This Repository Contains code implemented by me for the Coursera Machine Learning Course taught by Profressor Andrew NG.
sebastianbirk /
This repository contains all source code and lecture notes from the Deep Learning Specialization of Andrew Ng on Coursera.
harishmuh /
This repository contains curated resources, notebooks, and exercise solutions for Machine Learning Specialization course by Stanford Univeristy and DeepLearning.AI (2026) by Prof. Andrew Ng
BrianWU-S /
Machine Learning is a wide range field, which mainly includes the following four parts: Supervised Learning, Unsupervised Learning, Weakly Supervised Learning and Reinforcement Learning. Also, deep learning is a sub-field of machine learning. Knowing what machine learning includes, understanding the mathematical principles and implementation details of these contents, and learning the specific ideas behind them are far more important than just calling python libraries to solve certain tasks. I have been playing machine learning for more than 2 years, including taking various machine learning-related courses and completing machine learning-related tasks. These courses include: Standford MachineLearning course , Coursera DeepLearning course taught by Andrew Ng; SJTU CS420 Machine Learning course and CS410 Artificial Intelligence course; CSDN machine learning course taught by Shunxiang Liu. The machine learning-related books I have read include: Professor Zhou Zhihua’s "machine learning book" (watermelon book), Ian Goodfellow's "Deep Learning" book (flower book) and Francois Chollet's "Deep Learning with Python". They are all very helpful to me. The machine learning related projects (codes and their corresponding description, reports) are also included in this repository. In short, this repository contains a lot of content, and I hope it can be helpful to you.