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Neural Networks and Deep Learning

Deep Learning,

If you want to break into cutting-edge AI, this course will help you do so. Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. 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 This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. This is the first course of the Deep Learning Specialization.

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Rating 4.8 based on 5,985 ratings
Length 5 weeks
Starts Aug 10 (6 weeks ago)
Cost $49
From via Coursera
Instructors Andrew Ng, Head Teaching Assistant - Kian Katanforoosh, Teaching Assistant - Younes Bensouda Mourri
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Algorithms Machine Learning

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What people are saying

andrew ng

Neural Networks and Deep Learning is the first course in a new deep learning specialization offered by Coursera taught by Coursera founder Andrew Ng.

I particularly enjoyed Andrew Ng's first course of the Deep Learning specialization because of its interactivity.

The course was good, Andrew NG is undoubtedly a great mind with too much knowledge, however, all the lessons were written on a white board.

I consider Andrew Ng one of the best instructors in this field.

I was a bit familiar with Python and Numpy, maybe that didn't serve me in this perspective xD Great pace of teaching and excellent delivery of materials as expected from Prof. Andrew Ng.

I just enjoyed listening these concepts .Great Job Andrew Ng and Team .

Thanks to Andrew Ng and all others who participate in this course creation.

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step by step

Builds the concepts step by step.

The problems are structured well, and take you through the implementation step by step.

Knowledge are addressed clearly step by step in well structured video.

The course is designed full of heart From the very basics, you build Neural Networks, Step by Step - One Step At a Time.

All in all completely worth a month of your time :) Great intro, which is helping to understand a lot of Neural Nets I really enjoyed Prof Ng's course, his lectures are crystal clear and comprehensive, the assignments are thoughtfully designed to help you understand the theory completely and guide you step by step practically, I have learned a lot.

I think it could involve more real world examples to help get a better understanding of applications not that simple topic excellent explained and nice step by step programming excersises The course is really good, and give me a basic and better understanding of Deep Learning.

The learner is slowly introduced to main ideas step by step and the exercises are well written.

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linear algebra

The course page states that it only requires basic Python programming knowledge, although any experience you have with machine learning, linear algebra and calculus will be helpful with gaining a deeper understanding of the material.

It is designed for beginners in deep learning who have a background in basic python and linear algebra.

Andrew makes concepts from Linear Algebra really easy to understand and apply it in the Artificial Intelligence field.

After becoming comfortable with the notations, matrices, what the dimensions are, the whole process of learning I intend to now go more deeper into the mathematics of Deep Learning as i always found Linear Algebra and calculus interesting and this course just gave me a fascinating foothold.

A bit easy for a physics PhD, but like it as I can go through all the calculus and linear algebra for a much better insight.

Professor Ng has made this course every easy to follow, even if you do not have much calculus or linear algebra background.

I also think this class was lacking the linear algebra review.

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gradient descent

It's a great course to understand basics of deep learning with a detailed walkthrough of gradient descent algorithm, forward propagation, backward propagation, cost and activation functions with logistic regression as a starting point.

I particularly liked that the topic is introduced from "first principles", i.e., including the calculation of derivatives (for gradient descent) and the implementation of back- and forward propagation functions.

Never learned gradient descent this way.

But I would add that before doing this course, it is better to do some courses or tutorials on Linear Algebra, Logistic Regression and Gradient Descent so that you have a good foundation to understand this course.

with gradient descent from scratch.

: P Such a great overview of not only the critical component of deep learning (shallow learning model, gradient descent) and technical details of how to achieve these models with python and numpy.

The 2nd week's lectures goes through each of the steps in building a Neural Network, including the explanation of a Gradient Descent, Logistic Regression, and derivatives.

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black box

This course opens the "black box" of neural networks and explains what happens inside.The second and third courses in this specialization build on this one and explain how to build efficient NN.

Got to open black box of neural network and deep learning, learnt the science behind neural network , cost calculation.

Possibly the best basic intro to Neural Networks, helped me peer inside the "black box" :) Very good course.

a deep dive in to the "black box" of AI plus my favorite programming tool python!

Great exercises give you confidence on how NN functions internally, instead of just using them as a black box.

I'm insanely curious about demystifying the "black box" so to speak.

Earlier I used to use Deep Leaning API's in Keras and Tensorflow as black box, but now I really understand the intuition behind the deep learning algorithms and understand how to use and tweak them.

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balance between

I had to google by myself to find asnwer to these questions, Lovely structured and with the right balance between experience an theory.

Best course If you have basic linear algebra skills, this course is perfet to set your brain in th "deep learning forma-mentis" Absolutely top quality and perfect balance between math and practical experience.

It's the perfect balance between theory and practice to create a baseline knowledge for the NN and Deep Learning.

Very detailed with perfect balance between theory and application.

Great learning material, good balance between theory and piratical exercises.

I, however, am doing this for learning rather than certification so it was a minor issue.Really nice videos, a clear structure and a very thoughtful balance between the complexities of math and the "get things done" possibilities that jupyter notebook and Coursera permits.

It strikes a superb balance between simplicity and depth that is rare even in in-person university courses, and much rarer still in MOOCs.

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An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Research Scientist-Machine Learning $55k

Computer Vision, Deep Learning Engineer $67k

Computer Vision & Deep Learning Engineer $67k

Machine Learning Engineer - Information Extraction $74k

Computer Vision / Machine Learning Engineer $83k

Deep Learning Research Scientist $86k

Machine Learning Engineer - Ads Prediction $87k

Deep Learning Research Engineer $88k

Research Scientist - Deep Learning $91k

Deep Learning R&D Engineer $127k

Applied Scientist, Machine Learning $130k



Sorted by most helpful reviews first

Guest says:

I had the honor of completing the very first session of this course, which is an absolutely brilliant introduction into deep learning. The instruction was clear and concise, packed full of information and new concepts I haven't seen anywhere before (not even in the original Machine Learning course). Of course, the problem with being an early adopter in these courses is that they don't have the benefit of having students' feedback to go off of. As I mentioned in the review I left on Coursera, this NN introduction could benefit from a few tweaks. For example, sometimes we'll see different terminology used between one lecture and the next, which makes it difficult to keep track of what's what. There were also some quiz questions that were confusing either because there isn't enough information or because of some ambiguity. All-in-all though the benefits far outweigh the negatives. I do recommend anyone who's serious about this course to factor in additional time if you're rusty on math and new to programming.

Guest says:

Great overview of NNs and thorough instruction/review on implementation. Looking forward to taking remainder of this Specialization. If you have taken the old Machine Learning course, know that this has quite some overlap. However, there's quite a lot of new material, too. No harm in taking both IMO since they're all the same monthly cost now that Coursera does subscriptions.

Guest says:

This should be the new definitive introduction on neural nets. Recommend this course and the second course (Improving Deep Neural Networks) to anyone who wants to learn for academic reasons. Highly suggest the entire Specialization for those who want to join industry.

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Rating 4.8 based on 5,985 ratings
Length 5 weeks
Starts Aug 10 (6 weeks ago)
Cost $49
From via Coursera
Instructors Andrew Ng, Head Teaching Assistant - Kian Katanforoosh, Teaching Assistant - Younes Bensouda Mourri
Download Videos On all desktop and mobile devices
Language English
Subjects Programming Data Science
Tags Computer Science Data Science Algorithms Machine Learning

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