#
Neural Networks and Deep Learning

Below are the **top discussions** from
Reddit that mention this
online Coursera course from
**DeepLearning.AI**.

Offered by DeepLearning.AI. In the first course of the Deep Learning Specialization, you will study the foundational concept of neural ... Enroll for free.

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Taught by

**Andrew Ng**

Instructor

and 2 more instructors

Offered by

**DeepLearning.AI**

#### Reddit Posts and Comments

0 posts • 29 mentions • top 18 shown below

**r/learnmachinelearning**• comment

20 points • SnakeOne

It is maybe an unpopular opinion but I think that the old Andrew Ng couse is really boring starting with the Neural Network week. So I would recommend to you is to try his 2017 Neural network course which goes much indepth and the topics are explained much better.

link: https://www.coursera.org/learn/neural-networks-deep-learning

**r/AskComputerScience**• comment

5 points • jhaluska

I think for a quick introduction, 3Blue1Brown has a great introductory video. Also I find Andrew Ng's Coursera course meets your needs.

**r/ArtificialInteligence**• comment

1 points • thewordishere

You study everything in this course and memorize it.

https://www.coursera.org/learn/neural-networks-deep-learning

Then research everything you can on Google & Youtube.

**r/learnmachinelearning**• comment

1 points • unseenwizzard

https://www.coursera.org/learn/neural-networks-deep-learning

**r/AutoHotkey**• comment

1 points • ParmenidesDuck

I wasn't at home when I wrote that. Here is the actual link. Kudos to you and good luck.

Also one last tip. Don't pay for the course until you've just about finished it. Then pay for it, do the extras like all the extra assignments. Then stop paying once you grab your cert. Its the best way.

**r/learnmachinelearning**• comment

1 points • puffybunion

I think I'm going to do the Coursera deeplearning.ai course first since it's free.

https://www.coursera.org/learn/neural-networks-deep-learning

**r/canada**• comment

2 points • questions_are_hard

Source please?

I personally coded an AI which could identify a cat photo from not a cat photo with better than 90% correct attribution by taking a FREE online course.

https://www.coursera.org/learn/neural-networks-deep-learning/home/welcome

Are goldfish just that much better?

**r/stanford**• comment

2 points • AICoderGamer

I think so for sure! Check out the DL Coursera course for yourself though (https://www.coursera.org/learn/neural-networks-deep-learning?specialization=deep-learning) and see if you follow along. If you can finish the first week, the rest should be smooth sailing!

**r/learnmachinelearning**• comment

6 points • blackHoleDetector

Check out Jeremy Howard and Rachel Thomas' fast.ai course. It focuses more on coding and is really practical.

Additionally, see this post regarding my Deep Learning Fundamentals YouTube playlist and this post for my Deep Learning with Keras playlist. (Keras is a neural network API written in Python.)

And yes, Andrew Ng's ML course and Deep Learning course on Cousera are go-to's for developing an understanding in the general ML arena.

**r/MachineLearning**• comment

1 points • earee

I think the best source I have used for the history of machine learning is from Dr Ng's course https://www.coursera.org/learn/neural-networks-deep-learning Dr Ng is a respected authority and he was a participant for some early implementations. I studied a little machine learning 30 years ago and only recently returned to the subject. Even back then there it was tantalizing to see the potential. I think its fair to say that performance is still a significant bottle neck but at least now there are a handful of real world applications.

**r/france**• comment

1 points • Belenoi

Je fais de la "data-science" dans le domaine de la vision par ordinateur depuis un peu plus de 2 ans maintenant. Avec tes connaissances en stats et en algèbre, tu as déjà une bonne partie des connaissances dont tu as besoin. Si en plus tu as fait des études sur les séries temporelles, ça peut être un gros plus.

C'est un domaine qui est très tourné vers l'applicatif, et en conséquence, il n'est pas nécessaire de comprendre comment ça fonctionne pour avoir des résultats corrects. Cependant, personnellement, je trouve que comprendre le fonctionnement te donne un vrai avantage en terme de rapidité de développement, en debugging et en performance de l'algo que tu développes. Il y a des formations en ligne très reconnues dans le milieu, par exemple, cette formation sur le Deep Learning sur coursera, qui est donné entre autre par Andrew Ng. Je ne sais pas ce que vaut la formation avec Centrale-Supélec, mais je pense que la renommée de l'école peut aider sur le CV.

Finalement, je donne juste un petit avertissement : le ML, c'est pas toujours très glamour. Tu passes beaucoup de temps à analyser tes données, comprendre les caractéristiques qui sont pertinentes, nettoyer les données, mettre en place des procédures pour pouvoir les faire annoter, discuter avec des experts du domaine pour pouvoir comprendre là où potentiellement tu as fait des erreurs dans ton appréciation des caractéristiques importantes des données, etc. **La donnée, c'est vraiment le truc le plus important du domaine.** Le côté "AI", dev d'algo de machine learning, c'est peut être 20% de ton temps ? Et comme beaucoup de techniques aujourd'hui, surtout avec le Deep Learning, sont plus ou moins des boites noires, il y a également une bonne partie du temps qui est dédié à trouver les meilleurs hyper-paramètres de l'algo. Comme disait mon prof de Reinforcment Learning : "Un dev qui fait du Deep Learning, c'est un dev qui passe 6 mois à chercher la meilleure valeur de ses hyper-paramètres".

Ah, et un dernier truc : le milieu avance très très vite, et c'est obligatoire de faire de la veille constamment. Parfois, je me sens un peu submergé par toutes les avancées.

Je me rends compte que je suis assez négatif, mais je pense que c'est pour équilibrer la vision ultra glamour qu'ont les gens extérieur au milieu. C'est assez logique quand on voit ce qu'on arrive à faire avec, mais c'est juste qu'il ne faut pas oublier que y a aussi des côtés moins marrants.

Si tu as des questions, n'hésites pas à m'envoyer un MP.

**r/deeplearning**• comment

2 points • yunusemrecatalcam

Vov, I'm on a similar situation. I'm working as an embedded software developer now and planning to being a ML developer. I think I handled the most of the math of learning. I've used Andrew Ng's courses on coursera:

https://www.coursera.org/learn/machine-learning

https://www.coursera.org/learn/neural-networks-deep-learning

After completing these courses trying to implement algorithms without using ML frameworks is really important, because you know; when you implement the algorithm you feel like "I really got this!"

Also there are YouTubers that make really useful videos about ML:

3blue1brown's deep learning calculus videos are really good and makes you able to what's going on in a neural network. https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw

Siraj Raval has more practical videos but has a math of intelligence playlist that good for learn math of ML https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A

**r/neuroscience**• comment

1 points • Spaceandbrains

https://www.coursera.org/learn/computational-neuroscience , https://www.coursera.org/learn/neural-networks-deep-learning , other courses on edx, coursera, ecornell are quite good? human brain project or other online resources could be useful to download mri datasets, then you could use spm and conn? https://www.fil.ion.ucl.ac.uk/spm/ https://web.conn-toolbox.org/ Hope it's useful!

**r/starcraft**• comment

1 points • jackfaker

Yes, you can do just about everything with only python.

Most of the topics fall under the umbrella of operations research. I don't know if your are interested out of just pure curiosity,but these topics have many applications in industry if you are looking for career direction, particularly in scheduling, logistics, data science, and general optimization. I could perhaps give more relevant advice if I knew your interests and background better.

Heuristic-based local search is a technique in discrete optimization. Here's a course I took that helped explain discrete optimization, including a section on local search techniques. https://www.coursera.org/learn/discrete-optimization. The assignments aren't very clearly structured imo but lectures are good.

Neural Networks are a way to approximate the relationship between inputs and output as a nonlinear function. Here is an introductory course I took a while back on machine learning that I would highly recommend: https://www.coursera.org/learn/machine-learning. And a follow up course: https://www.coursera.org/learn/neural-networks-deep-learning

https://www.datacamp.com has many good courses for building models in python, particularly relating to data science.

Both of those websites charge about $30/month for unlimited courses. A couple other buzzwords that might be worth looking into are Monte Carlo Simulations, newton's method, gradient descent, game theory. Here is a lighter documentary on alphaGO, a program developed by google that beat the world champion in Go: https://www.youtube.com/watch?v=9gzMQOa5MD4. And if you are more interested in the AI side of things I would highly recommend this playlist on reinforcement learning by deepmind: https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLqYmG7hTraZDM-OYHWgPebj2MfCFzFObQ.

**r/TooAfraidToAsk**• comment

1 points • ICrackedANut

"So are you saying thats its a mix of nature/nuture or is it 100% environment? "So are you saying thats its a mix of nature/nuture or is it 100% environment? Because if black peeps were raised well and stuff so they had the same iq as everyone else. Because they have higher bone density wouldn't that make them the superior race?"

​

Your thinking maybe right but due to lack of evidence we can provide, we can't say for sure that "You are black is why you are dumb". Currently, it is believed by some neuroscientist that environment is the cause. BUT you can change it by becoming a neuroscientist.

​

"Also i'd to love learn about some neuroscience :)"

​

First and foremost, most people who fail to study ANY subject is because of lack of self-discipline and not knowing how to learn properly.

​

Note: The courses below are all free. Click "Audit the course" after clicking enroll in Coursera.

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Step #1

Learning How to Learn is a course by University of California San Diego. It focuses on how to better learn and avoid procrastination.

​

Step #2

Now you need to learn the relevant math and stuff.

- Statistic and probability (You will learn that there is no such thing as 100% confidence in statistics.)
- Linear Algebra
- Graph Theory
- Digital Signal Processing
- Take sometime to memorize brain parts.
- Depends on which side you want to study (Don't worry about this now as your course will tell you.)

Step #3

Now you are ready to jump into neuroscience!

- Medical Neuroscience
- Synapses, Neurons and Brains
- Fundamental Neuroscience for Neuroimaging
- Computational Neuroscience
- You can also learn Neural Network (A.I.) now if you have learnt the math above.

Step #5

Now it is time for you to become a scientist!

- Scientific Methods and Research
- Now you can ask yourself what question public have that you as a scientist want to answer. You can do your research from here. Write a scientific journals and then perhaps write a book on it.

​

Remember, it takes discipline to learn something. You are not only are you gonna be learning neuroscience with above steps, you will be learning self-discipline too. Most people who fail is because they lack self-discipline. When you gain self-discipline, you basically win in life.

​

PS: Don't try to rush. Instead, sit down and study relaxly. You want to understand every topic well so get yourself a notebook and a pen. Enjoy your journey.

PPS: Get yourself a weekly blog. That way you will have motivation to study the path above. It will also be useful for future employment and university admission and of course, big scholarships.

**r/learnmachinelearning**• comment

1 points • ItisAhmad

I will make you a 1.5-month PATH assuming 4 hours a day for an entry-level JOB.

1) Coursera Machine Learning (1st 8 days)

Just watch videos of it. Do not focus on programming assignments as they are in Matlab. You can watch all videos of it in a week.

2) Crash courses of Numpy, Pandas, Matplotlib(2 days, total days = 10)

3) Udacity free Intro to Machine Learning by Sebastian Thrun. It is free and in Python. (15 days, total = 25)

3) Course 1 of deep learning specialization by deeplearning.ai to understand how neural networks work. (3 days, total = 28)

4) Do Fast.ai Practical Deep Learning for coders. 7 Lessons (2 hours each = 14 hours plus 10 hours on each assignment = 15 days) will give you Neural networks in a practical way and you will have knowledge of NLP COMPUTER VISION and all deep learning aspects in a practical way and above all, you will also have knowledge of a fancy framework **PyTorch. (1.5 Months total)**

5) In case quarantine longs more then this time, its time to brush up maths and stats. Start with either Khan Academy Linear Algebra Series or Gilbert Strang MIT Linear Algebra course. For Calculus again Khan Academy course which is taught by 3blue1brown. For stats, I would say MIT's stats course is pretty much good. (2 months in total)

Hopefully, you will land your first job after this schedule. Also, note that all courses are either completely free to free to audit.