Practice is important for subjects like math and coding bcz it help you choose the right tools needed for the task sometimes you might need the combination of two and more tools which wasn't mention on theory part of that tool.
Also, for subject which requires understanding of things (like Operating System). Try building a visual model in brain. If u already know some of the parts then connect those part visually with the new concept you are learning.
Usually, this is the best I can do. It's like revealing the map in a computer game. You start with a set of absolutely unfamiliar concepts and then, as you learn more, the connections between them start to appear.
PaperVoice: Free audiobook app for LibriVox recordings ( Or classic audiobooks). We started this project to support our friends who wanted the audiobook for their books. Since Audible doesn't allow machine voices ( and human narrating is expensive). We decide to create a platform for these authors.
So, It's a platform where any author can publish their audiobook.
Got it. But these corporation have alot of influence in our political system. If things will go on like this we might end choosing the govt. (or the political system) they want. Bcz they might filter the other feeds which are impacting them somehow.
This is not a matter of people making a choice, this about people attacking the Congress, killing two cops, etc.
If any kind of filtering is relevant to this it is 'if it bleeds it leads'; if you have decapitation videos on your platform that is great for engagement and clicks.
The Alchemist and Eleven Minutes from Paulo Coelho
These books taught me to take risk and importance of being different.
“There is only one thing that makes a dream impossible to achieve: the fear of failure.” — Paulo Coelho, The Alchemist
“Everything tells me that I am about to make a wrong decision, but making mistakes is just part of life. What does the world want of me? Does it want me to take no risks, to go back to where I came from because I didn't have the courage to say "yes" to life?”
― Paulo Coelho, Eleven Minutes
This course is extremely good mostly because it covers the essential theoretical topics and gives some practical advice.
TIP: do solve the assignments bcz it will clear a lot of concepts while solving it. ( or other solution can be found on github )
I strongly second that and also Andrew Ng's : Machine Learning course.
The only thing that was annoying for me was that the Jupyter assignment auto-grader would incorrectly fail correct answers and it's not always easy to debug the reason why it failed. If the python syntax deviates too much from the expected answer, it also can cause some issues. Please note: I am a very experienced programmer and have been using python for more than a decade. This was not my first rodeo...
Otherwise this should in no way be a deal breaker, the material and assignments are top-notch. The forums are also helpful in finding out issues with the auto-grader.
I went through it, really enjoyed it. But I had experience in the same subject matter before taking it. If you have some ML experience, I would recommend diving straight in for a good breadth-first look at deep learning topics. If you don't have any ML experience or don't really know the concepts, I would recommend taking their other course first (Intro to AI, or AI for Everyone, or w/e -- which I skimmed to see if it was something I should recommend to others, and I liked it).
The deep learning course is taught in a way where you don't need the machine learning course first, so it's possible to start with either, especially if you have any familiarity with ML. Deep learning is one specific type of machine learning so there is alot of other techniques you will be missing if you only do the deep learning course though.
I think it would be useful to take Udacity’s machine learning class as well to provide an additional perspective. There are some parallels such as edge detection uses the same techniques as convolutional networks, regularization is a general technique useful for both neural networks and clustering, optimization of steps in planning probably has a parallel to how Alpha Go works. Particle filters was cool.
Also, take several of the classes in the specialization. Don’t stop at the first course. Convolutional networks has been great.
I liked working on the notebooks and watching the interviews with some of the pioneers of Deep Learning.
I have also started learning NLP this year. Here is what have followed.
If you are completely new to deeplearning I would recommend course from https://www.fast.ai. Since it will teach you the concepts in a more practical way and also the next courses will more theory than practical.
For theory on NLP with deeplearning you can follow Stanford course by Christopher Manning.
it will give you a good understanding of how deeplearning is used in a certain area of NLP. But remember deeplearning is one of the techniques for solving NLP problems if you are more interested in Understanding NLP then I would recommend the following book.
Although, Stanford course should give you a great high-level understanding of how GPT models work but if you really need to go under the hood then I would suggest after finishing this course you should learn Unsupervised Learning in DeepLearning.
Currently, there isn't any perfect TTS system that almost sounds like a human and if you are looking for good TTS services then neural voices from Google, IBM, and Amazon might be promising since they are trained on recent DeepLearning technologies. But they are pretty monotonous although you can define the speech prosody using SSML tags but the results are not good.
Also, for subject which requires understanding of things (like Operating System). Try building a visual model in brain. If u already know some of the parts then connect those part visually with the new concept you are learning.