Deep Learning Crash Course for Beginners
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Learn the fundamental concepts and terminology of Deep Learning, a sub-branch of Machine Learning. This course is designed for absolute beginners with no experience in programming. You will learn the key ideas behind deep learning without any code.
You’ll learn about Neural Networks, Machine Learning constructs like Supervised, Unsupervised and Reinforcement Learning, the various types of Neural Network architectures, and more.
✏️ Course developed by Jason Dsouza. Check out his YouTube channel: http://youtube.com/jasmcaus
⭐️ Course Contents ⭐️
⌨️ (0:00) Introduction
⌨️ (1:18) What is Deep Learning
⌨️ (5:25) Introduction to Neural Networks
⌨️ (6:12) How do Neural Networks LEARN?
⌨️ (12:06) Core terminologies used in Deep Learning
⌨️ (12:11) Activation Functions
⌨️ (22:36) Loss Functions
⌨️ (23:42) Optimizers
⌨️ (30:10) Parameters vs Hyperparameters
⌨️ (32:03) Epochs, Batches & Iterations
⌨️ (34:24) Conclusion to Terminologies
⌨️ (35:18) Introduction to Learning
⌨️ (35:34) Supervised Learning
⌨️ (40:21) Unsupervised Learning
⌨️ (43:38) Reinforcement Learning
⌨️ (46:25) Regularization
⌨️ (51:25) Introduction to Neural Network Architectures
⌨️ (51:37) Fully-Connected Feedforward Neural Nets
⌨️ (54:05) Recurrent Neural Nets
⌨️ (1:04:40) Convolutional Neural Nets
⌨️ (1:08:07) Introduction to the 5 Steps to EVERY Deep Learning Model
⌨️ (1:08:23) 1. Gathering Data
⌨️ (1:11:27) 2. Preprocessing the Data
⌨️ (1:19:05) 3. Training your Model
⌨️ (1:19:33) 4. Evaluating your Model
⌨️ (1:19:55) 5. Optimizing your Model’s Accuracy
⌨️ (1:25:15) Conclusion to the Course
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Hey all! Let me know what you guys think of the course?! Took a lot of preparation and work to get this out. Hope you all enjoy and get a solid foundation in the world of deep learning 🙂
I want learn machine learning and deep learning. Because cloud service is expensive. I want to use laptop for machine learning and deep learning training . Would you suggest any hardware configuration for basic and long term?
Such as CPU AMD, I5,I7,
Ram: 88G,16G,32G,
GPU：1060,1160,2060,2070,etc
Thanks
This is really well explained! Can you recommend a good book on the theories of Deep Learning? 🙂
can my 4 year old 16gb ram 1tb ssd i5 processor laptop handle deep learning?
When you say that our validation size depends on the hyperparameters, you mean the hyperparameters that the algorithms has by default or the hyperparameters that we tuned?
is it okay to say that RNN "digest" data a little longer (as an analogy to feedback loops) so it can "spit" better results?
Is machine learning pre requisite for Deep learning ?
Excellent introduction to the topic. Great slides, great explanation, right pace. Really good.
thank you
watch your career with great interest young Jedi
This is honest to god a great lecture and perfect for introducing deep learning! I hope there’s another video in the future that shows some light programming.
To be honest, trying to describe all of the different types in one huge block of information is too much. You'd be better off breaking it down into smaller parts rather than overwhelming the audience with too many new concepts and terms in one go. I got so far and just switched off about half way through, it's just too much to take in. You really shouldn't need an hour and a half for an introduction! You shouldn't have so much detail in an introduction.
Nice video!!!!! Very good work! Thx
Great video! I like the structure and the depth of knowledge shown in it.
Thanks bro. Appreciated.
Gud goin Jason ! Great content….Keep adding more courses….. Looking forward.
Loved it.
Thank you Jason!!!
Nice introduction. Some of the ideas probably should be reigned in a bit. For example, people who study learning do not think machine learning models how the human brain learns at all. Calling neural network nodes "neurons" misleads people into thinking you are actually trying to imitate a neuron instead of just using a software node. Stating that activation functions are non-linear is not always correct – in fact the equation you showed when you said that looks like a linear sum of terms.
All this doesn't take away from the work you've done to make these ideas accessible. It's a good intro – but hopefully people understand there are some sweeping statements that might not hold up under close scrutiny.
Excellent…..please upload the very basic idea of machine learning and artificial intelligence…. it's too useful for us like intern and upcoming future…please upload freecodecamp…
???
Some (hopefully constructive) annotations to improve the video for better clarity:
* 6:33 – "Channels have weight". And the slide matches it. But 7:08 says the weight is something of a neuron (how important is the neuron, rather than how important is the relationship). I think it is confusing; aren't weights a property of the relationship/channel, in graph theory?
* inside slides that list advantages/disadvantages you might use the color red for disadvantages, and not just always green to highlight terms. Ex. "small" at 29:08.
* I like the examples of descending the Everest, and the one about memorising songs.
* 40:21 – the slides disappears till 40:57
* same at 52:01; till 53:11
* 58:15 – "sometimes you may find the ???? depicted over time" (the automatic subtitles don't get it either)
* 1:04:17 – audio says "input gate, output gate, and a forget gate"; but the slides shows "Update, Reset & Forget gates".
* 1:10:30 – "although if you are interested I'll leave them in the notes below"; yet, it would be useful if a list (or a link to more info) could be added in the description.
These are the major notes that I think should be fixed, for better clarity.
Anyway it is well made, a good explanatory overview of the neural networks world, that I had no idea about it.
It was ok to understand for me, if I skip on the name of the specific algorithms (that in the end are implementation details).
But I already got some basic knowledge of statistics & data-analysis, and about graph theory. My only doubt is if others that never dig in those topics can follow this video as well.
Keep up with this interesting contents! Thanks for your time and effort!
who also think that freeCodeCamp deserve to earn money via youtube ads after all these years of free service.