Machine Learning vs Deep Learning
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- Опубліковано 30 бер 2022
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Get a unique perspective on what the difference is between Machine Learning and Deep Learning - explained and illustrated in a delicious analogy of ordering pizza by IBMer and Master Inventor, Martin Keen.
#AI #Software #ITModernization #DeepLearning #MachineLearning
Thank you for such a valuable explanation. The practical example revealed the potential of these methodologies and your charisma made the video easy to follow. Cheers!
Wow! I am impressed how good you are at explanation such things. I was struggling with it. Thank you.
I appreciate you for broadening my horizons on the subject.
I'm still wondering how he wrote all of those from the opposite projection from us.
He wrote in his natural wiriting direction and the video was flipped left to right during video production before uploading.
I had the similar query. It's amazing btw.
I checked just to see if it was the first comment...
I'm pretty sure he just wrote on a tablet of some kind, recorded the screen he was writing on, keyed out the background in a video editor and overlaid and flipped during editing.
Lightboard is a glass chalkboard pumped full of light. It's for recording video lecture topics. You face toward your viewers, and your writing glows in front of you.
Thank you so much. You have explained it brilliantly ❤
The presentation was amazing!
It was a great explanation for ML and DL. That Neural Network was a key detail for understanding The difference between ML and DL and their Fundamentals.
That was very interesting and a great explanation of machine and deep learning.
Phenomenal easy explanation ❤
Your smile made me really enjoy the whole video! Thank you for the wonderful video : )
Thank you for valuable information 🙏🙏
I love these videos. I just had a tech exec at a Fortune 200 company ask me for any podcasts that could help him stay abreast of current/emerging technology. I didn't have a great answer for him, but I did mention this series. He was looking for more audio-centric content though. Food for thought, @IBM Technology!
We're glad you like the videos! As for a podcast, it's definitely something we're interested in, make sure you're subscribed, we'll be sure to announce it here, if and when it happens.
Fantastic distillation of the concepts.
Are the presenters mirror images to make their writing appear the way it does or is it another tech trick?
Unsupervised learning is not limited to deep learning. The classic ML method k-means clustering is already able to discover the similar patterns given the samples.
I would say that the bright side of deep learning is on the feature extraction. In the old days, we do a lot of work to discover useful features: feature engineering. With deep learning, now we only need to supply the most basic features to the model, pixels for images, raw waveform or spectrogram for speech. This saves my days.
so do you think the need for labelled data will decrease or increase?
@@estring123 labeled data will still be valuable for some tasks, especially for fine-tuning models, validating performance, and solving new and specific problems. On top of that, having labeled data is critical for certain applications where high accuracy and interpretability are required for example medical diagnosis or safety-critical systems. Depending on the specific machine learning task and the type of data available, the balance between labeled and unlabeled data will vary.
@@estring123 Yes, you will still need labbeled data, the example given in the video is very bad and very wrong, deep learning models are a form of Supervised learning because like in the Video you might ask what in an image of a Pizza makes the algorithm know its a Pizza? The label Pizza is an arbitrary name given by people to it, you need the label to train the network.
Back propgation isnt just going backwards like the video suggest, its the algorithm that actually make this Neural networks feasable from a computational possible other with it would be too slow.
So why can this deep networks can "learn". The root of it is convolutional Neural networks, the convolutional layer take sections of image and isolants features, where previously the feature selection was crucial for success. Knowing the correct set of convolutional Layers on the other hand is not easy, so it was the combination with Genetic Optimization algorithms that have made them effective. However the output layer will still need labels, unsupervised learning is only useful to find useful features. But a classification problem needs labels this should be obvious, otherwise you cant classify.
Nice, loved it.
Thank you! It was a great explanation!
Great video. Thank you
I programmed a 8080 to Jump Non Zero at times. Full Machine code to make side street and main street traffic lights. Worked first time with no bugs.
Awesome videos! Love your teaching method!
I like your style... you IBM people are smart....
Actually, the example is IMHO not well-suited for explaining ML and/pr DL as the aspect of "learning" (which is actually an optimization) is not really addressed by it. So it remains unclear a) what learning actually IS in terms of the example, and b) how the decision making can benefit from the learning aspect of the model.
From 1-10 this is 20!! Thanks!
How did u determine the threshold? How did u come up with -5? Please explain this concept. Thanx!
I also want to know that
Maravilhoso! Amei o vídeo, nota 1000000...
Excellent video. Thank you
very easy well explained thanks!
Thank you for your useful video🙏🏻
Thanks for the information given to me.
Thank you
Great explanation
the way he explained ! Boommed my mind
Thanks!
where are all the neurons, weights and biases stored? in ram, in a database? what datastructure is used?
good one!
Great Video. I would like to understand more details about the layers. What are layers from a logical and technical prospective?
They are computing processes, I think.
Bless UA-cams play speed feature.
Hi there. Very helpful. Thank you.
Machine Learning (ML) and Deep Learning (DL) are both subsets of artificial intelligence (AI) that focus on different approaches to learning from data:
1. **Machine Learning (ML)**:
- ML is a field of AI that involves developing algorithms and models capable of learning from data to make predictions, decisions, or uncover patterns.
- ML algorithms can be broadly categorized into three types:
- **Supervised Learning**: The algorithm learns from labeled data, where inputs are paired with corresponding outputs or target labels. Common tasks include classification (predicting categories) and regression (predicting continuous values).
- **Unsupervised Learning**: The algorithm learns patterns and structures from unlabeled data, without explicit target labels. Tasks include clustering (grouping similar data points) and dimensionality reduction.
- **Reinforcement Learning**: The algorithm learns through trial and error interactions with an environment, receiving feedback in the form of rewards or penalties. It aims to maximize cumulative rewards over time and is used in scenarios like game playing and robotics.
- ML models are typically based on statistical methods, feature engineering, and algorithmic optimization techniques.
2. **Deep Learning (DL)**:
- Deep Learning is a subset of ML that focuses on neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.
- DL models are capable of automatically learning features and patterns directly from raw data, without the need for explicit feature engineering.
- Key components of deep learning include:
- **Neural Networks**: Composed of interconnected layers of neurons, neural networks are the building blocks of deep learning models.
- **Deep Neural Networks (DNNs)**: DNNs consist of multiple hidden layers between the input and output layers, allowing them to learn complex representations of data.
- **Convolutional Neural Networks (CNNs)**: Specialized DNNs for processing grid-like data such as images and videos, leveraging operations like convolution and pooling.
- **Recurrent Neural Networks (RNNs)**: DNNs designed for sequential data processing, with connections that allow feedback loops and memory of past information.
In summary, Machine Learning is a broader field that encompasses various learning algorithms and techniques, including supervised, unsupervised, and reinforcement learning. Deep Learning is a subset of ML that focuses on neural networks with multiple layers to automatically learn hierarchical representations from data, particularly effective for tasks like image recognition, natural language processing, and speech recognition.
Very nice bhai 👌🏻
So next time I can't figure out what to have for dinner I just need to build a neural network?
I don't completely agree on deep learning explanation, because for weight training, labelling is required. Yes pattern/feature extraction can be debated, but labelled data is required
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I think there is a confusion between feature extraction and unsupervised learning. Hope that you can revise it
greate!
Charismatic presentation...
Great. I was always thinking NN and DL are two words for the same thing.
respect for writing backwards so the camera sees normal
Please provide, is multi layer neural network a deep learning model ? If not, please provide me an example of deep learning model.
Content is great. Audio is too low on these videos.
"It's time for lunch!" lol. I love this video. Thanks so much!
I hate the idea of weighting variables because if you change them you change the answer. Which to me suggests there is no right or wrong answer - but if you get it right for your business or problem it says to me figuring out how to weight the variables is actually where the true problem and data is.
Introduces bias, which, depending on the scope, would include not just personal bias, but company bias, industry bias, and political bias. Weights and models have this issue.
Thank you :)
Thanks a lot
Superficial explanation.
You're making education engaging and accessible for everyone. #NurserytoVarsity
You did well explaining mate, no idea what they’re talking about
Plot twist, most people were eating while watching this video.
Lol I know this guy from his beer brewing channel. I had to double check if it's actually him. So here I am, learning both how to brew beer and both Deep Learning. Crazy coincidence haha
Beautifulllllllll ❤️❤️❤️😊
Thank you! Summary: deep learning is not so deep after all!
TL;DR:
If an NN has more than 3 layers, it's considered a DNN.
DL finds patterns on its own without human supervision, and learns from them. It's a more specific type of ML.
You used threshold as 5 what actually threshold means according to your example of pizza ?
I didn't know that Gordon Ramsay gives lessons about Machine learning and deep learning. for real tho the video was amazing and very helpful
Steve Brunton style is becoming a genre...
is there any connection b2n Semi and Reinforcement Learning
bro managed to make an example about pizza... and i was eating it while watching this video 💀
the moment he said pizza, i just pause and ordered one and resume when i got pizza.
I loved it❤️
I do not understand about the input Zero 0. Whatever weight you give to it, it will always evaluate to 0 so either you give it weight 1 or weight 5 the outcome is the same. What is the catch?
Is one actually 2?
I think replacing '0' with a '-1' will solve the problem.
I'm impressed how he can write backwards so good haha
See ibm.biz/write-backwards
It's a recruiting criteria for IBM
Now I want pizza AND burger AND taco.
Where do you get free pizza coupons?
Homebrew Challange guy!
00:20 No Sir - won't do that. Can't learn while digesting pizza.
So I do like this series, but this confused me because he switched from one output - "should I buy pizza" - to another output - "is this a pizza or a taco". Is this a fundamental difference in what DL vs ML is able to do? Or that the first output doesn't require as many layers to become a neural network so therefore would always sit at a DL level? Sorry, I think I need to do more study and come back to this video
Dear Martin Keen, I really liked your video and find it extremely useful. However, I wanted also discuss about activation function so the formula you used is - (x1*w1)+(x2*w2)+(x3w3)-threshold. As I understood the threshold is a biggest number used, so that's why you took number 5? Also our w2 is equals to 0, so if the w2 even would be 999999999 (like for us it is super important to be fit) the answer for whole equation would still be positive. So this is my concern about formula if w2 id more prevalent than other options, why in any possible situation we are only capable to have the answer YES ORDER PIZZA. and even x1 and x2 would be 0, but x3=1, with w1 and w2 equaled to 899796 or any other big number we will still get positive outcome. This really baffled me, so I would happy to read your response!
Hello, I think this can be solved by replacing the number '0' with a '-1'.
By doing so I guess it will be a more fair output based on our preferences.
Good question btw 👍
My question as well.
I don't understand how you suddenly use 1(yes) and 0(no) as numbers to calculate with?
Yes, any weight attributed to x2 will result in 0. Can someone please explain this?
5:08 classical ML human intervention
So is it possible to have unsupervised Machine Learning?
You can. Just be sure to hide the pizza.
Academically speaking, should AI not be a subset of DL? I think you’ve done a commercial magic trick here.
No.
nice
UPSC aspirant from India 😊
It would be a pleasure, if someone could tell me how you can make a video like this
I mean "writing on the screen" :)
why 'Threshold' was 5 ?
how is it possible for you to write 🙏😅
looking at us
which way is the board?
noticed he's been writing with a left hand😇
It is very simple, in most video editing programs, to flip a video horizontically.
What a coincidence lol.. im eating burger when clicked on this video 😅😅
4:30 but if your interest in staying lean is 10000, the equation still comes to the same conclusion. shouldnt X2 therefore be a choice between -1 and +1?
❤
Do you write mirrorwise?
Search on "lightboard videos"
Top tip: the worst thing you can do when you’re learning is eat food beforehand :)
📝 Summary of Key Points:
📌 Deep learning is a subset of machine learning, with neural networks forming the backbone of deep learning algorithms.
🧐 Machine learning uses structured labeled data to make predictions, while deep learning can handle unstructured data without the need for human intervention in labeling.
🚀 Deep neural networks consist of more than three layers, including input and output layers, and can automatically determine distinguishing features in data without human supervision.
💡 Additional Insights and Observations:
💬 Quotable Moments: "Neural networks are the foundation of both machine learning and deep learning, considered subfields of AI."
📊 Data and Statistics: The threshold for decision-making in the example model was set at 5, with weighted inputs influencing the output.
🌐 References and Sources: The video emphasizes the role of neural networks in both machine learning and deep learning, highlighting their importance in AI research.
📣 Concluding Remarks:
The video effectively explains the relationship between machine learning and deep learning, showcasing how neural networks play a crucial role in both fields. Understanding the distinctions in layer depth and human intervention provides valuable insights into the evolving landscape of AI technologies.
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Free pizza? There's nothing to calculate there. That pizza is mine!
We'll gees! That was easy now wasn't it? I was doing really well during the "order out" part, but after that, I turned off the video and ordered a pizza.
Then what is PCA, Regressions etc..?!
Threshold value 5 means what sir!?
If the sum is inferior than this threshold , so the decision is negative
What will happen if the out put is zero
I would have thought AI was a subfield of ML.
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love
I want to get a pizza after this
I would like to earn badges and know about educators programs with IBM