Machine Learning vs Deep Learning 

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.

Thank you so much. You have explained it brilliantly ❤

I'm still wondering how he wrote all of those from the opposite projection from us.

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.

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.

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?

The presentation was amazing!

Phenomenal easy explanation ❤

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!

Thank you for valuable information 🙏🙏

Your smile made me really enjoy the whole video! Thank you for the wonderful video : )

I like your style... you IBM people are smart....

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.

Thank you! It was a great explanation!

From 1-10 this is 20!! Thanks!

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.

How did u determine the threshold? How did u come up with -5? Please explain this concept. Thanx!

where are all the neurons, weights and biases stored? in ram, in a database? what datastructure is used?

Awesome videos! Love your teaching method!

Nice, loved it.

Great video. Thank you

Maravilhoso! Amei o vídeo, nota 1000000...

Please provide, is multi layer neural network a deep learning model ? If not, please provide me an example of deep learning model.

is there any connection b2n Semi and Reinforcement Learning

Thank you

I think there is a confusion between feature extraction and unsupervised learning. Hope that you can revise it

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

Bless RU-vids play speed feature.

Great Video. I would like to understand more details about the layers. What are layers from a logical and technical prospective?

You used threshold as 5 what actually threshold means according to your example of pizza ?

Great. I was always thinking NN and DL are two words for the same thing.

Thanks for the information given to me.

Excellent video. Thank you

Thanks!

Thank you for your useful video🙏🏻

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.

very easy well explained thanks!

I've always thought that supervised learning is classification, and unsupervised is clustering. Thus DL is always a supervised learning because it still needs a labeled learning set. The differentiation between NN and DL is only in the feature extraction part, NN and "classic" ML require expert knowledge to shape input features, which are computed from the raw data and often normalized. In other words, DL doesn't require labeled features but still needs labeled data to learn from. Also, ML is not only NN but also rule induction algorithms (decision trees, Bayesian rules).

the way he explained ! Boommed my mind

Content is great. Audio is too low on these videos.

Hi there. Very helpful. Thank you.

Superficial explanation.

Charismatic presentation...

You're making education engaging and accessible for everyone. #NurserytoVarsity

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.

Great explanation

You did well explaining mate, no idea what they’re talking about

greate!

good one!

"It's time for lunch!" lol. I love this video. Thanks so much!

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?

So next time I can't figure out what to have for dinner I just need to build a neural network?

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.

respect for writing backwards so the camera sees normal

Steve Brunton style is becoming a genre...

Thank you! Summary: deep learning is not so deep after all!

Where do you get free pizza coupons?

Very nice bhai 👌🏻

Thank you :)

why 'Threshold' was 5 ?

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?

It would be a pleasure, if someone could tell me how you can make a video like this I mean "writing on the screen" :)

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

Thanks a lot

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

how is it possible for you to write 🙏😅 looking at us which way is the board?

Plot twist, most people were eating while watching this video.

I don't understand how you suddenly use 1(yes) and 0(no) as numbers to calculate with?

How it's possible,so risk trade but you win so amazing. I from bangladesh.i want do it same to you

Then what is PCA, Regressions etc..?!

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

So is it possible to have unsupervised Machine Learning?

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!

I'm impressed how he can write backwards so good haha

UPSC aspirant from India 😊

the moment he said pizza, i just pause and ordered one and resume when i got pizza.

What will happen if the out put is zero

bro managed to make an example about pizza... and i was eating it while watching this video 💀

Beautifulllllllll ❤️❤️❤️😊

5:08 classical ML human intervention

Threshold value 5 means what sir!?

Homebrew Challange guy!

I loved it❤️

What a coincidence lol.. im eating burger when clicked on this video 😅😅

Do you write mirrorwise?

Now I want pizza AND burger AND taco.

00:20 No Sir - won't do that. Can't learn while digesting pizza.

humanities are fundamental in this proccesses! now the funny example is pizza, what about human rights? who's feeding the bias to the algorithms???

❤

Academically speaking, should AI not be a subset of DL? I think you’ve done a commercial magic trick here.

nice

I would like to earn badges and know about educators programs with IBM

Top tip: the worst thing you can do when you’re learning is eat food beforehand :)

Free pizza? There's nothing to calculate there. That pizza is mine!

📝 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. Made with Talkbud

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.

I would have thought AI was a subfield of ML.