本字幕由TME AI技术生成
喔耶耶耶喔
Okay
So you're thinking about uh getting into the world of deep learning
Maybe definitely a hot topic
Right now yeah
And maybe even looking at it as a career for sure
Well you come to the right place
Absolutely today we are bringing you the essential advice on how to get started and thrive deep leararexactly today for
For deep ep ve ve we've been looking closely at a conversation with entering a true pioneer in the field
Death ony from this clip on the next friddman ai podcast
We've been ststracting the most valuable invice for you guys
Ys exactly the learning pass the core concept things
You really need to know and even how to approach building a career
It's a dynamic space
So you got another moves
Yes
So the the mission today is to cut through all the noise and give you a clear actionable road map
I like it
We want you to walk away
Knowing exactly how to begin your deep learning adventure
What to focus on first and how to see your long term growth in this field very cool and feel in power to art art
You don't need a fphd or anything
That's a relief
Okay
So you wait a jumin
Where does andrew nangg suggest someone even begin so a really interesting starting point
He brings up is his own machine learning course
On course sarah
And it's really amazing how many people who are now active and ai point back to this course as their initial spark their foundation
Yeah
Seems to be a very very pivoal experience for a lot of people
Also that's lot sense se so that's kind of a general machine learning course
Yeah
What deep learning specifically so so that he really adadvocates for the deep learning specialization
Which is also on course serah
Ah cool
And this program is designed to give you a deep understanding of neural networks
I like it from the basic building block right to sopisticated architectures
Like you've probably heard of cnns
Convolutional neural networks
Rns
Recurrent neural networks
Of course
Sequence models
Attention models
It's all there
So it's sentainly
This is really ththrow hands on experience in those technologies
Yes
Definitely the ones that power
Image recognition and natural language processing absolutely those are some of the big ones
What's the core aim in his view
是的 对的
The main goal is to go beyond theoreticical understanding
It's structured
So learners can actually implement these algorithms right and use them effectively from the very beginning
That's great
I mean so many times i see people you know learning something yeah
But they never actually put into practice
It into speciactice
Yeah Yeah
That's what's great about the specialization as you can you learn the theory
But also you get the skills
Get the skills or love it
So now for someone i ying the specialization
What kind of background do they need right to succeed right you know
Realistically
Realistically
Do we need to go back to calculus textbooks
Luckily No
Not at all okso for programming just basic understanding is necessary okay
Primarily because the exercises are done in python
Right that's for the math and eggest school level l good enough to get started okay
So no
That's nothing too crazy
Nothing too crazy
But he does highlight basic linear algebra especially understanding nothing to multiple fly matuses can be very beneficial
I see that makes sense kind of the language of deep learning
So basbasic
Ultro sumppie
That
That's great
That prepretty low berriored entry
It is now when someone dives in especially through the specialization
Yeah
What are some of the foundational concepts that run into early on the specialization really zero in on the fundamental building blocks of neural networks
Okay
How to build them from a single unit to multiple interconnected layers
Yeah
Understanding those different activation functions is also really key to what exactly are those activation functions again
So think of them as the decision makers within a neural network
That they allow the network to learn those complex nonlinear patterns
Ok
That go beyond simple linear relationships in the data i see
And that's the beauty of them
They introduced the complexity
That allows these networks to solve those really tricky problems i see
So like they're making decisions based on what they see in the data exactly and deciding what information to pass on to the next layer got it
So it's about understanding the core components yes
How they fit together yes
But it's not all just theory right no definitely not nans emphasize the practical side of things
Absolutely a big part of the deep learning specialization is getting these models to work in the real world
Okay
You understanding those little nuances between different optimization algorithms right those are the ones is fine to in the learning process
Exactly
And then also identifying and addressing overfitting okay
So what's over fitting again
Basically
It's when modeting model memorizes the training data too well okay
And then does poorly on new unseen data i see
Kind of like if you just memorize the answers to practice questions without understanding the concept right
You wouldn't do well on the real tax
Exactly you can say overfitting is a common pitfoall
For sure
What other practical skills does the specialization teach so knowing when to get more data
And when it's not onna help you and points out that some people spend months chasing more data
When the real problem is somewhere else right it's not always the solution
Exactly
And it also teaches you how to modify the network's architecture okso so how to cture ture those layers
We talked about
Exactly that to improve performance
So it sounds like it really equipped you not just with knowledge
But with the ability to trouble shoot in iterate on your models
For sure
It's all about getting those practical skills
I like it
He also touched on something that i found interesting
是的 对的
Getting your hands dirty with actual networks
Yes
He's a big advocate for that
But was his advice on it
He says dive right iting an experiment
Wehave train them on small datasets
Run inference to see what they predict just play around
Yeah
Building in into it to get get feel for it
Exactly
Yeah
He cautions tions agast spending months on large abstract projects
Before you really abstract the basic i see you need hands on experience with the fundundamental absolute help soludify the understanding for sure
Yeah
So learn by doing and ststart small i like it
He also made it interesting comparison to programming
Oh Yeah
Knowing the syntax vers understanding the structure of code
Right how does that apply to deep learning so in programming
Knowing how to right lines of code
The syntax is just one part of the picde
The understanding how to organize those lines into coherent systems is coqually important
Right if not more sell i make sense
It's the same with machine learning
Ok
It's not just about the deep learning
Syntacx is not you know how to write the code to define a neural network
It's about understanding the entire process of building training and evaluating a model
Effectively so the big picture
The big picture yeah you know had to structure the data
Choose the right architecture debug the learning process absolutely all of that
And i imagine debugging in machine learning is a different beast than traditional software debugging
Oh
For sure
In traditional software you might be looking for a misplaced semiccalin or a logical error
But in machine learning you're often trying to figure out why a model isn't learning or why it's not improving
I say so you're looking at things like do you have enough data is your model overfitting
Did you choose the right optimization strategy those are important questions
They are an ygg really emphasize that those who can debug those kinds of issues in machine learning are super valuable right
Because it's not always straighforward
Yeah
It's a whole different way of thinking
Yeah
He also mentioned that deep learning is like learning math in that concepts build on each other
Yeah he makes that comparison for a reason
Why is that significant
对对对对对
Well if you miss a foundational concept in math
It makes it so much harder to understand later more advanced ideas
Right
That's a domino effect exactly
Training
Training foundation exactly
So if you don't understand basic
Basic algebra trigometry will be a struggle make sense
So if deep learning
If you don't understand how gradient descent works
Which is a key optimization algm
So then understanding more complex architectures or advanced training techniques will be really difficult
Which is delelearstanberation design to address that it is
It breaks down complex concepts into smaller digestiable modules
So so get get sosod underunderstanding each each stage before moving on so all about building that foundation layer by layer exactly brick by omke
No
He talked about oho moments in the learning process
Yes
Those light buld momoment what really did up for him
So he found that reinforcement learning especially seeing deeper reinforcement learning in action is really inspiring
Okay
Specially ally fleners so for this work from the
You can use a quick cap of what reinforcement learning is all about sure
So it's basically where an agent learns to make decisions by interacting with an environment
Yeah
And getting rewards or penalties got it so imagine a neural network learning to play a game or control a robot from scratch
I see witnessing that can be a real law moment
Yeah
You can really see the potential
Exactly
It igninites that interest it deepens the understanding
Now we also talk about the current real world applications of reinforcement learning
Yes
And this is important
Right it's grateful leararyeah
And has some successing game egg points out that it doesn't have as many widespread and packful applications outside of games
Ok
Compared to other areas of deep learning
Like supervised learning
Supervised learning being where you train models on label data
Exactly
And that's behind so many of the ai applications we use every day right so reinforcement learning is still a vital part of ai of course
But it's not necessarily the most immediately applicable skill for everyone
One that's a really important point
I think a lot of people get really excited about reinforcement learning they do
But it's good to have that balance prospective
Absolutely
He also pointed out the deep learning isn't the only tool in the ai tool box
Yes
That's very true
What else do
Ai professionals use use effective aa teams use a variety of tools depending on the problem
So things like statistical methods right dimensionality reduction techniques like pca principle component analysis right graphical models for relationships tween variables
Even knowledge graphs for organizing information
So it's really about having that broad skill set
It is to choose the right tool for the job
Exactly not just trying to fit deep learning into every situation
Right some problems are better suit for other methods
No
For someone taking the deep learning specialization
Owhat's the time commitment like so officially
It's about sixteen weeks
Which is roughly months okay
But it's self paced so some people might finish it faster
Exactly if they dedicate more time
They could finish it in a month or even less
That's great
And he also mention ed financial ate is available
Yes for those who need it
Which is it makes it accessible to a wider audience
Absolutely beyond that structured learning environment
What advice did he gave for learning consistently in such a demanding field
He really emphasized reguctive building a consistent routine
Even if it's just for short perioeach each day weethink it's like conlening ning easier er more effective
I like
Like that like
Like brushing your teeth
Once you make a hahabit
You do withwithout inking like aa create analogy
That's a consistent effort is what leads to real progress
Not those sporaditic bursts of work
Exactly
He also shared a specific study technique that he finds helpful
Yes
He's a big fan of handwritten notes interesting
Why is that because it forces you to slow down and rephrase the information in your own words ok
Which actually helps you retain it better in your long run
So it's not just about transcribing word for word
No
It's about actively engaging with the material
Summarizing it and making it your own i see
It's about that deeper level of processing exact
So it's not just about how much time you spend learning
But how you engage with the material absolutely quality of equality
Now that shift gears and talk about building a career in deep learning
That's good
Where should someone whose grasp the basics through a course
Like this specialization start their professional journey
Angg says
Strstrong ounoundation through course work like this aa way to ststart kay
It helps you master the fundamentals and break into the field
And this is even true for those interested in research
Yes
Even then having that solid base is incredibly beneficial
So beyond course work
What else is important
Gaining practical experience through personal projects is key
Okay and staying current by reading blogs in research paper
Right
The field is always evovy actly
So you have to keep up with the latest advancements
He also talked about the importance of starting small yes
Both for individuals and organizations
Why is them starting with smaller well defined projects
Allows you to build your skills and intuition
Without getting overwhelmed i see
So you can get those quick wins and build confidence
Exactly
And then you can iterate and learn from those experiences before moving on to bigger thing right you don't want to buy ite off more than can can w now for those who might be thing
You are going into research
Yeah
What are ang's thoughts on pursuing a phd
对的对的
So he says it's not strictly necessary to make significant contributions to the field interesting
Well it's essential for becoming a professor at a top university right
There are other ths in industry research and start tups
Where you can have a real impact without of phd
So it really depends on your individual goals
It does what specific career pass did he highlight
So he mentioned working for large tech companies
Okay
Joing ing seararch groups within those companies
I say going the academic out as a specifiss so researcher right or launchching your own ai ststup the excciting
It is and eemphasize that each path offers unique opportunities right
But maybe the most interesting advice gave wasn't about more specific role or company
That's true
What was that key take away
He stressed that the people you work with everyday are so important
Okay much more important than the company itme or whether you're an academia or industry interesting
So it's not just about the work itself no
It's about the environment
And the people you're surround
What did he specifically recommend
He said to look for great managers who can mentor you and talented supportive peers who you can learn from that makes a lot of sense
And he even suggested asking about your manager and team during the interview process
So finding out who you'll be working with is crucial absolutely
Because that can make or break your experience
It's all about those daily interactions in the support you receive exactly those things really influence your growth and your career satisfaction and having colleagues
Who challenge and support you accelerates your learning
For sure
It's all about that collaborative leararenvironment so to rap things
Things for your listeners
The key message from andrew hang is that starting your deep learning journey is more accessible than you might think yeah
It's not as daunting as it seems right
It's about taking advantage of resources like the deep learning specialization and committing to consistent effort putting in the work
Yeah
And always focusing on practical application
Through hands on projects learn by doing
Exactly
And even with a basic understanding of math and programming
You can build a solid foundation
That's encouraging it is in remember the journey involves both understanding the concepts and developing those practical skills
Debugging
Refining your models all part of the process and that key point he made about the people you work with yeah
That was really insighttful sugggest that your colleagues and the learning environment they create can be just as important as the technology itself maybe even more so as you think about this
What aspects of deep learning are you most curious about yeah
What excites here what kind of projects
Could you start to build
Put your skills
And how can you integrate those consistent learning habits into your routine
And finally a thought provoking question to leave you with i like get if the people around you are so crucial to your career growth right
How will you find and cultivate those valuable connections as you dive in to deep learning
That's a great question to ponder as you take your next steps
It is yeah feeling
Thanks for joining us for this deep dive into deep learning
It's been fine
It has and be sure to tune next time for more insights and explorations
See the febby
