Tutorial 1- Introduction to Neural Network and Deep Learning
Summary
TLDRIn this video, the presenter introduces the basics of deep learning, comparing it to how the human brain learns from the environment. They discuss the evolution of neural networks from perceptrons to more advanced models like CNNs and RNNs, highlighting the significance of backpropagation invented by Geoffrey Hinton. The video aims to guide viewers towards mastering deep learning, potentially aiding career transitions. It promises to cover neural network architecture and backpropagation in upcoming videos.
Takeaways
- đ„ The video aims to teach deep learning concepts and provide code examples on GitHub.
- đ§ Deep learning mimics the human brain's learning process, which was an idea conceived in the 1950s and 1960s.
- đ¶ The script uses the example of distinguishing between a dog and a cat to explain how neural networks learn from features.
- đ¶ It discusses how humans learn to recognize objects by observing features and receiving explanations from others.
- đ The script mentions the limitations of early neural networks, like the perceptron, which could not learn properly.
- đ The 1980s brought the invention of backpropagation by Paul J. Werbos, which greatly improved neural network efficiency.
- đ Backpropagation is a key concept that has allowed neural networks to be used in many applications.
- đšâđ« The video promises to explain backpropagation in upcoming videos.
- đ The script encourages viewers to subscribe to the channel for more deep learning content.
- đ It suggests that viewers search for more information on Google and learn from experts like Jeff Dean.
Q & A
What is the main goal of the video?
-The main goal of the video is to introduce the basics of neural networks and deep learning, with the aim of helping viewers become proficient in these areas, potentially aiding in job transitions.
What will the presenter be uploading to GitHub?
-The presenter will be uploading code related to deep learning and neural networks to GitHub, which viewers can follow along with to enhance their understanding.
What is deep learning?
-Deep learning is a technique that mimics the human brain's ability to learn from the environment, using neural networks to process information.
When were the initial concepts of neural networks developed?
-The initial concepts of neural networks were developed in the 1950s and 1960s.
What was the limitation of the perceptron?
-The perceptron had limitations in learning properly due to its inability to handle complex tasks like pattern recognition and was unable to break the symmetry in weights.
Who is credited with inventing backpropagation?
-Backpropagation was invented by Paul J. Werbos, and it significantly improved the efficiency of neural networks.
What is backpropagation?
-Backpropagation is a method used to calculate the gradient of the loss function with respect to the weights of the network, enabling the network to learn from the errors in its predictions.
How does the presenter relate the learning process of a child to neural networks?
-The presenter uses the example of a child learning to distinguish between a dog and a cat based on features provided by family members, similar to how a neural network learns from input features.
What is the role of the input layer in a neural network?
-The input layer in a neural network is responsible for receiving the initial input features, similar to how our eyes receive visual information.
What is the significance of the line mentioned in the script?
-The line mentioned in the script represents the connection between neurons, which is crucial for the flow of information through the neural network.
What will be discussed in the upcoming videos?
-The upcoming videos will delve into the specifics of backpropagation, different types of activation functions, and the architecture of neural networks.
Who is Geoffrey Hinton and what is his contribution to deep learning?
-Geoffrey Hinton is a leading researcher in the field of deep learning. He has contributed significantly to the understanding and development of neural networks, particularly in the area of unsupervised learning.
Outlines
đ€ Introduction to Deep Learning and Neural Networks
The speaker introduces themselves and the purpose of the video, which is to teach deep learning through various use cases. They mention uploading code to GitHub and encourage viewers to follow along if they want to become proficient in deep learning or are considering a job change. The speaker discusses the history of deep learning, starting from the 1950s and 1960s when researchers aimed to mimic the human brain's learning capabilities. They explain that deep learning is a technique that models the human brain, and they briefly touch upon the limitations of early neural networks, the perceptron. They also introduce the concept of backpropagation, credited to Geoffrey Hinton, which revolutionized the efficiency of neural networks and led to their widespread use in applications today. The speaker promises to delve into the details of backpropagation in upcoming videos and concludes by discussing the basic architecture of a neural network, starting with the input layer, which corresponds to sensory organs like the eyes.
đšâđ« Understanding Neural Network Layers and Activation Functions
The speaker continues the discussion on neural networks, focusing on how information passes through the neurons after being received by the sensory organs, which is analogous to the input layer in a neural network. They emphasize the importance of the connections between neurons and hint at the role of activation functions, which will be explained in more detail in future videos. The speaker also mentions the significance of understanding these concepts for mastering deep learning. They reference a person named 'Cuba' who works at Google Brain and has contributed significantly to their learning. The speaker encourages viewers to subscribe to the channel, share the video, and expresses a desire to see them in the next video, ending with a blessing.
Mindmap
Keywords
đĄDeep Learning
đĄNeural Networks
đĄPerceptron
đĄBack Propagation
đĄFeature Extraction
đĄInput Layer
đĄActivation Functions
đĄSupervised Learning
đĄOutput Layer
đĄEnvironment
đĄYann LeCun
Highlights
Introduction to creating a playlist on deep learning.
The goal is to help viewers become proficient in deep learning and potentially switch jobs.
Deep learning mimics the human brain, inspired by research from the 1950s and 60s.
The human brain's capacity to learn from the environment is highlighted.
The limitations of the perceptron, an early neural network model, are discussed.
The invention of backpropagation in the 1980s revolutionized neural networks.
Backpropagation was developed by Geoffrey Hinton, a key figure in deep learning.
The video will cover the basics of neural network architecture.
An analogy of learning to distinguish between a dog and a cat as a child is used to explain neural networks.
The importance of feature extraction in neural networks is emphasized.
The role of the input layer in neural networks, representing sensory input, is explained.
The process of information passing through neurons to be learned by the network is described.
Activation functions and their role in neural networks will be discussed in upcoming videos.
The video encourages viewers to subscribe and share the content for those interested in deep learning.
A call to action for viewers to join the journey of learning deep learning through the playlist.
The video concludes with a blessing and an anticipation for the next video in the series.
Transcripts
hello my name is in this video I'm going
to create a all the this to show you the
arouse and fine dot
I'll be taking the youths kisses from
tagging and I will be solving all these
particular use cases I'll be uploading
all the code in the github so please
diligently follow this particular
playlist if you want to become a pro in
deep learning and you want to you know
if you're planning to switch your jobs
it will be dependent be helpful
so let me just discuss about en and CNN
RN because these are the base models for
super deep so deep learning is a
technique which basically mimics the
human brain so in nineteen fifties and
sixties our researchers and scientists
thought that can we make a machine lord
and work like how P human actually learn
and you know that we will be learning
from the basically from the environment
with the help of our brain you know
appeal has such a capacity that can long
do these things very quickly so so the
scientists and researchers thought can
we make the machine law in the same way
so that is where the deep learning
concepts came I thought late to the
mention of something called as neural
networks
the first simplest type of funeral it
was a funny thing called s percent from
concept rock but there were some
problems in the perceptron because the
perceptron or the neural network was not
able to learn very properly you know
because of the constant cycle of life
but later on in 1980s researcher or
scientist or a teacher is basically my
teacher so could have learned a lot of
things from them who is basically called
as just religion he invented the concept
of something called back propagation
because of this back propagation the a
and NLCS and arlynn became so efficient
that many companies are now using it
many people are using in many people
have developed a lot of application
which are efficiently working because of
Jeffrey Newton and his concepts of back
propagation so we will discuss all about
what what is back propagation as we go
in the upcoming videos but in this
particular video we just understand the
basic neural network architecture so to
begin with guys understand suppose I I
when I when I when I was a kid and I
first I saw or a dad or a dog or a cat
right at that time those inputs I was
not even to directly distinguish nobody
can correctly distinguish it is directly
seeing an object for the first time
because you will not know whether that
is a dog or a cat right but some of the
information will basically get in from
someone like suppose if I take that my
family members explain okay what is the
basic difference between dogs and cats
so they provided the features they told
I okay - has some pointy nose you know
oh sorry pointy years it has some
different types of ice colors and it is
usually small so this three information
and apart from seeing the color of the
cat and all those kind of information I
basically got interested I got my brain
one brain with respect to those features
now in able to distinguish what is a cat
and what is a talk so this way only we
can also make a neural network
architecture done you know by providing
those features ingesting those features
and each and again do not present in
that neural network will learn those
information
actually give you the output and with
the help of back propagation of the
train itself to learn new things so to
begin with see Hagen get ingesting the
features through my sensory organs
currently it is my eyes so that layer is
basically my input layer so here the
first layer of the neural network is
basically inputs which this will be and
this will be this because after this I
shall explain you in the next video of
this particular playlist or name then
remember as the information has the
feature passes through my eyes it goes
through all the neurons right get passed
to all the neurons so this features will
get passed to all that and this
particular line is very important right
so the value explaining what this line
actually specifies where everything will
be explained as we go ahead with this
medical playlist so this particular
information passes through all
this is specify whether this happens
what is the use of that activation will
be explained in I will also discuss all
the different kind of activation
functions also you understood about
perceptron just propagation don't worry
about the back propagation I will just
explain it but if you want to read about
just read that and just go and search in
the Google is the head of Google brains
he works in Google currently and a lot
of a lot of research because he has
dinner I have learned most of it equally
from this classes
Cuba not so I hope you like this
particular video make sure you subscribe
our channel if you like this particular
videos share with all your friends I'll
see you all in the next video
god bless you all
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