Complete Road Map To Prepare For Deep Learning🔥🔥🔥🔥
Summary
TLDRIn this video, Krishna provides a complete roadmap for learning deep learning, highlighting its importance in modern data science jobs. He explains the foundational concepts like neural networks, loss functions, optimizers, and activation functions. Krishna also discusses advanced topics such as artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN), and transfer learning. Viewers are encouraged to practice these techniques through projects, deployments, and cloud platforms. The video emphasizes hands-on learning and provides guidance on using popular libraries like Keras, TensorFlow, and PyTorch.
Takeaways
- 😀 Deep learning is essential for data science jobs today, alongside machine learning.
- 📚 The core of deep learning includes neural networks that mimic the human brain's learning process.
- 🧠 The foundational topics include neural networks, loss functions, optimizers, and activation functions, which are essential before diving into specific models.
- 🤖 Mastering artificial neural networks (ANN) is a critical first step, followed by deployment knowledge using tools like Flask and cloud platforms.
- 🖼️ Convolutional Neural Networks (CNN) are vital for handling image and video data, with key concepts like filters, strides, and layers to understand.
- 📈 Recurrent Neural Networks (RNN) and its variants like LSTM, GRU, and attention models are crucial for sequence-based tasks like natural language processing (NLP) and time series forecasting.
- 🛠️ Libraries such as Keras, TensorFlow, PyTorch, Hugging Face, and Ktrain are important tools for implementing deep learning models.
- 📦 Deployment skills, including Dockerization and cloud services, enhance practical knowledge and project completion.
- 🔍 For image-based tasks, CNN combined with transfer learning techniques (like ResNet and Inception) is highly recommended.
- 🏆 Staying updated with state-of-the-art models like GPT-3 is important, but the focus should remain on mastering the fundamentals of ANN, CNN, and RNN for interviews.
Q & A
What is the significance of deep learning in the current job market for data science roles?
-Deep learning is becoming increasingly important in the data science job market. While machine learning was sufficient to get jobs a few years ago, many companies now seek candidates proficient in both deep learning and machine learning to meet industry demands.
Why did deep learning come into existence, and what is its primary objective?
-Deep learning was developed to mimic the human brain, allowing machines to learn in a way similar to humans. The concept of neural networks, which underpins deep learning, was introduced by researchers like Geoffrey Hinton, particularly through the backpropagation algorithm.
What foundational concepts should be understood before diving into deep learning?
-Before learning deep learning, one should understand neural networks, loss functions, optimizers (like gradient descent and Adam), and activation functions (like ReLU, tanh, and sigmoid). Mastering the math behind these concepts is essential for grasping deep learning.
What is the role of optimizers in deep learning, and why is Adam Optimizer widely used?
-Optimizers, such as gradient descent and Adam, adjust the weights of a neural network during training to minimize the loss function. Adam is popular because it adapts the learning rate dynamically, improving the speed and accuracy of training compared to other optimizers.
How can you apply the knowledge of artificial neural networks (ANN) to real-world projects?
-Once familiar with ANN, you can solve machine learning problems like regression and classification. You should also learn how to deploy ANN models using web frameworks like Flask, and deploy them on cloud platforms like AWS, Azure, or Heroku for practical applications.
What are the key differences between artificial neural networks (ANN) and convolutional neural networks (CNN)?
-While ANN is used for general machine learning tasks, CNN is specialized for processing image and video data. CNNs use additional layers like convolution layers, which apply filters to images, enabling the network to detect patterns and features in visual data.
What are some advanced topics to explore in convolutional neural networks (CNN)?
-Advanced topics include transfer learning techniques like VGG, ResNet, and Inception. For more experienced learners, object detection algorithms like RCNN, Mask RCNN, SSD, and YOLO are crucial to understand for tasks involving object recognition in images.
What are the key concepts in recurrent neural networks (RNN) and NLP applications?
-In RNNs, concepts like LSTM (Long Short-Term Memory), GRU (Gated Recurrent Units), word embeddings, and sequence-to-sequence models are critical. NLP applications often involve tasks like translation, sentiment analysis, and text generation using models like BERT and Transformers.
Why is sequence-to-sequence modeling important in NLP, and how are encoders and decoders involved?
-Sequence-to-sequence models are important for tasks where the input and output are both sequences, such as language translation. Encoders process the input sequence, while decoders generate the output sequence, often improved by using attention mechanisms for better context understanding.
What are some resources or platforms recommended for learning and practicing deep learning?
-The speaker suggests using platforms like Keras, TensorFlow, and PyTorch for deep learning implementation. Additionally, blogs and resources like Keras' documentation, Hugging Face (for NLP), and practical projects using Docker and cloud platforms like AWS are recommended.
Outlines
🧠 Introduction to Deep Learning Roadmap
Krishna introduces the topic of deep learning preparation and addresses the growing importance of deep learning for data science jobs. He explains that deep learning is crucial alongside machine learning for landing data science roles, and then lays out a roadmap to learn deep learning. He emphasizes the foundational topics like neural networks, loss functions, optimizers, and activation functions, which form the basis for tackling deep learning techniques.
🔍 Core Concepts: Neural Networks and Optimization
This section focuses on understanding essential deep learning concepts such as weight initialization, hyperparameter tuning, and different optimizers like gradient descent, RMSprop, and Adam. Krishna explains how these concepts relate to training neural networks and discusses libraries like Keras, TensorFlow, and PyTorch for implementation. He also suggests learning Google Colab for utilizing GPUs, along with project deployment on cloud platforms like AWS, Heroku, and Azure.
🖼️ Convolutional Neural Networks (CNN) and Image Processing
Krishna introduces Convolutional Neural Networks (CNNs) and explains their application to image and video frame processing. He highlights the key mathematical concepts behind CNNs, such as filters, strides, and how images are reduced in size through convolutions. The section emphasizes mastering CNNs, transfer learning techniques like VGG16, and image classification projects, followed by advanced topics like object detection using algorithms like RCNN, SSD, and YOLO.
💬 Natural Language Processing (NLP) and Recurrent Neural Networks (RNN)
Krishna shifts focus to sequence data and NLP techniques. He explains RNN-based methods, including LSTM, GRU, and bidirectional LSTM, along with word processing techniques like word embeddings and attention models. He introduces libraries such as Hugging Face and KTrain for implementing NLP and discusses how to apply these techniques to tasks like sales forecasting and language translation using models like BERT and Transformers.
Mindmap
Keywords
💡Deep Learning
💡Neural Networks
💡Backpropagation
💡Convolutional Neural Network (CNN)
💡Optimizers
💡Loss Functions
💡Activation Functions
💡Transfer Learning
💡Recurrent Neural Network (RNN)
💡Object Detection
💡Deployment
Highlights
Deep learning is becoming essential for data science jobs, as companies increasingly look for both deep learning and machine learning expertise.
The main goal of neural networks in deep learning is to mimic the human brain and its learning processes.
Jeffrey Hinton's backpropagation algorithm played a pivotal role in the development of deep learning techniques.
A strong foundation in neural networks, loss functions, optimizers, and activation functions is critical before diving into advanced deep learning concepts.
Important optimization techniques include gradient descent, stochastic gradient descent (SGD), Adagrad, RMSprop, and Adam Optimizer, with Adam being the state-of-the-art technique.
Artificial neural networks (ANNs) can solve similar problem statements as machine learning but with the added benefit of deeper learning capabilities.
Deploying deep learning projects using frameworks like Flask, and deploying them on cloud platforms (Heroku, AWS, Azure) is a recommended practice.
Hyperparameter tuning, including weight initialization and deciding hidden layers and neurons, is crucial for ANN performance optimization.
Keras, PyTorch, and TensorFlow are recommended libraries, with Keras being a simple and easy-to-use choice, especially with TensorFlow 2.0.
Convolutional Neural Networks (CNNs) are primarily used for image and video processing and include layers like convolution and fully connected layers.
Understanding CNN concepts like filters, strides, and image reduction is essential, as well as the math behind them.
Transfer learning techniques, such as VGG16, ResNet, and InceptionV3, are highly beneficial for improving model performance in specific tasks.
Object detection algorithms, including RCNN, Mask RCNN, SSD, and YOLO, are essential for more advanced applications, especially for those with 2-3 years of experience.
Recurrent Neural Networks (RNNs) and their variants like LSTM, GRU, and attention models are vital for handling sequence-to-sequence data, such as natural language processing (NLP) and time series forecasting.
Hugging Face and KTrain libraries are recommended for practical applications in NLP, especially when working with transformers like BERT.
Transcripts
hello all my name is Krishna and welcome
to my YouTube channel so guys many of
you are actually requesting to upload a
video on complete road map to prepare
deep learning so in this particular
video I'll be discussing about that I'll
be telling you the path that you should
actually take to learn deep learning in
a proper way so that you'll be able to
cover almost most of the things so that
it'll be helpful for you for interviews
and one more thing guys yes deep
learning is becoming much more popular
because there were many questions put up
by many of my subscribers saying that is
machine learning enough to get into jobs
with respect to data science I'll say
guys nowadays it will be difficult
because companies are looking for both
deep learning and machine learning
techniques it is good to have both of
them and that is what is the trend if
you go three years back I think I I
would definitely say that if you know
machine learning you'll be able to get
the jobs itself so let's understand this
road map after making you understand
I'll also be showing you from where you
can learn all these things yes some of
the part of this is actually remaining
to be uploaded in my playlist but it
will be soon getting uploaded till you
learn all the things till then all the
things will get actually uploaded so let
be let's begin guys now one of the most
important thing is that why did deep
learning come into existence and what is
the main aim of the neural networks
which is basically used in deep learning
it is used to mimic the human brain so
scientist researchers actually thought
that can we make the machine learn in
such a way that like how we human being
learn right so because of that they came
up with the concept of perceptron neural
networks and it was Mr Jeffrey Hinton
right because of him because of his uh
paper that was on back propagation
algorithm that led to the invention of
all these techniques that are right now
yes so let us go ahead and try to start
with how we should start with deep
learning itself now guys in the base
over here in the bottom part you you can
see that I've actually written
introduction to neural network then I
have written loss function optimizes
gradient descent stochastic gradient
descent adagrad RMS prop and Adam apart
from this there are also activations
functions like reu tan sigmoid
activation function so that all are the
base for everything that we will try to
learn because on training right suppose
if we are training our images in
convolution neural network indirectly
you'll be using some kind of optimizers
you'll be using some kind of loss
functions apart from that you'll also be
using some kind of activation functions
so in short to begin with guys all the
topics that I have actually mentioned
over here in the base are pretty much
important you need to learn this you
need to know the maths how it actually
works because implementation of this is
just like hardly a single line of code
and training it will automatically take
until you get a optimal Minima right a
global Minima so inside this please make
sure that you learn about what is the
how does the neural network work where I
talk about inputs weights bias you know
because we update the weights in such a
way that once that particular uh you
know where we will be actually getting a
global Minima till that point the
training will be going on then apart
from that you also have loss functions
I'll be showing you from where you can
learn loss functions there are amazing
amazing blogs on Keras right they are
amazing amazing loss functions like uh
categor cross entropy cross entropy
binary cross entropy and many more
things which we'll be going through then
you have optimizers like gradient
descent stochastic gradient descent this
gradient descent if you understand I
think stochastic grading descent will
also become easy because there is some
minor change there was some disadvantage
in grading descent so they came up with
SGD then they came up with adagrad then
they came up with RMS prop and then
finally which is a state of art which is
called as Adam Optimizer nowadays
everybody's using Adam Optimizer because
it is nothing but it is it is also able
to change the momentum that is basically
your learning rate as your training is
actually going on so pretty amazing task
and these all are common to all the
things all the three pillars that we
have I have actually drawn over here
okay so please make sure that before
you're starting with Ann before you're
starting with convol neural network RNN
you have you'll be perfect with all
these Concepts and all these concepts
are available in my playlist okay and
again I'll go through the playlist at
the last uh then we basically go to the
first pillar which is artificial neural
network once you're able to understand
all these things I think directly you'll
be able to implement artificial neural
network now in machine learning you will
be having a data set right like a
regression or classification problem
that similar problem statement you can
solve with the help of artificial neural
network okay now when you implement
artificial neural network you can do it
in your local machine you can uh you
should also have the knowledge of Google
collab because there there'll be a usage
of gpus you'll get an idea how to work
in the GPU how to execute the programs
and see that how the training actually
happens right after learning artificial
neural network don't stop over there
guys try to deploy some projects by
using some web Frameworks like flask uh
in the heru cloud in the uh AWS Cloud
Azure Cloud any type of cloud that you
want again you can check my deployment
playlist you should also try to uh
dockerize this whole uh model and try
dockerize this whole web application try
to deploy that that will also be a very
very good idea now as I said you that
whatever problem statement that you're
solving with machine learning can also
definitely solve with artificial neural
network there are also some Concepts in
artificial neural network like weight
initialization how do you do the weight
initialization what are the different
parameters how you can actually perform
hyperparameter tuning with respect to
artificial neural network right like how
do you decide like how many number of
hidden layers are there how many number
of neurons should I take in the hidden
layer that all can be done by the help
of hyper parameter tunings and for that
you can use caras tuner it is there is
also something called as Auto caras that
also you can actually use again
everything has been uploaded in my
playlist I'll go through the playlist at
the last so once you're comfortable with
this the type of libraries that you can
use is py toch caras tensorflow okay I
would suggest you to go with Keras
because it'll be very very easy if you
are trying to use Kaz with tensorflow
2.0 because Kaz is already included in
that okay so go with that and also pytor
videos also I'm trying I've been
uploading in the other playlist now
coming to the second pillar now with
respect to the second pillar you have
convolution neural network again the
important Concepts like loss function
optimizer like gradient descent SGD
adagrad arm fromom Adam uh activation
functions will be used the same things
will be used as I said in the
convolution neural network on top of
that in converstion neural network you
have an additional layer which is called
as convolution you need to understand
how does this convolution actually work
and remember guys whenever we are
talking about convolution neural network
we are talking about images we are
talking about video frames so those kind
of inputs gets processed with the help
of conation neural network that is
pretty much simple and that is the thing
that you need to understand okay almost
remaining all the things will be working
like the Ann because at the last layer
you'll be seeing that there will be
fully connected layer at the end of the
convolution neural network and with
respect to that you can also solve a
image classification problem okay now in
convolution neural network please make
sure that you understand all the math
concepts with respect to convolution
layer there you'll be seeing something
called as filters what are filters what
are strides what is the formula how the
image will get reduced when you apply a
filter of 3 cross 3 5 cross 5 if your
image is of 224 224 what is the next
layer that will actually happen all
those explanation is again given in my
uh videos guys it is pretty much
important to understand okay apart from
that guys I've also started with the
transfer learning techniques and I have
also uploaded a lot of videos I've also
done end to-end projects with respect to
transfer learning like vg6 Al xet you
have Inception V3 you have reset
everything so all those videos are
available in my playlist you just have
to follow them guys okay please make
sure that you follow them to do this now
as a beginner just focus on these two
things don't worry about the object
detection as a beginner but if you are
around 2 to 3 years experience you
should also start with something called
as object detection in object detection
first of all start with rcnn then you
have M rcnn then you have SSD single
shot detector right then you have the
YOLO algorithm so YOLO is pretty much
handy for the object detection it is
also pretty much fast when compared to
rcnn and master RC Master rcnn is like
object detection they'll also be doing
masking a lot of research are also going
on with respect to this so these all
topics I've actually mentioned guys and
again I'm not telling you as a fresher
you should directly go understanding
object detection so you start with
convol neural network then go with
transfer learning techniques once you
are very good with transfer learning
techniques try to do some kind of end to
endend projects where you are able to do
some kind of applications like image
classification by taking some many
number of images try to create a front
end try to create a flash framework try
to create with the help of apis how do
you can do the deployment everything so
whole web application once you're able
to create again do the deployment in
different platforms like heru AWS Azure
and just try to check that how that
particular application actually works
okay and once you comfortable with all
the transfer learning techniques then
move into object detection and try to
cover this uh five algorithms or four
algorithms that I have actually
mentioned because these are some amazing
algorithms itself right now once this is
done this is your second pillar again
the most popular pillar of out of this
I'll I'll suggest that it is the NLP
part or you can say the RNN part the
most of the applications are with
respect to sequence sequence to sequence
data whenever your input data are like
sequence to sequence like uh you have
sentence you have uh sales forecasting
this all kind of use cases are there
again guys uh with respect to this
you'll be H using mostly RNN again the
base RNN you need to understand that is
recurrent neural network on top of that
you have lstm Gru B directional lstm you
have word pre-processing techniques like
word embedding word Toc then you have
encoder and decoders these are like
sequence to sequence model uh if you
remember guys there is something called
as neural language translation all those
applications are actually done by this
encoders and decoders you have to learn
about attention models what is the
advantages of attention models then we
are coming to Transformers B right so
Transformers again I've taken the live
session only the things is about bird
but is also Prett pretty much amazing
and if I talk about bird guys all the
NLP transfer learning techniques can be
actually done with the help of bir and
Transformers right and the libraries
that are actually used are called as
hugging face and K train now you will be
thinking Krish what all videos you have
not uploaded guys I need to upload this
object detection all the videos and I
need to upload uh bir theoretical videos
and with the help of hugging face and KR
I'll be showing you a lot of practical
application only this two and this two
are remaining guys remaining all the
videos has been uploaded and till till
you prepare till all this particular
Concepts I will be uploading all these
topics very soon okay I just need some
amount of time I've been preparing
materials from hugging face it will take
time because I really want to show you
uh that is the thing that is not
available in the documentation of
hugging face instead I'll try to show
you so that I'll be training that thing
in front of my GPU and uh I'll also be
showing you some custom technique in hug
face and K train Library again uh I'm
also planning to make some deployment
projects end to end so that you'll be
able to accommodate all these things now
these all are pretty important you have
to go again the base and and the most
favorite interview questions will be
revolving around this base things like
optimizers you know loss functions
activation functions things about
conition neural network again they
directly will not ask you about object
detection you know they'll just ask you
that whether you know or not give us
some basic idea something like that but
mostly they'll be focusing on this three
pillars this three base pillars that is
a Ann CNN and RNN they may talk about
bidirectional but out of this uh NLP
part which is this one right is much is
becoming much more popular second I'll
say that it is uh conol neural network
then you have Ann apart from this guys
in conversation neural network there is
also a separate topic which is called as
computer vision uh computer vision try
to learn in such a way that you'll be
able to capture the frames from your
webcam and try to use it and detect it
through your uh convention neural
networks something like it may be face
recognition it may be different kind of
image classification live image
classification and many more things like
you you you'll be creating some bounding
boxes and many more things and those
kind of video has also been uploaded in
my playlist so this in short is the
whole road map to prepare the Deep
learning again remember the important
libraries are py Tor K tensive flow then
you have having phase then you have
catering libraries right now let's go
ahead and try to I'll try to show you
from where you can learn from my
playlist uh so let's go over here guys
uh so yes uh this is my whole deep
learning playlist you'll be able to see
that I have uploaded uh somewhere around
uh 53 videos and this actually covers
Alex vet transfer learning techniques
till Transformers I have explained in
depth yes there is a plan that I will
start uploading the Practical videos
very soon now I have planned it I'm
preparing the material as soon as that
will be available it will be available
to you all also and I'll try to upload
it as soon as possible guys the other
thing that you should follow is
basically your K blog this K blog is
pretty much amazing if you really want
to know about losses you can click away
what are the different loss technique
like with regression losses will be mean
square error mean absolute error I think
we have also done this in machine
learning right in uh classification
problem you'll be having binary cross
entropy then category cross entropy
sparse category cross entropy poison
class and all these different different
techniques are there okay uh apart from
this you also have like model API so all
the different kind of model apis will be
used over here so here you have
sequential model and everything I've
explained in my uh this whole playlist
guys we I have included both practical
and theoretical implementation like you
here you can see how we can perform word
embedding implementing word embedding
using kasas develop your neural networks
kaggle fake news classifier stock price
prediction forecasting right uh remember
with the help of RN you'll also be able
to do the forecasting because it deals
with sequences of data and remember guys
only two main things this is the
playlist that you should follow this is
the loss function uh I mean this is the
Keras uh block page that you should
actually follow uh the transfer learning
techniqu is also given if I click on K
application here you'll be able to see
all the transfer learning techniques
that it supports you can actually reuse
any of these techniques and train your
model as soon as possible right so these
all are some stateof art algorithms
you'll be able to see that how many
parameters are there uh like recently if
you know about gpt3 it has 175 billion
parameters I guess now here if you take
an example of vj6 it has 138 million
parameters so billion and million right
it's a huge Gap so it is said that uh
gp3 is one of the state of the art
algorithm and probably it should be
available I don't know whether it'll be
available to everyone or not I've still
requested for the API but I've not got
it yet so that I start exploring things
so this was the complete road map guys
uh now one important thing is that I may
teach you any number of times I may
teach you with different different
things I may teach you everything as
such but the main thing is that you have
to learn things you have to try
different different things again I'm
tell telling you these all tutorials
that I have uploaded may not be just
sufficient to just be an expert in a
company that you'll be able to work in
any uh computer vision or deep learning
application but it is up to you after
having this base strong I I've uploaded
all the videos which will actually make
your base strong once that is strong you
can on top of that you can apply any
application right when I talk about
Jeffrey Hinton who's the creator of this
back propagation who invented this idea
of back propagation algorithm now every
people are actually using it researchers
are using and they're creating something
amazing things right similarly all the
base part of all these particular videos
has been uploaded for you all you just
have to practice try to do some more
additional things on top of that work on
audio classification work on different
different things and try to make some
good application from your own it will
not be possible for me that you have an
idea you'll just ask me chrish how to do
it because I also have to explore it
right I'm also a common man guys I may
be knowing this base right but on top of
it if I really want to implement
anything I also have to put that much
effort so that is the reason why I tell
you that you have to practice things you
you don't have to leave those things
whenever you have an interest on
something start working on it okay if
you have some amazing ideas start
working on it because the base is ready
for you so I hope you like this
particular video please do subscribe to
the channel if you have not already
subscribed and I'll see you all in the
next video have a great day thank you
and all bye-bye
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