Complete Road Map To Prepare For Deep Learning🔥🔥🔥🔥

Summary

In 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

00:00

🧠 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.

05:00

🔍 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.

10:02

🖼️ 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.

15:02

💬 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.

Keywords

💡Deep Learning

Deep Learning is a subset of machine learning that focuses on artificial neural networks and deep neural networks. It is a method of teaching computers to do what comes naturally to humans: learning by example. In the video, the speaker discusses the importance of deep learning and how it has become a crucial skill for data science jobs, indicating that companies are increasingly looking for professionals with expertise in both deep learning and machine learning.

💡Neural Networks

Neural networks are a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling, or clustering raw input. The video emphasizes the foundational role of neural networks in deep learning, highlighting that they are used to mimic the human brain's learning process.

💡Backpropagation

Backpropagation is a method used to calculate the gradient of the loss function with respect to all the weights in the network, which is the key to learning in neural networks. The script mentions Mr. Jeffrey Hinton's paper on the backpropagation algorithm, which is foundational to the development of many deep learning techniques discussed in the video.

💡Convolutional Neural Network (CNN)

A Convolutional Neural Network is a type of deep learning algorithm commonly applied to analyze visual imagery. The video script discusses CNNs as a pillar of deep learning, emphasizing their use for processing image data, such as for image classification, and mentions the importance of understanding the mathematical concepts behind the convolution layer.

💡Optimizers

Optimizers in the context of neural networks are algorithms that help in adjusting the weights of the network to minimize the loss function. The video mentions several types of optimizers, including gradient descent, stochastic gradient descent (SGD), adagrad, RMSprop, and Adam, which are crucial for the training process of neural networks.

💡Loss Functions

Loss functions are a crucial part of training neural networks, as they measure how well the model is performing and how much it is missing its target. The script discusses different types of loss functions like categorical cross-entropy, which are used to train models in deep learning.

💡Activation Functions

Activation functions are used to introduce non-linearity into the model, allowing neural networks to learn arbitrary decisions boundaries. The video script lists functions like ReLU, tanh, and sigmoid as examples of activation functions that are fundamental to how neural networks process information.

💡Transfer Learning

Transfer learning is a method where a pre-trained model is adapted to a target task. The video script mentions transfer learning as a technique used in deep learning, particularly in the context of CNNs, where pre-trained models like VGG16, ResNet, and Inception V3 are used to boost performance on new tasks.

💡Recurrent Neural Network (RNN)

Recurrent Neural Networks are a class of neural networks that are designed to work with sequential data, like time series or natural language. The video discusses RNNs as a key component of deep learning for sequence-to-sequence data problems, such as natural language processing.

💡Object Detection

Object detection is a computer vision technique that not only classifies the object in an image but also determines the object's location. The video script suggests that while object detection is an advanced topic, it is an important area of deep learning, mentioning algorithms like R-CNN, YOLO, and SSD.

💡Deployment

In the context of machine learning and deep learning, deployment refers to the process of putting a trained model into a production environment where it can make predictions. The video script stresses the importance of deployment skills, suggesting that learners should practice deploying models using web frameworks and cloud platforms.

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