To start this course, we shall go from the very basics.
So start by understanding tenses and variables and how to use them with TensorFlow.
You could see here would go from the very basics like initialization and casting right up to more advanced
tensor types like the sparse rect and string tenses.
Then we'll also look at the variables and how to load and safe models in TensorFlow.
Now from here we shall dive into this car prediction project and this competition project.
Actually, you will learn how to build a simple linear regression model.
So you first of all, start by preparing your data, then building out this model, creating some last
function, and then train your own model.
Then once the model is trained, you dive into performance measurement.
We'll then see how well our model performs with the validation and testing.
And then based on this performance, we'll carry out some corrective measures.
Now we've looked at how to build a simple linear regression model for the next project, which I'll
be working on.
That is on malaria diagnosis.
We shall understand how the convolutional neural networks work.
So from your going from this fully connected neural networks, we'll now move to the convolutional neural
networks, which are greatly used in the domain of computer vision.
So just as before prepared the data, then we build the model.
With TensorFlow, we have the similar training process.
We evaluate the model, and then we load and save, or we learn how to save and then load our model
to Google Drive.
Now, from this step, that is from training with a convolutional neural network, we will then learn
how to build even more advanced models with TensorFlow.
So your will build this convolutional neural network with the sequential API, and then you will use
more advanced methods like the functional models and even models of class.
And then we'll take a deep dive at the model evaluation.
That is, we'll look at even more better methods of evaluating our model, like the precision recall
accuracy coefficient metrics And the RC curve from here would improve on our model performance by seeing
how to leverage certain functionalities which come with TensorFlow, like the callbacks, which itself
also has other specific callbacks like learning rate scheduling, model, check pointing, and even
early stopping.
Then from here we shall see how to tackle the problems of overfitting and undefeated.
First of all, we'll start by understanding what those problems are and then we'll see how to solve
or tackle these problems.
Now, talking about overfitting, one very common means we generally use to tackle this problem is data
augmentation.
And so in the section we shall dive into different data augmentation methods we could use with TensorFlow.
And that is going from the very basic methods which are already available in TensorFlow image package
right up to the mix up and current mix augmentation strategies.
Now, given that albumin stations is a data augmentation forecast library, we shall see how to make
use of it and how to integrate this with TensorFlow.
Now from here, we'll look at other advanced topics like how to create custom losses and metrics, how
to switch between the eager and graph modes and understanding how this work.
And from here, also creating our own custom training loops, which are very important when we want
to build some customized systems.
From here we'll move on to the tensor bot integration.
So Tensor Bot is this tool which permits us not only log our data but also view model graphs carry out
hyper parameter tuning.
Thus peak out.
Help us pick out the best hyper parameters for training setup and also carry out profiling and certain
visualizations which are important in helping us take certain strategic decisions.
Then from here we shall focus on weights and.
I asked this and we're symbiosis is this machine learning operations tool which helps us with experiment
tracking hyper parameter training to dataset versioning and even model version.
And so with this tool, we are not only going to train our models, but we are also going to be able
to keep track of our model training process.
Now, at this point, we've looked at all the different topics, but with this project that is our malaria
diagnosis.
So we will then move on to human emotion recognition and we'll then build our model fully train this
model, implemented augmentation and also see how to integrate TensorFlow records.
But given that we've been working with very basic convolutional neural networks so far, we are then
going to look at more advanced and modern convolutional neural networks like the nets, big nets, rest
nets, mobile nets and efficient nets.
And from here we will see how these models outperform the very basic model we had been working with
so far.
Then we'll also dive into this very interesting topic of trans fat learning where instead of training
our models from scratch will make use of already pre trained convolutional neural networks to not only
speed up our own training process, but also get much better results.
Then after getting this better results is going to be important for us to understand what goes on in
the black box.
By black box here we simply mean the convolutional neural network.
So we'll look at two methods, thus the visualization of the intermediate layers and also the grad cam
method.
Then we'll look at class imbalance and assembling.
This is actually tackling class imbalance and how to bring together several models to produce one model,
and then that will be it for the calf nets.
We will now look at the even more recent models.
That's the transformers in vision.
So so far before this time, the transformers were mainly used in natural language processing, but
now we have transformers which are also used in computer vision.
And so in the section we'll see how the whites work.
We're going to build a bit from scratch.
We're also going to fine tune a bit from Haugen phase, and then after evaluating the model, we'll
see how it outperforms even the best components We had already worked with our seen right here.
So that will be it for the whites.
We are going to look at more data efficient transformers and the screen transformers.
Then from here we'll see how to deploy our model.
So we create end to end systems which actually work.
So start by understanding quantization.
And the reason behind this is simply because we could make our models much smaller and faster without
losing much model performance.
So that's what we're going to do.
We're going to see how to convert our model into an Onyx model that's from TensorFlow to Onyx, how
to quantized the Onyx model, how to implement quantization, our training, how to also convert our
model to TensorFlow Lite.
And then from here, we're ready to deploy this model.
But before deploying, we'll understand how APIs work.
We're going to build our own API with fast API Python library, then we'll deploy our API to the cloud.
And finally, we're going to carry out some load testing to see how many users could make use of our
API at once.
So now that we're able to deploy our model to the cloud, we are then going to move to another project
that's object detection.
And it should be noted that in this section we are going to build our own YOLO model from scratch.
So we'll build this model, train it, and then we will test it for you to see how we could carry out
object detection from scratch.
The next project we shall work and will be that of image segmentation, and we shall build a simple
unit model to solve this problem.
And the section will implement techniques like data augmentation and class weighting to improve our
model's performance.
And then we'll move on to the people's counting problem where we shall make use of the CSR net to build
a working system which gets inputs of people like this.
And.
Able to count the number of people we have in year from year.
We will now finally have this project on Image Generation.
It's actually a matter of two projects.
The first one is Digits Generation with VA is that variational auto encoders and then for the next that
is fifth generation with Gans would first of all start by understanding how the Gans work and then we'll
make use of this Gans that specifically the this again to generate phase images.
This course is constantly be updated.
So once you're done with this course material, you could take out five different projects which you
build end to end and then share that with the world.