|Python is a popular platform used for the research and development of production systems is a vast language with several modules, packages, and libraries that provides multiple ways of achieving a task. Python and its libraries like NumPy, SciPy, Scikit-Learn, Matplotlib are used in data science and data analysis. Python is a popular platform used for research and development of production systems||PyTorch is an open-source machine learning library used primarily for computer vision and natural language processing applications.PyTorch is a strong player in the field of deep learning and artificial intelligence, and it can be considered primarily as a research-first library that heavily leverages Gpu Computation|
Section of Data Science
Data analysis is a process of inspecting, cleansing, transforming and modeling data to discover useful information, informing conclusions and supporting decision-making.
Statistics is used to process complex problems in the real world so that Data Scientists and Analysts can look for meaningful trends and changes in Data.
Software Development is the process of conceiving, specifying, designing, documenting,texting and bugs fixing.
We Implemented required data science algos using Python and deep learning using Pytorch.
Why and When to make Machine Learn?
Lack of human expertise- The very first scenario in which we want a machine to learn and take data-driven decisions, can be the domain where there is a lack of human expertise. The examples can be navigations in unknown territories or spatial planets.
Dynamic scenarios- There are some scenarios which are dynamic in nature i.e. they keep changing over time. In case of these scenarios and behaviours, we want a machine to learn and take data-driven decisions. Some of the examples can be network connectivity and availability of infrastructure in an organization.
Difficulty in translating expertise into computational tasks- There can be various domains in which humans have their expertise,; however, they are unable to translate this expertise into computational tasks. In such circumstances we want machine learning. The examples can be the domains of speech recognition, cognitive tasks etc.
Machine Learning Types:
- Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning.
- Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to various fields.
- Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems.
CONVULATIONAL NEURAL NETWORK
Convolution networks are simply neural networks that use convolution in place of general matrix multiplication in at least one of their layers.
- LAYERS:- Convolutional layer -Pooling layer Fully Connected Layer
“Anime Faces Generation using GAN’s”
What is a GAN? A generative adversarial network (GAN) is a combination of a generator and a Discriminator:-
- The generator learns to generate plausible data. The generated instances become negative training examples for the discriminator.
- The discriminator learns to distinguish the generator’s fake data from real data. The discriminator penalizes the generator for producing implausible results.
- When training begins, the generator produces obviously fake data, and the discriminator quickly learns to tell that it’s fake.
- What do we need? Well, Since we are making this program in Pytorch we need Pytorch and Google collab to write our program code
Steps Involved in a GAN :-
The generator part of a GAN learns to create fake data by incorporating feedback from the discriminator. It learns to make the discriminator classify its output as real.
- Generator training requires tighter integration between the generator and the discriminator than discriminator training requires. The portion of the
GAN that trains the generator includes:
- random input.
- generator network, which transforms the random input into a data instance.
- discriminator network, which classifies the generated data.
- discriminator output.
- generator loss, which penalizes the generator for failing to fool the discriminator.
The discriminator in a GAN is simply a classifier.It tries to distinguish real data from the data created by the generator. It could use any network architecture appropriate to the type of data it’s classifying. Discriminator training:
- The discriminator classifies both real data and fake data from the generator.
- The discriminator loss penalizes the discriminator for misclassifying a real instance as fake or a fake instance as real.
- The discriminator updates its weights through backpropagation from the discriminator loss through the discriminator network.
Outcome of the project?
This is how the output of a well trained Generator network over time looks like that produces almost real like images that are detected by the discriminater as Real, who heavily penalizes generater on producing fake images.