Real World Applications Of Deep Learning Training Ppt

Slide 1

This slide lists applications of Deep Learning in the real world. These include detecting developmental delay in children, colourisation of black & white images, adding sound to silent movies, pixel restoration, and Sequence Generation or Hallucination.

Slide 2

This slide states that one of the finest applications of Deep Learning is in the early detection and course correction of infant and child-related developmental disorders. MIT's Computer Science and AI Laboratory and Massachusetts General Hospital's Institute of Health Professions have created a computer system that can detect language and speech disorders even before kindergarten, when most cases typically emerge.

Slide 3

This slide describes image colorization i.e. the technique of taking grayscale photos and producing colorized images that represent the semantic shades and tones of the input. Traditionally, this technique was carried out by hand and required human labor. Today, however, Deep Learning Technology is used to color the image by applying it to objects and their context within the photograph. 

Slide 4

This slide states that to identify acceptable sounds for a scene, a Deep Learning model prefers to correlate video frames with a database of pre-recorded sounds. Deep Learning models then use these videos to determine the optimal sound for the video.

Slide 5

This slide discusses that in 2017, Google Brain researchers created a Deep Learning network to determine a person's face from very low-quality photos of faces. “Pixel Recursive Super Resolution” was the name given to this approach, and it considerably improves the resolution of photographs, highlighting essential features just enough for identification.

Slide 6 

This slide showcases that sequence generation or hallucination works by creating unique footage by seeing other video games, understanding how they function, and replicating them using Deep Learning techniques like recurrent neural networks. Deep Learning hallucinations can produce high-resolution visuals from low-resolution photos. This technique is also used to restore historical data from low-resolution quality photographs to high-resolution images.

Slide 7

This slide describes that the Deep Learning approach is incredibly efficient for toxicity testing for chemical structures; specialists used to take decades to establish the toxicity of a particular structure, but with a deep learning model, toxicity can be determined quickly (could be hours or days, depending on complexity).

Slide 8

This slide showcases that a cancer detection Deep Learning model contains 6,000 parameters that might help estimate a patient's survival. Deep learning models are efficient and effective for breast cancer detection. The Deep Learning CNN model can now identify and categorize mitosis in patients. Deep neural networks aid in the study of the cell life cycle.

Slide 9

This slide states that based on the dataset used to train the model, Deep Learning algorithms can forecast buy and sell calls for traders. This is beneficial for short-term trading and long-term investments based on the available attributes.

Slide 10

This slide describes that Deep Learning algorithms classify consumers based on prior purchases and browsing behavior and offer relevant and tailored adverts in real-time. We can see this in action: If you search for a particular product on a search engine, you will be shown relevant content of allied categories in your news feed also.

Slide 11

This slide showcases that Deep Learning offers a promising answer to the problem of fraud detection by allowing institutions to make the most use of both historical customer data and real-time transaction details gathered at the moment of the transaction. Deep Learning models may also be used to determine which products and marketplaces are more vulnerable to fraud and to be extra cautious in such circumstances.

Slide 12

This slide states that seismologists attempt to forecast the earthquake, but it is far too complicated. One incorrect prediction costs both the people and the government a lot of money. There are two waves in an earthquake: the p-wave (travels quickly but does less damage) and the s-wave (travels slow but the damage is high). It isn't easy to make judgments days in advance, but using deep learning techniques, we can forecast the outcome of each wave based on prior data crunching and experiences. This may take hours, but it is rapid enough to serve as a useful warning that can save lives and prevent damage.

Slide 13

This slide gives an overview of Deep Fakes, which refers to modified digital material, such as photos or videos, in which the image or video of a person is replaced with the resemblance of another person. Deep Fake is one of the most severe concerns confronting modern civilization.

Instructor Notes: 

In 2018, a spoof clip of Barack Obama was made, using phrases he never spoke. Furthermore, Deep Fakes have already been used to distort Joe Biden's footage showing his tongue out in the US 2020 election. These detrimental applications of deepfakes can significantly influence society and result in the spreading of false information, particularly on social media.