Engineering with the brain !! Deep Learning

Let’s Learn “How to Learn” … And that’s how series of an algorithms in the name of Neural Network came into life..

What is Neural Network?

Simple Neural Network

Neural networks are a series of algorithms that mimic the operations of a human brain to recognize relationships between vast amounts of data.

A neural network works similarly to the human brain’s neural network. A “neuron” in a neural network is mathematical function that collects and classifies information according to a specific architecture. The network bears a strong resemblance to statistical methods such as curve fitting and regression analysis.

Hidden layers fine-tune the input weightings until the neural network’s margin of error is minimal. It is hypothesized that hidden layers extrapolate salient features in the input data that have predictive power regarding the outputs.

Applications of Neural Network

Today, an artificial neural networks are used for solving many business problems such as sales forecasting, customer research, data validation, and risk management.

ANN’s can be used for a range of tasks.

These include analyzing data, transcribing speech into text, powering facial recognition software , or predicting the weather.

By adopting Artificial Neural Networks businesses are able to optimize their marketing strategy.

Using an Artificial Neural Network , system can consistently learn and improve themselves.

Artificial Neural Networks are being used by the pharmaceutical industry in detecting disease and finding different way of treatment..

Deep learning and Artificial Neural Networks applications are powering systems capable of detecting all forms of financial fraud.

Industry use cases of Artificial Neural Network

Today , Artificial Neural Network is used in each and every industry . As it plays vital role in predicting the demands and solving real life problems. So let’s see how different industries are using ANN and growing day by day.

LinkedIn using Deep Learning Artificial Neural Network

1. To perform personalization at the member level, they need machine learning algorithms that can understand content in a comprehensive fashion.
2. Combining machine learning with member intent signals, profile data, and information about a member’s network, they can extensively personalize the recommendations and search results for their members.
3. They heavily leverage deep learning, a branch of machine learning that automatically learns complex hierarchical structures present in data using neural networks with multiple layers, to understand content of all types.
4. Deep learning methods can also capture nonlinear patterns in both temporal, sequential, and spatial data in an effective fashion.
5. They employ three broad classes of deep learning methods for most of our natural language processing and computer vision tasks: the aforementioned LSTM, CNNs, and sequence-to-sequence models.
6. They also employ canonical multi-layered perceptrons wherever necessary for some supervised learning tasks.

Tesla using Deep learning to train their neural network.

1. Tesla applied cutting-edge research to train deep neural networks on problems ranging from perception to control.
2. Their per-camera networks analyze raw images to perform semantic segmentation, object detection and monocular depth estimation.
3. Their birds-eye-view networks take video from all cameras to output the road layout, static infrastructure and 3D objects directly in the top-down view.
4. Their networks learn from the most complicated and diverse scenarios in the world, iteratively sourced from our fleet of nearly 1M vehicles in real time.
5. A full build of Autopilot neural networks involves 48 networks that take 70,000 GPU hours to train . Together, they output 1,000 distinct tensors (predictions) at each timestep.

They developed the core algorithms that drive the car by creating a high-fidelity representation of the world and planning trajectories in that space.

In order to train the neural networks to predict such representations, algorithmically create accurate and large-scale ground truth data by combining information from the car’s sensors across space and time.

Google using Deep Learning

Google has been a powerful force in championing the use of deep learning — a technology now so prevalent in cutting edge applications that its name is pretty much synonymous with artificial intelligence. There’s a simple reason for this — it works.

1. Putting deep learning to work has enabled data scientists to crack a number of difficult cases which had proved challenging for decades, such as speech and image recognition, and natural language generation.
2. In 2014, Google acquired UK based deep learning startup Deep Mind.
3. Deep Mind pioneered work in connecting existing machine learning techniques to cutting edge research in neuroscience, leading to systems which more accurately resembled “real” intelligence (i.e. brains).
4. Deep Mind was responsible for the creation of Alpha Go, which used video games, and later the board game Go, to demonstrate the ability of their algorithm to learn how to carry out a task and become increasingly good at it.
5. Google uses deep learning today on its core services is to provide more useful recommendations on YouTube and other of its services.

Artificial Neural Network In Healthcare

Before 2006, the main successes of ANNs were found in areas like speech processing and image processing. Today, as new technologies emerge, capable of changing the way that we approach neural networks in the first place — it’s worth noting that there may be numerous new options for changing the industry.

Today, the possibilities for Neural Networks in Healthcare include:

• Diagnostic systems — ANNs can be used to detect heart and cancer problems, as well as various other diseases informed by big data.
• Biochemical analysis — ANNs are used to analyze urine and blood samples, as well as tracking glucose levels in diabetics, determining ion levels in fluids, and detecting various pathological conditions.
• Image analysis — ANNs are frequently used to analyze medical images from various areas of healthcare, including tumor detection, x-ray classifications, and MRIs.
• Drug development — Finally, ANNs are used in the development of drugs for various conditions — working by using large amounts of data to come to conclusions about treatment options.

So these are the applications of Artificial Neural Networks and how it became the important process of development in big MNC’s.

I think the brain is essentially a computer and consciousness is like a computer program. It will cease to run when the computer is turned off. Theoretically, it could be re-created on a neural network, but that would be very difficult, as it would require all one’s memories. — Stephen Hawking