“Industry Use Cases OF Neural Network⚡”

What is Neural Network?

Simply,Neural networks reflect the behavior of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of AI, machine learning, and deep learning.

Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.

Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.

Neural networks rely on training data to learn and improve their accuracy over time. However, once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts. One of the most well-known neural networks is Google’s search algorithm.

How do neural networks work?

Think of each individual node as its own linear regression model, composed of input data, weights, a bias (or threshold), and an output. The formula would look something like this:

Once an input layer is determined, weights are assigned. These weights help determine the importance of any given variable, with larger ones contributing more significantly to the output compared to other inputs. All inputs are then multiplied by their respective weights and then summed. Afterward, the output is passed through an activation function, which determines the output. If that output exceeds a given threshold, it “fires” (or activates) the node, passing data to the next layer in the network. This results in the output of one node becoming in the input of the next node. This process of passing data from one layer to the next layer defines this neural network as a feedforward network.

Let’s break down what one single node might look like using binary values. We can apply this concept to a more tangible example, like whether you should go surfing (Yes: 1, No: 0). The decision to go or not to go is our predicted outcome, or y-hat. Let’s assume that there are three factors influencing your decision-making:

1. Are the waves good? (Yes: 1, No: 0)
2. Is the line-up empty? (Yes: 1, No: 0)
3. Has there been a recent shark attack? (Yes: 0, No: 1)

Then, let’s assume the following, giving us the following inputs:

• X1 = 1, since the waves are pumping
• X2 = 0, since the crowds are out
• X3 = 1, since there hasn’t been a recent shark attack

Now, we need to assign some weights to determine importance. Larger weights signify that particular variables are of greater importance to the decision or outcome.

• W1 = 5, since large swells don’t come around often
• W2 = 2, since you’re used to the crowds
• W3 = 4, since you have a fear of sharks

Finally, we’ll also assume a threshold value of 3, which would translate to a bias value of –3. With all the various inputs, we can start to plug in values into the formula to get the desired output.

Y = (1*5) + (0*2) + (1*4) — 3 = 6

If we use the activation function from the beginning of this section, we can determine that the output of this node would be 1, since 6 is greater than 0. In this instance, you would go surfing; but if we adjust the weights or the threshold, we can achieve different outcomes from the model. When we observe one decision, like in the above example, we can see how a neural network could make increasingly complex decisions depending on the output of previous decisions or layers.

In the example above, we used perceptrons to illustrate some of the mathematics at play here, but neural networks leverage sigmoid neurons, which are distinguished by having values between 0 and 1. Since neural networks behave similarly to decision trees, cascading data from one node to another, having x values between 0 and 1 will reduce the impact of any given change of a single variable on the output of any given node, and subsequently, the output of the neural network.

Lets head towards some of the use cases of Neural Network:

IBM’s Watson:

IBM is one of the largest and oldest of the legacy technology companies, but IBM has managed to transition from older business models to newer revenue streams remarkably well. None of IBM’s products demonstrate this better than its renowned AI, Watson.

• Identify meaningful relationships in raw data and has the potential to be applied in almost every field of medicine, including drug development, treatment decisions, patient care, and financial and operational decisions.
• Can extract relevant information from large amounts of data and generate actionable insights that could be applied to many applications.
• the feature of Neural network that analyze and extract different features different layers. This process allows the system to identify new data or images.

Watson has been deployed in several hospitals and medical centers in recent years, where it demonstrated its aptitude for making highly accurate recommendations in the treatment of certain types of cancers.
There are many more use-case other than this too like Automation of Cyberattack Countermeasures and many more

TESLA:

Tesla today held an “Autonomy Investor Day” at their HQ in Palo Alto, CA. There, Tesla outlined some of their plans for advanced driver assist and eventual autonomy in Tesla cars. The presentation was more technical than Tesla has revealed in the past, and significantly improved my impression of Tesla’s methods and chances. This was certainly the most important press conference that Tesla has given.

Tesla has taken a very different approach from the bulk of companies trying to build a truly autonomous car that can drive empty or let the passenger sleep. Tesla plans to use only radar and an array of video cameras around the vehicle to do the job. Almost all other teams use this but add LIDAR (laser) sensors which give the vehicle superhuman 3-D vision regardless of the lighting. At the meeting, they went into much more depth as to why they have taken that approach, and why the others are wrong.

Well, not just wrong. Elon Musk said LIDAR was a “fool’s errand” and those who depend on it are “doomed.” He predicted all other players “will dump LIDAR, that’s my prediction. Mark my words.” He said similar things about the use of detailed “HD” maps when store understanding of the road based on past trips over it.

In short, Tesla is placing a significant bet that they will be able to solve all self-driving problems using neural networks. They believe, in particular, that the problem can’t be solved without neural networks (which almost all would agree on) but go further and say that the neural network approaches needed make all other approaches (including additional sensors like LIDAR) a distraction and needless cost.

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