AI News, A simple way to understand machine learning vs deep learning ... artificial intelligence

Program Curriculum

The program consists of four integrated weeklong modules, each focusing on a different dimension of the global business environment,event management and technology.

Participants learn about cutting-edge tools in areas such as visual recognition, object recognition, natural language processing, language translation, and smart agents or bots.

Participants study the primary financial markets andthe assets traded in these markets The sessions will help develop a better understanding of how financial markets work from both a theoretical and practical perspective, how different financialmarkets interact and underpin the economy, and the increasing importance of information and technology in global financial markets.

The evaluation of global market opportunities and development of market entry and expansion strategies designed to create customer value is covered in week three.

In addition, participants will be invited to a number of workshops on resume writing, setting of effective career goals, job search strategies, interviewing techniques and public speaking.

What is AI? Artificial Intelligence Tutorial for Beginners

A machine with the ability to perform cognitive functions such as perceiving, learning, reasoning and solve problems are deemed to hold an artificial intelligence.

In this basic tutorial, you will learn- Nowadays, AI is used in almost all industries, giving a technological edge to all companies integrating AI at scale.

According to McKinsey, AI has the potential to create 600 billions of dollars of value in retail, bring 50 percent more incremental value in banking compared with other analytics techniques.

Concretely, if an organization uses AI for its marketing team, it can automate mundane and repetitive tasks, allowing the sales representative to focus on tasks like relationship building, lead nurturing, etc.

In a nutshell, AI provides a cutting-edge technology to deal with complex data which is impossible to handle by a human being.

The primary purpose of the research project was to tackle 'every aspect of learning or any other feature of intelligence that can in principle be so precisely described, that a machine can be made to simulate it.'

Machine learning is based on the idea that there exist some patterns in the data that were identified and used for future predictions.

AI has broad applications- AI is used in all the industries, from marketing to supply chain, finance, food-processing sector.

Explained by three critical factors for its popularity are: Machine learning is an experimental field, meaning it needs to have data to test new ideas or approaches.

Hardware In the last twenty years, the power of the CPU has exploded, allowing the user to train a small deep-learning model on any laptop.

Besides, big companies use clusters of GPU to train deep learning model with the NVIDIA Tesla K80 because it helps to reduce the data center cost and provide better performances.

Those pictures can be used to train a neural network model to recognize an object on the picture without the need to manually collect and label the data.

company needs exceptionally diverse data sources to be able to find the patterns and learn and in a substantial volume.

Algorithm Hardware is more powerful than ever, data is easily accessible, but one thing that makes the neural network more reliable is the development of more accurate algorithms.

Since 2010, remarkable discoveries have been made to improve the neural network Artificial intelligence uses a progressive learning algorithm to let the data do the programming.

At the beginning of the AI's ages, programmers wrote hard-coded programs, that is, type every logical possibility the machine can face and how to respond.

AI vs Machine Learning vs Deep Learning: What's the Difference?

Artificial intelligence is imparting a cognitive ability to a machine.

In this tutorial, you will learn- Machine learning is the best tool so far to analyze, understand and identify a pattern in the data.

One of the main ideas behind machine learning is that the computer can be trained to automate tasks that would be exhaustive or impossible for a human being.

The clear breach from the traditional analysis is that machine learning can take decisions with minimal human intervention.

Machine learning uses data to feed an algorithm that can understand the relationship between the input and the output.

When the machine finished learning, it can predict the value or the class of new data point.

It is a subset of machine learning and is called deep learning because it makes use of deep neural networks.

In the example, the classifier will be trained to detect if the image is a: The four objects above are the class the classifier has to recognize.

Training an algorithm requires to follow a few standard steps: The first step is necessary, choosing the right data will make the algorithm success or a failure.

The training set would be fed to a neural network Each input goes into a neuron and is multiplied by a weight.

it provides an actual value for the regression task and a probability of each class for the classification task.

The neural network is fully trained when the value of the weights gives an output close to the reality.

For instance, a well-trained neural network can recognize the object on a picture with higher accuracy than the traditional neural net.

A crucial part of machine learning is to find a relevant set of features to make the system learns something.

For example, an image processing, the practitioner needs to extract the feature manually in the image like the eyes, the nose, lips and so on.

The network applies a filter to the picture to see if there is a match, i.e., the shape of the feature is identical to a part of the image.

When there is enough data to train on, deep learning achieves impressive results, especially for image recognition and text translation.

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