AI helps to create an innovative new product, boosting revenue, cutting costs, and improve efficiency. The challenges that many organizations face today are, to train deep learning technique and deploy into applications. Data science is a broad term that includes products, programming, problem-solving to extract information from data etc., Data analytics, Machine learning, and Deep learning are widely used for solving problems in data science. Data analytics extracting the insights from big data. Machine Learning is a statistical technique to construct the model from the examples in the data. It is generally a classifier, feature extractor, linear regression that helps to solve data science problems. Users train large amounts of data for complex neural networks in GPU accelerated data science. Deep Learning Technique capable of human-level accuracy for many tasks that require a tremendous amount of computational power. There are many use cases in every industry like health care, financial service, retail, telecom for GPU accelerated data science. For ex. In health care uses accelerated data science to better predict diseases and the best treatments for a wide range of health conditions. Enterprises use accelerated data science to analyze customer data for product development, monitor IT systems to implement the records that will be the deeper insights for decision-makers. The quality and quantity of data will help the data science approach that helps best for the problem to solve.
Machine Learning: Machine Learning is a part of AI where intelligence is gathered from the experience of past data. Machine Learning may not be based on a statistical model but it provides some mathematical model to analyze the data. Machine Learning doing data mining to achieve a particular goal and it is part of data mining. Machine Learning is programming the computer to optimize the performance criteria based on past data. It means that the machine will improve its effectiveness in performing the task. Humans observe the actions, outcomes and keep them in memory. This learning turns into a skill for a human. Similarly, when we give the past data to our machine, the computer will learn from the data and start performing better. There are many use cases in the industry. For ex, google is able to predict what we want to search based on our own search history and numerous other predictors.
When we enter into the world of machine learning based on statistics, you can find the relationship between input and output variables. For ex, the real estate agent wants to predict the price of a particular property. Here, the output variable is the price of the property and the agent has to decide which factor affecting the price of the property like area covered, no. of bedrooms, proximity to landmark or market etc., Mathematically, the agent wants to establish price as a function of these variables like,
Y = f(X1,X2,X3,X4....)
There are 2 motivations of estimating the function. Those are,
1. Prediction
2. Inference
Prediction means that we are just interested in getting the value of Y and not in the relationship of Y with each individual variable. The inference will establish the relationship between each input variable and output so that we know how the output will change when we manipulate input. Instead of a real-estate agent, the builder likes to know whether the building is near a market or school which gives a better price. So, this person's motivation would be inference and not just prediction. Whether to predict or infer, we need the model for analysis. There are 2 major classifications of models. Those are,
1. Parametric Approach
2. Non-Parametric Approach
In the parametric model, there is a functional form of relationship between input and output variables. In the example, the predictive values of price are close to the actual values. But, non-parametric method, we do not assign a functional form to this relationship, but the functional form is estimated by the model which is very complex. Once you know the motivation, we can choose the type of model. There are 2 types of learning based on the data we have,
1. Supervised Learning
2. Unsupervised Learning
When your data has a particular output variable and one or more input variables, our model will learn from this data value of input and output. This type of learning is called supervised learning. However, if you do not have any output variable but just have a set of input variables. This model is learning the relationship between these variables. It is called unsupervised learning.
DeepLearning: Deep learning maps data samples from an input domain to an output domain. Input domains are text data, images, audio, and video streams. The output domain is determined by a business question that the problem to solve and a deep learning task to perform. For ex. If your business question requesting a yes or no answer (text data), then the deep learning task is detection. If the question requires an answer to what type of thing is it, the DL task is classification. If the question requires the shape or volume is an answer, then the DL task is segmentation. So, depends on the application, you may use a combination of tasks to achieve a more sophisticated output. For language translation, you could use speech detection in the form of classification followed by translating text from one language to other languages in the form of a prediction. Different deep learning tasks require different models like,
1. Convolutional Neural Network (CNN) used for analyzing 2D/3D images, classification, segmentation, prediction, sequence analysis, and regression problems.
2. Recurrent Neural Network (RNN) used for natural language processing and works well in sequences such as sentiment analysis, speech recognition, machine translation etc.,
3. Generative Adversarial Networks(GAN)generate images or results that are realistic. This technique is used to create images, automatically constructing pre-models, and perform image with a resolution that generates many answers to the same question.
4. Reinforcement Learning is used the ideal behavior in a specific context like optimizing resource management to create a computer cluster, teaching robots to perform complex tasks, personalized recommendation etc.,
Fundamentals of Computer Vision: Computer Vision is making things from pixel data in static images to photographs or moving images in the videos where the images are made up of pixels and picture elements. Each pixel made up of channels. There are 3 channels for color images and one channel for greyscale images. Each channel has a role from o to 255 denoting the strength of that channel. For color images, we have the channel representing red, blue, and green color. By the various combination values of these 3 channel elements, we can produce a variety of colors and produce the photograph.
Using the Simple Neural Network which is a fully connected feed-forward neural network to classify the iris dataset based on 4 attributes like petal height, petal width, sepal height, sepal width. These are propagated to a simple neural network which gives 3 outputs that were injected into a classifier and comes out with probabilities for 3 classes(iris Setosa, iris Versicolour, iris Virginica). Instead of input attributes, if we inject the image, you need to convert a one-dimensional vector. Also, you need to increase the no. of a parameter that occupies more memory space and needs to be trained in the model. These challenges can be mitigated by a convolutional neural network (CNN). It takes an image to the network that has 2 main elements like convolution and pulling which is combined to a variety of CNN architecture. The fully connected layer at the end of the network is responsible for converting the grid into vectors which are essential features of images. Hence CNN is called a feature extractor.
Basics of DL App Development: These are the steps to be followed in deep learning application development,
Step1: Collect the samples for the training data-set so that the deep neural network will learn. AI Projects aren't successful without the right data. This data needs to be prepped with purpose. The data you choose to train directly affect the quality of the resulting model. You may need several thousand images to classify a wider environment to be used.
Step2: Select a deep learning network model. It is an untrained neural network designed to perform
Step3: Choose a deep learning framework like tensor flow, PyTorch, imagenet which is used to train the dataset in a neural network. Each image processes the neural network and each node in the output layer indicates the number that shows how competent the image. For ex. to classify the image of tea or coffee, the model needs two output layers (one layer for tea, another layer for coffee) and the result is a competent factor. The deep learning framework looks at the label image to determine the correct answer. If it determines correctly, the framework strengthens the connections that contributed weight and if it infers the incorrect results, the framework reduces the weight that contributed to getting the wrong answer. After processing the entire training dataset, the neural network will have the experience to infer the correct answer. But, it requires additional epochs to process the dataset to achieve higher accuracy.
Step4: Now, the model has been trained in the large representative dataset. But, it is a low confidence number to classify the item. So, you need to modify the design topology of the model and retrain the model with the new dataset.
Step5: Once the model has been trained, you can optimize the run time performance by using layers, memory, communication overhead, and tuning nodes etc., The fully trained model is ready to be integrated into the application. So, the image can quickly infer the correct answer based on the training. This application can be deployed into cloud, workstation, robot, or self-driving car etc.,
In addition to ai application, you need to ensure your organization supports the implementation consideration for workloads like deep learning training, deep learning inference, and machine learning. So, all stakeholders must be on-board with appropriate ai infrastructure to support modern ai workloads.
Transfer Learning: There are models developed and trained the skill sets which saves the amount of team time and leverages the ai model. The Pre-Trained model provides specific skills from recognizing the images in video feed to detecting words in audio recordings for NLP. These nodes are trained by generally available data which can be adapted to specific use cases to improve the performance in our environment. It enables the team to fine-tune the specific needs.
Industry SDK provides tools and software for developers to build, test on deploy the ai solutions and integrate with applications to their industry. For ex. In healthcare, the medical institutions use the pre-trained model to classify MRI scans. With transfer learning, the Models identify new diseases or already existing diseases by fine-tuning the data available..
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