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Initially developed at IBM, SQL was originally called SEQUEL or Structured English Query Language. It was originally designed to manipulate and retrieve the data stored in IBM’s original quasi-relational database management system. Since it was developed, it has made lives easier for countless data science and data analytics professionals. SQL has made working with data and databases so much easier and simpler.
SQL or Structured Query Language, pronounced as ‘s-e-q-u-e-l, ’ is a language of databases and a very important tool used by professionals spread across multiple domains to work with large volumes of data and spreadsheets. It is a very important query language that has been designed to retrieve specific information from databases.
A set of SQL statements stored in a database in a compiled form is called a stored procedure. The procedure can be utilized by multiple programs to perform and accomplish specific tasks, thereby reducing the need to write separate code for every program and task. A stored procedure would accept the parameters and executes the T-SQL statements written in the stored procedure, and then returns the result set for the code, if any.
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· Effectively managing data access for end-users as stored procedures let them insert or modify the data but preventing them from tampering with or writing new procedures
· Reducing network traffic between the client and the server by executing commands in a procedure as a single batch of code
· Supporting multiple platforms and languages for different client applications so the same procedures can be used by embedding the standard procedure in the program
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Suppose, we wanted to create a stored procedure called ‘SelectAllDoctors.’ This procedure would select all records from a database called the ‘Doctors’ table. Then, here’s is the code that would be used for creating the stored procedure.
· Functions require more code than stored procedures to achieve the same results. With functions, the BEGIN and END codes and RETURNS and RETURN arguments are mandatory, which is not the case with stored procedures.
· Functions are less flexible than stored procedures as they require the schema for invoking them to be clearly defined and specified.
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E2E Cloud is an easy-to-use self-service cloud platform built to deliver high-performance web and mobile server-side applications. They have well-tested bundles of commonly used open-source and control panel software pre-installed on virtual machine images saving expensive time and resources for customers seeking to achieve their deployment goals. E2E clouds offer Linux clouds, Windows clouds, Cloud GPUs, cloud solutions like DBaaS, cloud load balancers, autoscale, and DNS. E2E’s Windows SQL cloud offering lets users run their database workloads with MS-SQL pre-installed with cloud servers, and the licenses required for the same are included in the pricing. This cloud can be used to create stored procedures easily on SQL.
Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.
As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.
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You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –
You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.
How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels.
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If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.
Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.
3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.
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Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:
Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.
The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.
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The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.
The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.
The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.
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So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website.
For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.
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Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:
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The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.
Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.
Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.
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For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.
The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results
