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Nowadays, world data plays a vital role in gathering information, analyzing the details, and making predictions to create user-friendly surroundings. It will let the system to make use of machine learning and offer the rules that are necessitated for the data and output with the advancement of machine learning. When you are concerned about creating real-time models, Tensorflow is challenging to implement the machine learning model.
Tensorflow is used to gather datasets, train the systems with the help of different models, and produce the results based on the training. It permits you to do machine learning and neural networking using python. Tensorflow builds a network to use different algorithms for machine learning and visualize output with the help of graphs. It is the deep learning library that has been turned into an open-source recently by Google. It would be utilized by the students who are studying python and work on the python assignment. This TensorFlow would let one develop deep learning. It creates it completely easy for beginners and experts to carry out machine learning models in mobiles, pc, and cloud.
1. Text-based applications: -Utilizes of TensorFlow are text-based applications such as analysis (CRM, social media), Fraud detection (insurance, finance), and Threat detection (Social media, government).
2. Voice/ Sound Recognition: Voice/Sound-based application is one of TensorFlow's most well-known uses.
3. Image Recognition: -It is early to expand in the healthcare industry, where TensorFlow algorithms process more information and spot more patterns than humans. Processors are now able to review scans and spot more illnesses than humans. Image recognition is frequently used in engineering applications to identify shapes for modeling purposes. These technologies can learn to identify a tree it has never seen before. Image recognition is a user case that is frequently used by social media, telecom, and handset manufacturers; face recognition, image search, motion detection, machine vision, and photo clustering can be used in the automotive, aviation, and healthcare industries. It intends to recognize and identify people and objects in images as well as understanding the content and context.
4. Video Detection: -Video detection is mostly used in motion detection. Real-time threat detection is gaming, security airports, and UX/UI. We will see many more inventive use cases soon, which will influence one another and contribute to machine learning technology.
5. Time-series: -These time series algorithms are utilized for analyzing time series data in order to extract meaningful statistics. This permits forecasting non-specific time periods in addition to generating alternative versions of time series. The recommendation is the most widespread use case of time series. These are mostly the field of interest to finance, accounting, security, government, and IOT with risk detections, predictive analysis, and resource planning.
Tensorflow is very necessary for you to learn about Tensorflow open source libraries, which let you reap lots of benefits that are obtainable by machine learning if you are pursuing a machine learning course.
How does Tensorflow work: -
Learners find it hard to complete Tensorflow assignment and a project as the software is complicated. Building a model, entering data, and getting the desired output is tough.
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Face recognition is an important application for machine vision and machine learning methods. This assessment will guide you to study how different combinations of techniques influence the performance of a simple face recognition system and write a report about your findings.
Get faceRec.py from NOW and run it in your Python environment. (Note: The first time you run it, it downloads a large data set of face images from the internet, which requires several hundred MBs of free hard disk space. Also, if you get an error message related to a function „urllib.urlretrieve“, this is likely caused by connectivity problems, so try again several hours later. If the problem persists, contact your tutor.)
Modify faceRec.py in a number of different ways, and write a report containing descriptions of your methods and results:
The report should have a brief introduction where you summarize what you did. It is not necessary to write a literature review. However, if you took a significant portion of code from somewhere, you must reference it. You must also reference any scientific paper or another source that you used for deciding which methods or parameters to use. For this assignment, it should not normally be necessary to take significant portions of code from anywhere except the online documentation of the libraries used. You must not use any external libraries other than those taught in the module. You must not use any direct file, system, or web access methods in your code. (Images can be loaded from files or the Internet using methods provided by the taught libraries.)
Both your report and your code must be submitted electronically on NOW before the deadline. The code must be submitted in TWO forms: (a) all Python files, (b) the whole code from all files as an appendix to your report. Do NOT submit the report as a compressed/zipped file --- please submit the original file format (DOC or PDF).
II. The Moderation Process
All assessments are subject to a two-stage moderation process. Firstly, any details related to the assessment (e.g., clarity of information and the assessment criteria) are considered by an independent person (usually a member of the module team). Secondly, the grades awarded are considered by the module team to check for consistency and fairness across the cohort for the piece of work submitted.
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