Tensor Flow Assignment Help

Tensorflow Assignment Help


The society of Tensorflow is growing. Studying about this assignment is very valuable for students. Students also have to achieve good marks in this subject without taking a toll on their final academic score. We have a deep learning assignment crew that helps experts who work entirely on TensorFlow assignments and projects. The codes which were written by our professionals are clean and executable. The learners who take up the job while pursuing the studies do not have sufficient time to allocate for writing the assignments. Our group of experts works day in and day out to complete the TensorFlow assignment. If you are facing any complexity when you write the assignment, you can contact us; our experts will help you and guide you properly. Students do not have to worry about writing the code and ensuring it is ready precisely as per the guidelines.

What is Tensorflow by The Programming Experts

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.

What are the main use cases of Tensorflow?

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).

  • Language detection: -It is one of the most accepted uses of text-based applications.
  • We all recognize Google translate, which supports over 100 + languages translating from one to another. The developed versions can be used for many cases like translating jargon legalese in contracts into plain language.
  • Text summarization: -It can be used to generate headlines for new articles. Google also not found that for shorter texts, summaries can be learned with a technique called sequence to sequence learning.

2. Voice/ Sound Recognition: Voice/Sound-based application is one of TensorFlow's most well-known uses.

  • Voice recognition: -This is typically used in IoT, Automotive, security, and UX/UI.
  • Voice search: -This is mainly used in Telecoms, Handsets manufacturers.
  • Sentiment Analysis: -These are most exploited in CRM.
  • Flaw detection: -It is mainly used in Automotive and aviation.

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.

Why must students learn Tensorflow?

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.

  • Support and usage: - This is the resourceful structure that lets you implement machine learning and deep learning. It is an open-source community when in doubt using Tensorflow or executing an application using it.
  • Simple to develop: You have to write down a reduced number of code lines. Here are many other mathematical and statistical tools you can use to create Android and IOS applications. Tensorflow is utilized for developing the mobile application.
  • Abstraction: -This is hard to deal with machine learning due to the complication involved in building the algorithms. Developers are capable of focusing on logic through TensorFlow. Abstraction is the essential advantage that is offered to Tensorflow. If you have faced any difficulty, you can look for the help of our experts help.
  • Visualization: -These tools that are used will let you debug, deploy, and optimize the application. Tensorflow permits you to use different types of content formats to learn about neural networks such as audio, video, histogram, and graph.
  • Support Kera: - It is highly well-organized. Kera is extensively used in different industries due to its fast prototyping and research methods. This is the API that is broadly used to develop machine learning models. This bears Kera and is integrated with Tensorflow.

How does Tensorflow work: -

  • It will let the developers generate the data flow graphs that assist you to learn how the data will be flow through the graphs/structures or between the processing nodes. Every node has a specific mathematical operation to perform. These nodes are associated with the help of multi-dimensional data arrays that are called Tensorflow. Tensorflow sustains other programming languages such as JavaScript, Python, swift, and other programming languages.
  • The tensor application would be developed using python. These programming languages would assist the developers in the ways to high-level partner abstraction to the application. It can be installed on any other system, including Ubuntu, windows, and Raspbain.

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Tensorflow Homework Help
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Sample of Tensorflow Assignment Solved by The Experts

COMP40511: Applied Artificial Intelligence Assessment Problem and Requirements

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.

Detailed instructions:

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:

  • Replace the learner in the original program with a Multilayer Perceptron (having one hidden layer) using scikit-learn. Create a subsection in your report describing all parameters of the perceptron and training method, and how they were set. Also, report the accuracy of the resulting method.
  • Replace the learner in the original program with a deep convolutional neural networkusing the keras or TensorFlow library (do not use dimensionality reduction with this learner). Create a subsection in your report describing all parameters of the neural network and training method, and how they were set. Also, report the accuracy of the resulting method.
  • Use methods from scikit-image to try and find the eyes in each picture of the dataset (it may be easiest to select a random subset of suitable size and display them together with the inferred position of the eyes as images using matplotlib. Accuracy can then be reported by counting the number of correctly marked eyes.) Create a subsection in your report describing your method of detecting the eyes, and the resulting accuracy.
  • Use a clustering method of your choice to cluster the images, and measure how accurately the clusters correspond to the sets of images of one person. (You should not use dimensionality reduction methods like PCA for this task, but you may decrease the resolution of the images if you need to speed things up). Also, investigate whether using one chosen image pre-processing method from scikit-learn (e.g., a variant of histogram equalization, or edge detection) can improve this accuracy. Create a subsection in your report where you mention your methods and the resulting accuracy.
  • Take a set of images of only two different persons. Apply PCA as in the original program. Then pass a suitable number of principal components to a genetic programming system that is implemented using the deep library, and evolve a function that outputs a positive number if an image is from one person, otherwise a negative number. Measure the accuracy of the evolved classifier. Create a subsection in your report where you describe 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.