Available Engines
| MatDeck Free | Lite MD Python Designer | MD Python Designer | Engineering Designer | Visionary Deck | MatDeck | |
| IDE Python, MD Script and C | ||||||
| LabDeck Note, Formula Editor | ||||||
| WYSIWYG Narrative text editing + drag and drop features direct to canvas |
||||||
| MD and Python GUI Designers | Limited | |||||
| Database Management SQL, PostgreSQL, SQLite, MySQL |
||||||
| AI Bench for Modelling | ||||||
| Statistics | ||||||
| MD Chemistry | ||||||
| Embedded Python File and Functions | ||||||
| Deploy EXE | ||||||
| FFT | ||||||
| Digital Signal Processing – DSP | ||||||
| Image Processing | ||||||
| ArrayFire | ||||||
| Advanced Math Functions | ||||||
| Data Acquisition | ||||||
| Hardware Control and Monitor | ||||||
| SCADA | ||||||
| Virtual Instrumentation | ||||||
| Dashboard |
Our no-code AI TensorFlow generator allows you to create and deploy custom AI models without any coding experience. You don’t have to be a programmer or data scientist to use it, as our user-friendly interface makes the process simple and intuitive. With a simple and concise GUI, you can create accurate TensorFlow AI Models in minutes. The Google TensorFlow No-Code AI Generator is available in Visionary Deck which is a much more affordable version of MatDeck, it is also available in all MatDeck versions.
Why use the TensorFlow AI Bench?
- Develop an AI model with extensive customization options.
- Predict new values directly from Python variables.
- Predict new values from variables within LabDeck notes.
- Enable direct utilization of variables from Python scripts and LabDeck notes.
- Utilize a professional Python integrated development environment (IDE).
- Access over 1600 Python functions.
How do we use TensorFlow?
Our generator uses the powerful TensorFlow library, which is one of the most widely used and respected open-source machine learning platforms available today. This ensures that your models are both accurate and reliable.
We use the TensorFlow library to create, save and train the AI Model, TensorFlow allows immense levels of customisations and tuning of the AI Model which allows you get the most accurate model possible.
Example: Using a TensorFlow Model to predict Kidney Stones
For this example, we will be using a csv dataset which contains data on the urology of people with and without kidney stones. Once we have selected this dataset on the TensorFlow form we can generate the AI Model, to do this we can choose from a 1, 3 and 5 layer model on the TensorFlow AI Generator, for this example I have chosen a 3-layer model with the default number of neurons in each layer. However, you can customise the number of layers, their activation functions as well as the number of neurons with the form, no code needed.
With the Form showed above, we can use the TensorFlow Library to create, save and train the AI Model. With TensorFlow, you can add immense levels of customisation and tuning to your AI Model, this allows you get the most accurate model possible. Now, while the customisation does introduce more variety and choice, you still won’t need to write a single line of code, all of this customisation is offered in the TensorFlow form. Once we have created the form to best fit our specification all we need to do is click the Generate button.
With the following code, we access the saved model generated from the form and we use it to predict the likelihood of someone having kidney stones, we use data from a csv file we import.
As we can see, with the boundary at 0.45 or 45%, nearly all of the people in this sample are likely you have kidney stones, however with such as a low boundary, it wouldn’t be ideal to use this data for prediction as most patient may still have a less than 50% of having kidney stones. To create a more strict and realistic prediction we should have a higher boundary such as 0.7 or 70%.
Now that we have changed the boundary to be higher, we can see that 6 out of the sample of 10 are likely to have kidney stones. Depending on what your objective is you can have the boundary as low or as high as you see fit.
Why use the AI No-Code TensorFlow AI Generator?
Our no-code approach means that you can get started right away without having to spend months learning complex programming languages or data science concepts. This saves you time and money, and allows you to focus on what really matters: making the most accurate and reliable AI model.
Users can choose between 1,3 and 5 layer AI Models and then finely tune each layer with the number of neurons as well as the activation type. This can all be done via the drop down menu and requires little to no skill or experience to create professional level AI Models.
Benefits of using a TensorFlow AI Model?
TensorFlow is an open-source framework, providing flexibility and efficiency for building and deploying machine learning models. It supports diverse tasks and scales well with large datasets. TensorFlow’s high-level abstraction simplifies development, and its extensive community provides resources for collaboration.
Moreover, TensorFlow enables deployment on various platforms, making it versatile for creating powerful AI solutions. Overall, TensorFlow offers flexibility, efficiency, abstraction, and deployment options, enhancing the development of AI systems.
What can the Google TensorFlow AI Generator customize?
The number of neurons in a neural network determines its complexity and the amount of information it can process. With our generator, you can adjust the number of neurons in each layer to create a model that’s tailored to your specific needs.
Activation functions are used to introduce nonlinearity into the model and enable it to make more complex decisions. Our generator provides a range of activation functions, such as ReLU, Sigmoid, and Tanh, which can be selected based on the type of problem you’re trying to solve.
Optimizers are used to minimize the error in the model during training. Our generator offers a variety of optimizer options, such as Adam, RMSprop, and SGD, which can be selected based on the size and complexity of your dataset.
Our TensorFlow AI Model generator allows you to specify the type of problem you’re trying to solve, such as classification or regression, and adjust hyperparameters such as learning rate, batch size, and number of epochs.
All of these customization options are accessible through our user-friendly interface, which makes it easy for anyone to create a powerful AI model without any coding experience. This means that you can experiment with different settings and configurations until you find the optimal solution for your problem.
Our no-code TensorFlow AI generator empowers users to create custom AI models without needing to understand the underlying programming and data science concepts. It provides an accessible and efficient way to leverage the power of machine learning and artificial intelligence to solve real-world problems.
AI Modelling with Python
Our No-Code TensorFlow AI generator can directly connect to variable from others Python scripts to predict new values for. It allows you to create your AI Model and then directly connect it to the source of your variables meaning that you will need even less time and code to successfully predict and obtain results that you need.
Python is a key language in the development of AI which is why all MatDeck functions are available for use in Python through MD Python, the official python binding for over 1600 C++ functions which
What else does MatDeck offer?
MatDeck offers a large variety of toolboxes which allow for users to achieve their goals without a single line of code, for all sorts of coders we also provide access to 1600 professional functions which provide you with highest speeds without any complexities. Notable MatDeck Features include: