Tutorials to Cover

  1. What is Tensorflow?
  2. Key features of Tensorflow.
  3. General workflow of Tensorflow algorithms.
  4. Which algorithms/models are used with Tensorflow?
  5. Difference between Tensorflow and Pytorch
  6. Applications of Tensorflow

What is Tensorflow?

TensorFlow is a library for number crunching created and maintained by Google Team. It’s used mainly for machine learning (especially deep learning) tasks. Mainly Tensorflow is used for Classification, Perceptron (basics of neural networks part), Understanding and Prediction tasks. It allows us to develop and train multi-layer and large-scale neural network models.

TensorFlow is a framework for any kind of computation that requires high performance and easy distribution. It excels at deep learning, making it possible to create everything from shallow networks (neural networks made of a few layers) to complex deep networks for image recognition and natural language process.

To work with Tensorflow one should know and have clear understanding the syntax of its functions as to perform and train various models. CNNs or convolutional is an example which is usually performed with both Tensorflow and Keras.

Tensorflow is derived from 2 words i.e. Tensor and Flow which can be described as follows:

“A Tensor is a typed multi-dimensional array. For example, a 3-D array of floating point numbers representing a mini-batch of images with dimensions [batch, height, width].”

“Flow defines the overall flow of data operations occurring while building deep learning models from generating datasets to validating accuracy result of your model.”

Features of Tensorflow

  1. One of the biggest features of TensorFlow is the ability to build a neural network.
  2. It is an open-source library that allows rapid and easier calculations in machine learning. It eases the switching of algorithms from one tool to another TensorFlow tool.
  3. With the help of python, it provides the front-end API for the development of various machines and deep learning algorithms.
  4. Originally considered not only as deep learning, but also as a library for performing tensor calculations, it is the most excellent library when considered as a deep learning framework that can also describe basic calculation processing.
  5. Distributed processing allows TensorFlow to handle large amounts of data such as big data.
  6. Execution of TensorFlow applications is made easy on various platforms such as Android, Cloud, IOS and various architectures such as CPUs and GPUs. This allows it to be executed on various embedded platforms (for e.g. Tensorflow Lite).

General flow of Tensorflow Algorithms

Most of the algorithms that are trained and build using Tensorflow follows the given below general flow:

  1. Import or generate datasets.
  2. Transform and normalize data.
  3. Partition the dataset into training, test, and validation sets.
  4. Set algorithm parameters or hyperparameters.
  5. Initialize variables.
  6. Define the model structure.
  7. Declare the loss functions.
  8. Initialize and train the model.
  9. Evaluate the model.
  10. Perform hyperparameter tuning.
  11. Monitoring the neural network model (or any other model).
  12. Deploy/Predict new outcomes.

The basic building of neural networks is done by perceptrons which performs very well on training and testing dataset.

Which Algorithms/Models are used with Tensorflow?

Some common algorithms/models that are widely used with Tensorflow are :

  1. Neural networks (CNNs, ResNet, MobileNet, etc).
  2. Tensorflow lite for developing and publishing android applications.
  3. Object detection/openCV  models.
  4. Image Segmentation and Classification.
  5. Natural Language Processing applications (BERT, ALBERT, etc.)

Difference between Tensorflow and Pytorch

  1. Tensorflow is compatible with programming languages like C++, Java, python whereas PyTorch is only for python based programming language.
  2. Tensorflow is recognised for fast computational problems (neural network models, object detection, etc.) while PyTorch supports research analysis especially for deep learning algorithms.
  3. Speed and performance are almost similar.
  4. Large datasets are preferred to train DL models on Tensorflow whereas Pytorch also uses large datasets that provides high-performance and accurate results.

Applications of Tensorflow

  1. Voice/sound recognition
  2. Language detection
  3. Video detection that can be especially done with the help of transfer learning.
  4. StyleTransfer of StyleGAN is an classical example of Tensorflow application.