## What is TensorFlow?
TensorFlow can train and run the deep neural networks for image recognition, handwritten digit classification, recurrent neural network,
**Tensor**is a multidimensional array**Flow**is used to define the flow of data in operation.
TensorFlow is used to define the flow of data in operation on a multidimensional array or Tensor. ## History of TensorFlowMany years ago, deep learning started to exceed all other machine learning algorithms when giving extensive data. Google has seen it could use these deep neural networks to upgrade its services: - Google search engine
- Gmail
- Photo
They build a framework called TensorFlow to permit researchers and developers to work together in an It was first released in 2015, while the first stable version was coming in ## Components of TensorFlow## TensorThe name TensorFlow is derived from its core framework, " A tensor can be generated from the input data or the result of a computation. In TensorFlow, all operations are conducted inside a graph. The group is a set of calculation that takes place successively. Each transaction is called an op node are connected. ## GraphsTensorFlow makes use of a graph framework. The chart gathers and describes all the computations done during the training. ## Advantages- It was fixed to run on multiple CPUs or GPUs and mobile operating systems.
- The portability of the graph allows to conserve the computations for current or later use. The graph can be saved because it can be executed in the future.
- All the computation in the graph is done by connecting tensors together.
Consider the following expression a= (b+c)*(c+2) We can break the functions into components given below: d=b+c
## SessionA session can execute the operation from the graph. To feed the graph with the value of a tensor, we need to open a session. Inside a session, we must run an operator to create an output. ## Why is TensorFlow popular?TensorFlow is the better library for all because it is accessible to everyone. TensorFlow library integrates different TensorFlow is based on graph computation; it can allow the developer to create the construction of the neural network with Tensorboard. This tool helps debug our program. It runs on CPU (Central Processing Unit) and GPU (Graphical Processing Unit).
## Use Cases/Applications of TensorFlowTensorFlow provides amazing functionalities and services when compared to other popular deep learning frameworks. TensorFlow is used to create a large-scale It is mainly used for deep learning or machine learning problems such as ## 1. Voice/Sound RecognitionVoice and sound recognition applications are the most-known use cases of deep-learning. If the neural networks have proper input data feed, neural networks are capable of understanding audio signals.
## 2. Image RecognitionImage recognition is the first application that made deep learning and machine learning popular. Telecom, Social Media, and handset manufacturers mostly use image recognition. It is also used for face recognition, image search, motion detection, machine vision, and photo clustering.
For object recognition, TensorFlow helps to classify and identify arbitrary objects within larger images. This is also used in engineering application to identify shape for modeling purpose (
## 3. Time SeriesDeep learning is using Time Series algorithms for examining the time series data to extract meaningful statistics. For example, it has used the time series to predict the stock market. A recommendation is the most common use case for Time Series.
## 4. Video DetectionThe deep learning algorithm is used for video detection. It is used for motion detection, real-time threat detection in gaming, security, airports, and UI/UX field.
## 5. Text-Based ApplicationsText-based application is also a popular deep learning algorithm. Sentimental analysis, social media, threat detection, and fraud detection, are the example of Text-based applications.
Some ## Features of TensorFlowTensorFlow has an interactive These features of TensorFlow will tell us about the popularity of TensorFlow. ## 1. Responsive ConstructWe can visualize each part of the graph, which is not an option while using ## 2. FlexibleIt is one of the essential TensorFlow Features according to its operability. It has modularity and parts of it which we want to make standalone. ## 3. Easily TrainableIt is easily trainable on CPU and for GPU in distributed computing. ## 4. Parallel Neural Network TrainingTensorFlow offers to the pipeline in the sense that we can train multiple neural networks and various ## 5. Large CommunityGoogle has developed it, and there already is a large team of software engineers who work on stability improvements continuously. ## 6. Open SourceThe best thing about the machine learning library is that it is open source so anyone can use it as much as they have internet connectivity. So, people can manipulate the library and come up with a fantastic variety of useful products. And it has become another ## 7. Feature ColumnsTensorFlow has feature columns which could be thought of as intermediates between raw data and estimators; accordingly, The feature below describes how the feature column is implemented. ## 8. Availability of Statistical DistributionsThis library provides distributions functions including Bernoulli, Beta, Chi2, Uniform, Gamma, which are essential, especially where considering probabilistic approaches such as Bayesian models. ## 9. Layered ComponentsTensorFlow produces layered operations of weight and biases from the function such as tf.contrib.layers and also provides batch normalization, convolution layer, and dropout layer. So ## 10. Visualizer (With TensorBoard)We can inspect a different representation of a model and make the changed necessary while debugging it with the help of TensorBoard. ## 11.Event Logger (With TensorBoard)It is just like UNIX, where we use Next TopicInstallation of TensorFlow through pip |