TensorFlow is one of the most popular Deep Learning libraries as it requires less computation power to produce accurate results in a given timeframe. Deep Learning is a subspace of Machine Learning that uses neural networks to process huge datasets and create Machine Learning models. According to Hacker News Hiring Trends, ML Developers and Engineers are in great demand and earn up to $144,885 per annum. TensorFlow is a great library to work with Deep Learning and Machine Learning frameworks.
Check out our video tutorial on TensorFlow and learn more:
What is Deep Learning?
As mentioned, Deep Learning is a subspace of Machine Learning, which in turn is a subset of Artificial Intelligence that is inspired by the cognitive abilities of human beings. Similar to our brain’s biological neural networks (BNN), Deep Learning uses artificial neural networks that allow a machine to perform various tasks such as speech recognition, Natural Language Processing, object detection, and more.
TensorFlow in Deep Learning (DL) has a layered architecture with an end-to-end problem-solving approach. There are mainly three layers, an input layer, hidden layers, and an output layer. DL algorithms need huge amounts of data to be efficient and precise with the results.
Now, let’s move forward and understand the working of an artificial neural network (ANN). This will help you get the core concept of Deep Learning.
Artificial Neural Networks
Artificial neural networks work in the same manner as biological neural networks where a node represents a neuron, links represent the axon, and accepter algorithms of a perceptron represent the dendrites. You can divide the ANN into three layers.
The input layer consists of several neurons that take in data from the external environment. This layer does not perform any computation and does not interact with the data.
Hidden Layers
Hidden layers are where all the processing is done. This set of layers extracts features, converts the data into valuable information, makes decisions, and predicts future actions. This layer is often called the deep neural network.
Output Layer
After the processing is done, the hidden layers transfer the data to the output layer for computing and providing the processed information to the outside environment.
Deep Learning requires an ample amount of data, which means that you need more computation power and better libraries like TensorFlow. So, let’s learn more about TensorFlow as it is one of the most popular Deep Learning libraries.
Deep Learning with TensorFlow
TensorFlow uses multi-layer neural networks to build complex applications with great accuracy. It can be used for image processing, video analysis, real-time object detection, decision-making, audio manipulation, and the detection of anomalies in a dataset.
TensorFlow provides algorithms and structure to implement Machine Learning using ANN and decision trees to compute large numerical datasets while maintaining accuracy. The library can run on almost any kind of device, even on a smartphone. You can use a normal PC of Core i3 Processor with 8 GB of RAM for implementing Deep Learning models without any performance issues.
TensorFlow is a popular Deep Learning library, but what are the reasons why it is ahead of other libraries such as Keras and PyTorch? Let’s find out the reasons next.
Why is TensorFlow so popular?
Below are some of the reasons why TensorFlow is a step ahead of other Machine learning libraries:
- Open-source: TensorFlow is an open-source library, which means that programmers can easily add more functions and make it more compatible with different datasets.
- Easy to build models: With TensorFlow, you can use multiple levels of abstraction as per your needs. For large training tasks, you can use the distribution strategy on different hardware configurations without changing the model.
- Powerful experimentation for research: TensorFlow allows users to build and train powerful models without sacrificing speed or performance.
Keras vs TensorFlow vs PyTorch
| Criteria | Keras | PyTorch | TensorFlow |
| Written in | Python | Lua | C++, Python, and CUDA |
| Speed | Slow; low performance | Fast; high performance | Fast; high performance |
| Datasets | Smaller datasets | Large datasets | Large datasets; high performance |
| Architecture | Simple, readable, and concise | Complex architecture; less readable compared to Keras | Easy to understand |
Now, let’s see how to install the TensorFlow library and its toolkits required for CPU and GPU.
Setting up TensorFlow for Deep Learning
To perform Deep Learning algorithms on any dataset, you have to make sure that your system can deliver the computing power as needed. For that, you need a minimum Intel Core i3 Processor with 8 GB of RAM, NVIDIA GeForce GTX 960, or higher GPU (or equivalent AMD GPU), and Windows 10 or Ubuntu OS. In the case of laptops, the gaming one would perform better in terms of time and efficiency.
Let’s dig deep into the topic and find out the steps you have to go follow for the installation:
Step 1: Installing Anaconda
- Open the link https://www.anaconda.com/ on your PC/laptop’s web browser. This guide shows how it is done for Windows, but the procedure is pretty much the same for others too
- Click on the Download button; it will take you to the portion of the web page from where you can download the setup based on your operating system
- In case you don’t know, Anaconda is a distribution of Python and other languages used for scientific computing. So, you need to download it and open the setup file
- Install the Anaconda software, and open the Anaconda prompt. It looks like the normal command prompt
- Depending on your GPU and operating system, download the CUDA toolkit from the link, Download CUDA toolkit. However, it is recommended you download CUDA 9.0 as TensorFlow is not compatible with 9.1 and 9.2 versions
- Unzip the folder, and paste it in your C drive: C:\cudnn-9.0-windows10-x64-v7
- Press Win+R, and run the command: sysdm.CPL
- Select the Advanced tab, and click on Environment Variables
- Press Edit, and add the path of the binfolder from cuDNN C:\cudnn-9.0-windows10-x64-v7\cuda\bin using the New button
Step 3: Creating an Anaconda environment
- Open the Anaconda prompt, and create a Conda environment name ‘TensorFlow’ (you can use any name in the place of ‘TensorFlow’) with the help of the following command:
- Now, activate the Conda environment by typing the command: activate TensorFlow
Step 4: Installing TensorFlow
- To install TensorFlow, open the command prompt and type C:\> pip install TensorFlow-GPU. This command is for the GPU version of TensorFlow. For the CPU-only version, use the following:
- After that, start making models of AI and Deep Learning with TensorFlow, and play around
Now that you have installed the TensorFlow library on your system, let’s go through the unique features TensorFlow brings to the table.
TensorFlow Features
From agriculture to space, TensorFlow performs very well in processing huge datasets and getting the results faster. Some of the best features of the TensorFlow library are as given below:
- Open-source: It is an open-source library, i.e., it can be manipulated in different ways to create amazing models. TensorFlow has a big community that constantly updates the library.
- Scalable: It allows data ingestion, which means that you can add chunks of data even after training your model on a small number of datasets. You get the advantage of using an already existing model and deploying it on bigger data without any changes.
- Easy to run: With executable applications, you can run TensorFlow on multiple platforms such as iOS, Cloud, Android, Windows, and Linux. Also, it requires less computation power as compared to other Deep Learning libraries.
- Easy experimentation: TensorFlow supports distributed computing, which means that you can feed in huge datasets without overloading CPU and GPU. Hence, it is easy to train models.
- Fast debugging: It provides computational graphics for the easy execution of the models. With the help of graphs and visualizations, anomalies can be detected in huge datasets without wasting time.
- Abstraction: You can add layers of code on top of each other to make your code cleaner and reduce its length. This process drastically reduces the development time of any model or visualization.
- Effective: TensorFlow allows you to work with multi-dimensional arrays, which proves to be beneficial when it comes to graph implementation.
- Flexible: It has easy prototyping, which is best suited for object-oriented deep neural networks. On large-scale systems, you can train multiple neural networks at once, which makes your model more efficient.
Conclusion
So, there you have it! Deep Learning is a part of Machine Learning that uses artificial neural networks to process complex datasets. Deep Learning with TensorFlow provides the functionality to perform complex calculations efficiently. It is used in image processing, object detections, navigation, weather predictions, and more to process complex information and huge datasets with minimal computation power. Coders can create dynamic graphs and models in TensorFlow using either C++ or Python.