As we are now moving at this speed in technology, we’re becoming more adaptive toward it. In recent years, there has been a lot of noise about Deep Learning, especially in the field of technology (to be specific, data science), and it’s been widely used today in various industries. The market growth is expected to grow by $18.16 billion by 2021.
Deep Learning, Machine Learning has become one of the most significant weapons in technology today including self-driving cars, automated tasks, AI-based voice-overs, and whatnot, it is widely operating in almost every domain to make work-life balance simpler and more advanced. Why this has become an urge of this technology and most demanding in every corner of the world? To help you with that, we’re here with this article to discuss the anomaly of deep learning and the frameworks that are being widely used today.
All About Data
You might agree with the fact that today “Data is the King” in the market and that’s why data scientists are the pioneer of its management. Their specific algorithms, tools, and techniques are making it easy to dive in from the shore without any hesitation. The “big data” revolution has brought many changes in the market with the help of which data scientists are generating an enormous amount of data. That’s why it was nearly impossible to arrange and manage those volumes of data by human interference, the urge was born to manage those complexities, and this gave birth to artificial intelligence, machine learning, and deep learning. Over the past decade, we’ve seen the kind of approach deep learning has received for any prediction methodology.
Now, let’s move on to check out the 7 best deep learning frameworks that exist today!
TensorFlow is one of the most popular, open-source libraries that is being heavily used for numerical computation deep learning. Google introduced it in 2015 for their internal RnD work but later when they saw the capabilities of this framework, they decided to make it open and the repository is available at TensorFlow Repository. As you’ll see, learning deep learning is pretty complex but making certain implementations are far easy and by such frameworks, it’s even smooth to process the desired outcomes.
How Does it Work?
This framework allows you to create dataflow graphs and structures to specify how data travels through a graph with the help of inputs as tensors (also known as a multi-dimensional graph). Tensor Flow allows users to prepare a flowchart and based on their inputs, it generates the output.
Applications of Tensor Flow:
- Text-Based Application: Nowadays text-based apps are being heavily used in the market that including language detection, sentimental analysis (for social media to block abusive posts)
- Image Recognition (I-R) Based System: Today most sectors have introduced this technology in their system for motion, facial and photo-clustering models.
- Video Detection: Real-time object detection is a computer vision technique to detect the motion (from both image and video) to trace back any object from the provided data.
The most famous, that even powers “Tesla Auto-Pilot” is none other than Pytorch which works on deep learning technology. It was first introduced in 2016 by a group of people (Adam Paszke, Sam Gross, Soumith Chintala, and Gregory Chanan), under Facebook’s AI lab. The interesting part about PyTorch is that both C++ & Python can use it but python’s interface is the most polished. Not so surprising, Pytorch is being backed by some of the top giants in the tech industry (Google, Salesforce, Uber, etc.). It was introduced to achieve two major goals, the first is to remove the requirement of NumPy (so that it can power GPU with tensor) and the second is to offer an automatic differentiation library (that is useful to implement neural networks).
How Does it Work?
This framework uses a computational dynamic graph right after the declaration of variables. Besides this, it uses Python’s basic concepts like loops, structures, etc. We have often used NLP functions in our smartphones (such as Apple’s Siri or Google Assistant), they all use deep learning algorithms known as RNN or Recurrent Neural Network.
Applications of PyTorch:
- Weather Forecast: To predict and highlight the pattern of a particular set of data, Pytorch is being used (not only for forecast but also for real-time analysis).
- Text Auto Detection: We might have noticed sometimes whenever we try to search something on Google or any other search engine, it starts showing “auto-suggestion” and that’s where the algorithm works and Pytorch is being used
- Fraud Detection: To prevent any unauthorized activities on credit/debit cards, this algorithm is being used to apply anomalous behavior and outliers.
To define any mathematical expressions in deep learning, we use Python’s library Theano. It was named after a great greek mathematician “Theano”. It was released in 2007 by MILA (Montreal Institute for Learning Algorithms) and Theano uses a host of clever code optimizations to deliver as much performance at maximum caliber from your hardware. Besides this, there are two salient features are at the core of any deep learning library:
- The tensor operations, and
- The capability to run the code on CPU or Graphical Computation Unit (GPU).
These two features enable us to work with a big bucket of data. Moreover, Theano proposes automatic differentiation which is a very useful feature and can also solve numeric optimization on a big picture than deep learning complex issues.
How Does it Work?
If you talk about its working algorithm, Theano itself is effectively dead, but the deep learning frameworks built on top of Theano, are still functioning which also include the more user-friendly frameworks- Keras, Lasagne, and Blocks that offer a high-level framework for fast prototyping and model testing in deep learning and machine learning algorithms.
Applications of Theano:
- Implementation Cycle: Theaonos works in 3 different steps where it starts by defining the objects/variables then moves into different stages to define the mathematical expressions (in the form of functions) and at last it helps in evaluating expressions by passing values to it.
- Companies like IBM are using Theanos for implementing neural networks and to enhance their efficiency
- For using Theanos, make sure you have pre-installed some of the following dependencies: Python, NumPy, SciPy, and BLAS (for matrix operations).
Since we’ve been talking about deep learning and the complexity it has, Keras is another library that is highly productive and dedicatedly focuses on solving deep learning problems. Besides this, Keras also help engineers to take full advantage of the scalability and cross-platform capabilities to apply within their projects. It was first introduced in 2015 under ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System) project. Keras is an open-source platform and is being actively used as a part of python’s interface in machine learning and deep neural learning. Today, big tech giants like Netflix, Uber, etc. are using Keras actively to improve their scalability.
How Does it Work?
The architecture of Keras has been designed in such a way that it acts as a high-level neural network (written in Python). Besides this, It works as a wrapper for low-level libraries (such as TensorFlow or Theano) and high-level neural network libraries. It was introduced with the concept to perform fast testing and experiment before going on the full scale.
Applications of Keras:
- Today, companies are using Keras to develop smartphones powered by machine learning and deep learning in their system. Apple company is one of the biggest giants that has incorporated this technology in past few years.
- In the healthcare industry, developers have built a predictive technology where the machine can predict the patient’s diagnosis and can also alert pre-heart attack issues. (Thus, this machine can predict the chances of detecting heart disease, based on provided data).
- Face Mask Detection: During the pandemic, many companies have offered various contributions and companies have built a system using deep learning mechanisms for using facial recognition to detect whether the person is wearing a facial mask or not. (Nokia was among one the companies to initiate this using the Keras library)
Originating from the notion SciPy Toolkit was designed to operate and handle high-performance linear algebra. Firstly, it was introduced back in 2007 during the Google Summer of Code project by David Cournapeau. This model is designed on various frameworks such as NumPy, SciPy, and Matplotlib and has been written in Python. The objective of sci-kit-learn is to offer some of the efficient tools for Deep learning, Machine learning, and statistical modeling that enlist:
- Regression (Linear and Logistic)
- Classification (K-Nearest Neighbors)
- Clustering (K-means and K-means++)
- Model Selection,
- Preprocessing (min to max normalization), and
- Dimensionality reduction (used for visualization, summarization, and feature selection)
Moreover, it offers two different varieties of algorithms (supervised and unsupervised).
How Does it Work?
The sole purpose of introducing this library is to achieve the level of robustness and support required for use in production systems, which means a deep focus on concerns (that include ease of use, code quality, collaboration, documentation, and performance). Although the interface is Python, c-libraries are an advantage for performance (such as NumPy) for arrays and matrix operations.
Application of Scikit-learn:
- Companies like Spotify, Inria, and J.P Morgan are actively using this framework to improve linear algebra and statistical analysis.
- It works on user’s behavior and displays the outputs based on their activity
- It helps in collecting data, analyzing those stats, and providing satisfactory outputs of what users would want to see. (just like booking flight tickets or doing online shopping)
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NLTK or Natural Language Toolkit is a method of capturing or analyzing the text or speech by any software or machine into code. In other words, NLTK is an analogy in that humans interact, understand each other’s views, and respond accordingly. A computer instead of a human makes the same goes in the NLP, the interaction, understanding, and response. Moreover, NLTK (Natural Language Toolkit) Library is a suite that contains libraries and programs for (SLS) statistical language processing. The best part of this toolkit is that it is the most powerful NLP library, which contains a small group of packages so that it can send machines the appropriate direction of instructions to convert the human language and reply to it accordingly.
How Does it Work?
It works just like human interactions like we have senses (eyes and ears), and they also do have the same (a program to read and audio to hear). We process functions by the brain and the system does the same by processing based on the provided inputs. It works on two different patterns algorithm development and data preprocessing. NLTK works in several ways such as tokenization, stop word, etc.
Application of NLTK
- Voice assistant such as Google, Alexa, and Siri works on the same mechanism and throws output based on an algorithm captured by voice command.
- With the help of tokenizing method, you can conveniently split up the text by word or by sentence. This will allow you to work with small tokens (pieces) of text that are still relatively coherent and meaningful to work on this algorithm.
7. Apache MXNet
Abbreviated as max-net, is an open-source, deep learning framework that companies are actively using in their projects to define, train and deploy neural networks. The reason behind calling it MXNet is simply that, it was developed by combining various programming approaches in a singular package. Besides this, MXNet supports Python, R, C++, Julia, Perl, and many other languages that promote developers to work seamlessly, and thus, they will not be required to learn any new languages for using any different frameworks. Although, it’s not old in the market the best part about MXNet is its deliverability which means the developers can exploit the full capabilities of both GPUs and cloud computing.
How Does it Work?
It helps in accelerating any numerical computation and statistical figures and places a special emphasis on speeding up the development of deep neural networks. Besides this, it uses both capabilities to provide a flawless experience to its users i.e. imperative and symbolic programming. MXNet generally 4 major capabilities that include:
- Device Placement
- Automation Differentiation
- Multi-GPU training
- Optimized Pre-defined Layers
Applications of Apache MXNet:
- Nowadays companies are actively using image recognition in their application and its capability to run on low-power has made it more resilient to work easily in mobile apps.
- Self-Driving Cars: Companies have started building an autonomous network of mapped routes for managing traffic in the allocated location
- People with disability (such as vision impairment) are getting benefits which converting texts into voice.