Google has acquired an artificial intelligence (AI) avatar startup for about $100 million to better compete with TikTok and boost its content game. Alter helps creators and brands express their virtual identities.
The source said the acquisition was completed nearly two months ago. However, neither of the companies disclosed it to the public. Notably, some of Alter’s top executives updated their LinkedIn profiles showing that they joined Google without acknowledging the acquisition. The source requested anonymity as they are sharing nonpublic information.
A Google spokesperson confirmed that the company had acquired Alter but refused to comment on the deal’s financial terms.
The US and Czech-headquartered Alter started its journey as Facemoji, which is a platform that offered plug-and-play tech to assist game and app developers in adding avatar systems into their apps. The startup secured $3 million in seed funding from investors, including Twitter, Play Ventures, and Roosh Ventures.
Facemoji later rebranded as Alter. A person familiar with the matter said Google hopes to use Alter to enhance its content offerings. Alter founders Robin Raszka, and Jon Slimak have not commented on the matter.
Elon Musk has finally closed his deal to buy the social media platform Twitter. The deal’s closure had a deadline of this Friday at 5 pm ET, after which a previously-postponed lawsuit by Twitter against Musk to get ahead with the deal would have been resumed.
First announced in April, the deal hit multiple hurdles along the way, including Musk’s reservations about the number of spam bots on Twitter. However, earlier this month, Musk proposed going ahead with the deal at the initially agreed price of $44 billion, or $54.20 per share.
During the deal, Twitter’s share price was $53.70 at market close, whereas Musk’s favored cryptocurrency, dogecoin (DOGE), was trading down 2.3% at 00:43 UTC, after increasing 16% in the lead till the deal’s completion. Musk said that dogecoin could be used for specific payments at Twitter
Musk had criticized Twitter’s workforce as “lazy and politically biased.” According to sources, Musk has told potential investors he plans to cut Twitter’s staff from about 7,500 employees to just over 2,000. Musk denied the report. There is also speculation about top executives being asked to leave Twitter.
Musk fired Twitter CEO Parag Agarwal, after the acquisition. Legal executive Vijaya Gadde, Chief Financial Officer Ned Segal, and General Counsel Sean Edgett were also fired. ‘The bird is freed’, Musk tweeted.
Potential crypto plans for Twitter remain unclear. In June, Musk discussed the logic for integrating digital payments into its service. Twitter added bitcoin tipping in 2021 under the previous CEO Jack Dorsey. The company also added ether wallets to the feature at the beginning of this year.
Twitter also became the first-ever company to use a new program from payments processor Stripe, which announced a feature allowing payments in USDC via Polygon in April. Musk’s takeover is being seen as a win for the crypto community.
SCS Tech India Private Limited is an IT and ITES company delivering IT services and solutions across the globe and currently has offices in India, Singapore, and Dubai. Mr. Sujit Patel, the CEO and managing director of SCS Tech, has a vision of creating value with innovation and going to the future with top-class products and services with the company. In 2019, The company was rewarded with the finest India skills talent (FIST) award for its ideas of planning, implementing, and operating smart solutions supporting digital transformation. To get insight and a better understanding of the story behind SCS Tech, Analytics Drift interviewed Dr. Prateik Ghosh, vice president of SCS Tech.
Dr.Prateik Ghosh, VC of SCS Tech
SCS Tech’s architecture
The company started in 2010 as a mainstream digital company focused on hardware, large IT infrastructure, and IT solutions, and now moved to software process-driven solutions with integrated hardware products. SCS Tech has expertise in various fields, including cybersecurity, IT infrastructure, digital transformation in AI and ML, smart and safe cities, and enterprise solutions. And the company works in numerous industries, including education, finance, homeland and security defence, emergency and disaster management, and many more. The objective of the company is to become one of the largest digital transformation companies by filling the gaps in the processing of IT infrastructure and software systems and uniting them on a single platform.
There are four main services SCS Tech provides that are spread over the areas of solutions, experience, connectivity, and insight. These services are:
Provide an integrated command and control center for disaster management or emergency protocols. The center provides actionable intelligence in the security sector that helps monitor, detect, prevent, and respond to threats.
Use of digital platforms such as dashboards with a certain set of tools for debriefing reports and analytics with connection to the Internet of things (IoT).
Have a dedicated department of supervision for cybersecurity, networking, and implementing security operations.
Provide an enterprise-level IT infrastructure consisting of data recovery, data center, and complete networking operations that have been the oldest pillar of the company.
Highlights on SCS Tech’s take on digital transformation
With advancements in emerging technologies, enterprise digital transformation help to improve services and also enhance customer experience. As modern digital enterprises are data-driven and demand quick and confident decisions, SCS Tech provides data-driven computations using various AI and ML processes like predictive analytics, statistical analyzing and visualizing, and more. These processes involve tasks of debriefing solutions, analytics, and dashboarding tools to perform continuous data analytics and forecasting.
The company has its own dashboard connected to IoTs for computing large datasets into a common database, bringing efficiency to the business with automation and improved productivity. The dashboard can take both structured and unstructured data collected from various sources, including social media platforms like WhatsApp, Facebook, and Instagram, and run operations to get insights. The company helps enterprises as a whole or levels of enterprises, as a single organization may need different computations at different levels on the same dataset. SCS Tech uses AI and ML tools to provide helpful insight according to the organization’s needs at all levels. As Dr. Prateik Ghosh stated, “Digital transformation takes the perspective of the people,” the company is centered on its client’s needs and tries to resolve their IT-related problems by collaborating and enhancing its systems and programs.
SCS Tech working towards one-stop solution
It gets difficult for the company to take charge of providing an engaging solution, including all four service points mentioned above, as not all companies can train large-scale data. “SCS Tech tries to give clients a ‘one-stop solution,’ where the company takes care of everything from software to hardware interfaces, provides training on how to run the systems, and if needed, handholds the client’s IT infrastructure for a few years and then hand it over,” explains Dr. Prateik. This way, SCS Tech empowers its client to build the best of both worlds with their ideas and the company’s expertise. The company commits to innovation and excellence to deliver consistent customer satisfaction.
Till now, SCS Tech has worked on large-scale projects for security centers and is running one of the largest disaster management systems for Maharastra in India. The upcoming exciting projects of SCS Tech are focused on the power sector and power generation systems, where the company is purposing to run their dashboard along with backend analytics of power sectors for disaster management systems. Here the idea of SCS Tech is to merge the power sector’s products and theirs to create an in-house interface computing necessary operations and insights.
The model created by Assistant Professor of the Department of Biosciences and Bioengineering, Dr. Souptick Chanda, and his team can assess the healing outcomes of various fracture fixation strategies, which allows an optimum strategy to be chosen for the patient depending on their physiologies and fracture type. Such precision models can reduce healing time and lighten the economic burden and pain for patients needing thigh fracture treatment.
The research team has used Finite Element Analysis and the Fuzzy Logic AI tool to comprehend the fracture’s healing process after various treatment methods. The study further analyzed the influence of various screw fixation mechanisms to study the fracture healing efficacies of each process.
IIT Guwahati’s AI-based simulation model can help surgeons choose the proper technique or implant before a fracture treatment. In addition to various patient-specific biological parameters, the model can also consider different clinical phenomena, such as smoking, diabetes, and others. The model can also be modified for veterinary fractures, which are, in various aspects, similar to human patients.
Based on the algorithm, the researchers plan to develop software or applications to use in hospitals and other healthcare institutions for fracture treatment protocols. Research done by IIT Guwahati researchers is helpful because the incidences of thigh-bone and hip fractures have increased significantly due to the increasing geriatric population in the world.
Machine learning is the subfield of artificial intelligence, which is the hot topic around the corner as it focuses on the capability of a machine to imitate human intelligence. It is an algorithm-intense field where a bunch of codes implements complex algorithms in a matter of seconds. According to the report of the state of octaverse, the most widely used coding language for machine learning is Python. Due to Python’s accessibility, user-friendliness, and immense developer community, it is best suited for machine learning algorithms. For large-scale usage of Python for machine learning algorithms, various Python libraries were built to write codes quickly. Here is the list of top Python libraries for machine learning.
Top Python libraries for machine learning
This list consists of the top 10 machine learning libraries in Python used vastly among programmers.
1. NumPy
Numpy is an open-source library that enables numerical computing in Python and is one of the most popular Python libraries for machine learning, useful for fundamental scientific computations. It was created in 2005 as an open-source project on GitHub, built on the early work of Numeric and Numarray libraries. NumPy comprises a collection of high-complexity mathematical functions which can process large multi-dimensional arrays and matrices. The library efficiently handles linear algebra, Fourier transformation, and random numbers. The main functions of NumPy are dynamic N-dimensional array objects, broadcasting functions, and special tools to integrate C or C++ and Fortran code. It lets users define arbitrary data types with a multi-dimensional container for any generic data and easily integrate them with most databases.
2. SciPy
SciPy is an open-source library based on NumPy. It is popular among Python libraries for machine learning because of its scientific and analytical computing capabilities. As SciPy is based on NumPy for its array manipulation, it also includes all NumPy functions with the addition of proficient scientific tools. SciPy was created as a resultant collective package written by Travis Oliphant, Eric Jones and Pearu Peterson in 2001 when there was an increased interest in creating a complete environment for scientific and technical computing in Python. Today, the development of SciPy is supported and sponsored by an open community of developers. In addition, the SciPy community is an institutional partner with Quansight Labs and is directly funded by Chan Zuckerberg Initiative and Tidelift. The library offers a range of modules for linear algebra, image optimization, integration interpolation, special functions, signal and image processing, ordinary differential equations solving, and more in science and analytical computing.
3. Scikit-learn
Scikit-learn or Sklearn is one of the basic Python libraries for machine learning used for classical machine learning algorithms. It is built on top of NumPy and SciPy for effective use in the development of machine learning. Scikit-learn was developed under the Scikit-learn project started by David Counapeau as a Google summer of code project in 2005. Then, in 2010 the first version on Sklearn was released by Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, and Vincent Michel of INRIA (The national Institute of Research in digital science and technology). The library has a wide range of functions supporting supervised and unsupervised learning algorithms. The main functionalities of Scikit-learn are classification, regression, clustering, model selection, preprocessing, and dimensionality reduction. In addition, Scikit-learn is used for data mining, modeling, and analysis.
TensorFlow is an open-sourced end-to-end platform and library used for high-performance numerical computation. It was first released in 2015 by the Google Brain team, and it specializes in differential programming, meaning the library can automatically compute a function’s derivatives. The library is a collection of tools and resources required to build deep learning and machine learning models. TensorFlow can be a great tool in deep learning for beginners because of its architectural and framework flexibility. The specialty of TensorFlow is its easy distribution of work onto multiple CPU or GPU cores by using Tensors. Tensors are containers that can store multi-dimensional data arrays as well as their linear operations. Although the primary function of TensorFlow is in the training and inference of deep neural networks, it can also be used for reinforcement learning and model visualization with its built-in tools.
5. Keras
Keras is an open-source software library in Python that provides an interface for deep learning. It can run on top of TensorFlow, Theano, and CNTK and was developed focusing on fast experimentation with deep neural networks. Among other machine learning libraries, Keras can work with the widest range of data types, including arrays, text, and images. Keras is simple to use, reduces the cognitive load on developers, and is flexible in adopting principles of progressive disclosure of complexity, meaning reducing complexity by introducing information and function at increment levels. Also, Keras is powerful, providing industry-strength performance, and has been used by organizations like NASA and YouTube. These three key features of simplicity, flexibility and power of Keras make it one of the best machine learning libraries in Python. Keras offers fully functional models for creating neural networks integrating objectives, layers, optimizers, and activation functions. The library has many use cases, including fast and efficient prototyping, research work, and data modeling and visualization.
6. Pandas
Pandas is a software library used for data science and analysis tasks in Python. It is built on top of the NumPy library, which provides numerical computing and specifies data extraction. Before building and training machine learning models, there is a need to prepare a dataset to clean and preprocess the data. Pandas help prepare the data with various tools for analyzing data in detail and is designed to work on relational and labeled data. The development of Pandas began in 2008 at AQR capital management by Wes McKinney, by the end of 2009, Pandas became open-sourced, and in 2015 Pandas became a NumFOCUS sponsored project. Now, Pandas is actively supported by a community of innovative developers and researchers worldwide, contributing to using the open-source Pandas library. It is one of the best Python libraries with high stability because of its backend code written in C or Python. Pandas provide high-level data structures, including two main types, one-dimensional series and two-dimensional DataFrame. Moreover, Pandas offers a variety of tools to manipulate series and DataFrames, so that users can prepare the dataset based on their needs.
7. Matplotlib
Matplotlib is a data visualization or plotting library used in Python and is built upon SciPy and NumPy used for graphical representation. It is compatible with plotting data from SciPy, NumPy, and Pandas and provides a MATLAB-like interface that is exceptionally user-friendly. In 2002, John Hunter developed Matplotlib, which was originally a patch to IPython enabling interactive MATLAB-style plotting. Matplotlib provides an object-oriented API using standard GUI toolkits like GTL+, wxPython, Tkinter, or Qt and helps developers to build graphs and plots. The library can generate different types of graphs, including histograms, bar graphs, scatter plots, image plots, and more. Although Matplotlib plotting is limited to 2D graphs, the graphs are high-quality and publish-ready.
Seaborn is an open-source Python data visualization library based on Matplotlib and integrates closet with Pandas data structures. Plotting with Seaborn is dataset-oriented, where declarative APIs are present to identify relationships between different elements and details of how to draw the graph. Seaborn also supports high-level abstractions for multi-plot grids and visualizes univariate and bivariate distributions. With data visualization, Seaborn helps explore and understand data by performing necessary semantic mapping and statistical aggregation internally to produce informative graphs. Seaborn is used in many machine learning and deep learning projects, and its visually attractive plots make it suitable for business and marketing purposes. Moreover, Seaborn can create extensive graphs and plots with simple commands and few lines of code, saving time and effort at the users’ end.
9. NLTK
NLTK (natural language toolkit) is one of the most popular Python libraries for machine learning used for natural language processing (NLP). It is a leading platform for building Python applications to work with human language and provides over 50 easy-to-use interface corpora and lexical resources for text processing. NLTK can be defined as a set of libraries combined under one toolkit for using symbolic and statistical NLP for English. Steve Bird and Edward Loper developed NLTK at the University of Pennsylvania with an initial release in 2001 and a stable release in 2021. There are various tasks like classification, tokenization, stemming, tagging, parsing, and semantic reasoning in NLP, which different text processing libraries in NLTK can perform. As NLTK processes textual data, it is suitable for linguistics, engineers, students, researchers, and industry analysts. Further, the library is used in sentiment analysis, recommendation and review models, text-classifier, text mining, and other human language-related operations in the industry.
10. OpenCV
OpenCV (open source computer vision) is an open-source computer vision and machine learning software library. It is a library dedicated to computer vision and image processing used by major camera companies to make their technology smart and user-friendly. OpenCV was built to provide a common infrastructure for computer vision applications. This library consists of more than 2500 optimized algorithms capable of processing various visual inputs like image and video data to find patterns or recognize objects, faces, and handwriting. Among other Python libraries for machine learning, OpenCV is the only library that focuses on real-time data processing for which OpenCV is used extensively in companies, research groups, and Government agencies.
There are other useful libraries for machine learning in Python, including PyTorch, PyCaret, Theano, Caffe, and more which didn’t make it to this list. However, perform efficiently and serves certain use cases in machine learning.
The trade volume of Reddit NFT (nonfungible token) avatars has surpassed US$1.5 million, as per reports from Polygon and Dune Analytics. This surge accounts for more than one-third of the collection’s total trading volume of US$4.1 million since its inception. At the same time, 3,780 digital collectibles reached a new all-time high in the daily sales volume of Reddit NFTs.
Reddit avatars are designed by independent artists, and creators from popular creative subreddit communities and are minted on the Polygon blockchain. Such collectibles are available for purchase on Vault, Reddit’s cryptocurrency wallet. These avatars can be shown as profile images by Reddit users.
The NFTs can be purchased and traded on secondary marketplaces like OpenSea after being purchased. OpenSea provides cross-chain operability, enabling them to be bridged onto Ethereum, Klaytn, and Solana.
Some of the avatars were premium NFTs, which users bought from the website for fixed prices starting at US$10 to US$100 each. According to Reddit Floor figures, approximately 86,000 NFTs were sold to users, giving them a market cap of about US$100 million as of right now.
A few collections have little to no bids, while others have floor values above US$2,000 in certain collections. A Spooky Season avatar created by anonymous artist poieeeyeee that sold for 18 ETH, was the most expensive Reddit NFT ever.
Many more Reddit NFTs, on the other hand, were given out or airdropped for free to some of the site’s users who had earned an especially high level of karma, across over 100,000 active communities (or subreddits), introducing many individuals to NFTs for the first time. Some of these NFTs include the Meme Team, The Singularity, Aww Friends, and Drip Squad collections.
Many others were caught off guard by the news. This is because NFT subreddit on the social network had long been a medium inundated with either hate for NFTs or rants about malicious NFT frauds.
So how did Reddit pave the way for the mass adoption of NFTs?
Industry experts believe that Reddit’s deliberate decision to refrain from using any forward-looking keywords like “NFTs” or “Crypto” is to be credited for helping new technologies become more widely accepted. Under the pretense of “Collectible Avatars,” an expansion of their already-existing function of “Avatar Builder,” it was able to offer what are technically NFTs.
The “Avatar Builder” feature, which allowed users to customize their look on the network, was introduced by Reddit in 2020. Redditors could build an avatar from a selection of endless accessories, clothing, and hairstyles. The introduction enhanced the Reddit Premium program by making unique accessories only accessible to those who were a member of it. Both the profile page and profile card for the user displayed the avatar. To further encourage the adoption of NFT avatars, Reddit teamed with Netflix, Riot Games, and the Australian Football League (AFL) to provide its users with unique avatars.
It will be interesting to see how Reddit continues to dominate the NFT industry amid the criticism and environmental concerns against NFTs.
Fukuoka, the second-largest port city in Japan and a designated National Special Strategic Zone, has announced its collaboration with Astar Japan Lab to establish itself as a Web3 hub for the country. At the “Myojo Waraku 2022” conference in Fukuoka, the partnership was unveiled by Sota Watanabe, the founder of Astar Network, and Soichior Takashima, the mayor of the city.
Together, both organizations will be able to develop new use cases for Web3 technologies thanks to the Astar Japan Labs relationship. To date, Astar Labs has partnered with more than 45 businesses to realize its vision. Microsoft Japan, Amazon Japan, Dentsu, Hakuhodo, MUFG, SoftBank, Accenture Japan, and PwC Japan are some of these companies.
With the help of the relationship with Astar Japan Lab, Fukuoka City is now well on its way to achieving its goals of becoming the hub for Web3 in Japan and luring enterprises with a worldwide focus. According to Astar Network, Web3 education will be made available to Fukuoka City as part of the collaboration. With Astar Japan Lab, they will collaborate with the city to explore new opportunities for local firms to expand globally. The municipal government aspires to make Fukuoka the Silicon Valley of Japan by encouraging innovation and entrepreneurship.
Sota compared the city’s stance on cryptocurrency to that of international leaders in the field. They highlighted that several American cities, including Miami and New York, have favorable attitudes towards Web3 and crypto. Sota continues by saying that the company would collaborate closely with Fukuoka City to draw in more entrepreneurs and developers. Being one of Japan’s four Global Startup towns, Fukuoka will foster collaboration among local citizens, entrepreneurs, and developers by introducing them to the Astar Network and its ambassadors and community.
Astar Network simplifies the creation of dApps using EVM and WASM smart contracts and provides developers with real interoperability via cross-consensus messaging. With its distinctive Build2Earn model, developers are given the ability to receive payment for the code they write and the dApps they create via a staking mechanism.
With the backing of all major exchanges and tier 1 VCs, Astar’s dynamic ecosystem has emerged as Polkadot’s leading Parachain globally. Top TVL dApps can access an incubator hub from Astar SpaceLabs to help their growth on the Polkadot and Kusama Networks.
Astar Network is the preferred blockchain network for Japanese entrepreneurs and businesses, as well as foreign companies wishing to enter the Japanese market. After taking a poll, the Japanese Blockchain Association named it the most well-liked blockchain in the country.
While online dating apps are great for meeting new people, receiving inappropriate photos may be a frustrating experience for users. In order to counteract this, Bumble has been shielding its users from vulgar images since 2019. The feature, known as Private Detector, examines photos supplied by matches to see whether they include objectionable material. Although it was primarily intended to detect unwanted nude photographs, it can also flag bare-chested selfies, which are also prohibited on Bumble. The software will obscure the unwanted photo when a match is made, giving you the option of seeing it, blocking it, or reporting the sender.
Bumble claims that Private Detector is trained using extremely large datasets, with the negative samples—those devoid of any obscene content—carefully chosen to better depict edge situations and other portions of the human body (such as the legs and arms) in order to avoid flagging them as abusive. Adding samples to the training dataset iteratively to replicate the behavior of real users or test misclassification proved to be a fruitful exercise that the dating app used throughout the years in all of the subsequent machine learning projects. Nothing precludes data scientists from potentially establishing new concepts (or labels), to potentially merge them back immediately before the actual training epochs, even if the downstream goal is phrased as a binary classification issue.
Bumble’s Data Science team has now published a white paper that explains the technology behind Private Detector and made an open-source version of it available on GitHub for commercial usage, distribution, and modification. In order to make the internet a safer place, the dating app anticipates that the feature will be embraced by the larger IT community. Bumble hopes that by making Private Detector open source, other digital firms will modify it and add their own features to increase online safety and accountability in the battle against abuse and harassment.
According to Bumble’s VP of member safety, Rachel Haas, “Open-sourcing this feature is about remaining firm in our conviction that everyone deserves healthy and equitable relationships, respectful interactions, and kind connections online.”
In recent years, Bumble has waged a campaign against cyberflashing in both the UK and the United States. Whitney Wolfe Herd, the CEO and founder of the app, contributed to the approval of HB 2789, a law in Texas that makes posting non-consensual nude photographs against the law. Since then, the dating app has aided in the passage of legislation like this in Virginia (SB 493) and California (SB 53).
Bumble has been pushing for the criminalization of cyberflashing in England and Wales, and the government stated in March 2022 that it would do so under the proposed rules, with offenders facing up to two years in jail.
These new regulations are the first step toward ensuring accountability and repercussions for this common kind of harassment that leaves victims—mostly women—feeling upset, violated, and vulnerable online.
Meta shares continued to plunge to 19% in extended trading Wednesday after Facebook’s parent issued a forecast for the fourth quarter, which came up well short of expectations for earnings from Wall Street.
Meta is battling a broad slowdown in online ad spending, problems from Apple’s iOS privacy update, and increasing competition from TikTok. Meta has produced consecutive quarters of revenue declines and is expected to post its third straight drop in the fourth quarter.
The company said revenue for the fourth quarter would be $30 billion to $32.5 billion. Analysts were expecting sales of $32.2 billion. While revenue fell 4% in the third quarter, Meta’s costs and expenses rose 19% year-over-year to $22.1 billion. Operating income declined 46% from the previous year to $5.66 billion.
Meta’s operating margin, or the profits left after accounting for costs to run the business, sank to 20% from 36% a year earlier. Overall net income was down 52% to $4.4 billion in the third quarter. At an after-hours level of about $108, Meta is trading at its lowest since March 2016, eight months before Donald Trump’s presidential election.
Revenue in the Reality Labs unit, which houses the company’s virtual reality headsets and its futuristic metaverse business, fell by almost half from a year earlier to $285 million. Its loss widened to $3.67 billion from $2.63 billion in the same quarter last year. Reality Labs has lost $9.4 billion this year, and there’s no end in immediate sight.
Meta said it is holding some teams flat in headcount, shrinking others, and investing headcount growth only in our highest priorities. As a result, Meta expects headcount at the end of 2023 will be approximately in line with third-quarter 2022 levels.
Python is becoming one of the top programming languages providing all kinds of libraries and modules to perform various tasks, including complex numerical computations with multi-dimensional data, data visualization and analysis, machine learning, and deep learning. Deep learning is a subdomain of machine learning and artificial intelligence that imitates the process of gaining knowledge as the human brain does. It is an important element of data science, including statistics and predictive modeling. A variety of deep learning libraries have been developed that offer simple tools and commands to upload data and effectively train the models, assisting users in developing and deploying deep learning models. Here is a list of top deep learning libraries in Python that will help to get accurate and intuitive predictions in deep learning models.
1. TensorFlow
TensorFlow is one of the best deep learning libraries for high-performance numerical computation. It is an open-sourced end-to-end platform and library, first released in 2015 by the Google Brain team providing a wide range of flexible tools, libraries, and community resources. TensorFlow specializes in differential programming, meaning the library can automatically compute a function’s derivatives. It can be a great tool for beginners and professionals in building deep learning and machine learning models because of its abstraction capabilities. The main features of TensorFlow is its architecture and framework flexibility, management of deep neural networks, and capability to run on a variety of computational platforms like CPU and GPU using Tensors. Tensors are containers that can store multi-dimensional data arrays and their linear operations. Thereby, TensorFlow works best on the tensor processing unit (TPU). In addition to training and inference of deep neural networks, TensorFlow can also be used for reinforcement learning and model visualization.
2. Keras
Keras is a notable deep learning library that provides an interface for deep learning and allows rapid deep neural network testing. It supports high-level neural network API, written in Python, and can run on top of TensorFlow, Theano, and CNTK. The library was developed by Francois Chollet that provides tools to build models, visualize graphs, and analyze datasets. Keras is preferred over other deep learning libraries because it is modular, extensible, and flexible. In addition, Keras can work with the widest range of data types, including arrays, text, and images. The library is user-friendly, integrates objectives, layers, optimizers, and activation functions. Another specialty of Keras is it adopts principles of progressive disclosure of complexity, reducing complexity by introducing information and function at increment levels. Keras is powerful enough to provide industry-strength performance and used by organizations like NASA, Microsoft Research, Netflix, and YouTube. The library has use cases for building sequence-based and graph-based networks, fast and efficient prototyping, data modeling, and visualization.
3. PyTorch
PyTorch is an open-source optimized tensor library for deep learning based on the Torch library, a deep library framework written in Lua programming language. It was created in 2016 by Meta’s AI research team and is now part of the Linux Foundation Umbrella. The library provides two high-level features, which are Tensor computing with strong acceleration via GPU and deep neural networks built on a tape-based automatic differentiation system. Also, with the help of the Torch distributed backend, PyTorch enables scalable distributed training and performance optimization in research and production. PyTorch is written in Python, CUDA, and C/C++ and has the support of libraries or packages used in these programming languages for processing. It provides high flexibility because of its hybrid front-end and allows users to write neural network layers quickly with its deep integration with Python. PyTorch is primarily used in computer vision applications and natural language processing and is one of the most popular deep learning libraries in the industry that companies like Facebook, Twitter, Tesla, Uber, and Google use to build deep learning software. A few deep learning software built on top of PyTorch are Tesla autopilot, Uber’s Pyro, Hugging face’s transformer, PyTorch lighting, and catalyst.
Microsoft CNTK, or the Microsoft cognitive toolkit, is a unified open-source deep learning toolkit for commercial-grade distributed deep learning. Microsoft CNTK describes neural networks as a series of computational steps via a directed graph. Microsoft Research developed CNTK in 2016, having highly optimized built-in components capable of handling multi-dimensional dense or sparse data from Python, C++, or BrainScript (its own model description language). CNTK supports interfaces in Python and C++ and can be used for handwriting, speech, and facial recognition. This popular deep learning library is known for its speed and efficiency due to CNTK’s capability to scale models in production using GPUs. Also, CNTK applies stochastic gradient descent and error backpropagation with automatic differentiation and parallelization across multiple GPUs and servers. It enables users to combine different deep learning models, such as feed-forward deep neural networks, convolutional neural networks, and recurrent neural networks. CNTK is one of the first deep learning libraries to support the open neural network exchange (ONNX), an open format built to represent machine learning models. ONNX gives the power to move machine learning or deep learning models between CNTK, Caffe2, MXNet, and PyTorch frameworks.
5. MXNet
MXNet is one of the most flexible and efficient deep learning libraries in Python developed by Apache Software Foundation and Carlos Guestin, professor of computer science at Stanford University. It is an open-source deep learning software framework designed to train and deploy deep neural networks. The interesting thing about MXNet is that it supports flexible programming models with many languages for binding, including C++, Python, R, JavaScript, Scala, and more. It is a highly scalable library compared to other deep learning libraries providing fast model training and distributed computing, allowing it to train networks across multiple CPU/GPU machines. Also, distribution training works in cloud platforms like AWS, Azure, and YARN clusters. MXNet has a dynamic dependency scheduler that automatically parallelizes symbolic and imperative operations programming to maximize efficiency and productivity.
6. Caffe
Caffe (convolutional architecture for fast feature embedding) is an open-source deep learning framework built for expression, speed, and modularity. Initially, the idea of Caffe was created by Yangqing Jia during his Ph.D. at UC Berkeley and further developed by Berkeley AI Research (BAIR) and other community contributors, and is currently hosted on GitHub. The library is written in C++ with a Python interface. It supports different deep learning models, including convolutional neural networks (CNN), region-based CNN aka RCNN, long short-term memory (LSTM) networks, and fully connected neural networks. Caffe supports GPU and CPU-based acceleration computational kernel libraries like NVIDIA, cuDNN, and IntelMLK that allow faster and high-performance computing, for which the library can process over 60M images per day only with a single NVIDIA K40 GPU. It is one of the most popular Python libraries for deep learning, with its exceptional architecture encouraging applications and innovations, extensible code scripts, and the backend community on GitHub. Caffe is mainly used for implementing image detection and classification, academic research projects, startup prototypes, and large-scale industrial applications in computer vision, speech, and multimedia. Although Facebook announced Caffe2, which is based on Caffe in 2017, enabling simple and flexible construction of deep learning models in addition to recurrent neural networks (RNN), Caffe is still in use mainly for academic purposes.
Theano is one of the popular numerical computation Python libraries for deep learning. It was developed by Montreal Institute for Learning Algorithms (MILA) at the University of Montreal in 2007, and its name “Theano” is associated with the incident philosopher Theano, the first known women mathematician who worked in the development of the golden mean. Theano is an open-source project, and its computations are expressed using NumPy-esque syntax and compiled to run either on CPU or GPU-based architectures. The library is written in Python and centers around NVIDIA CUDA, which allows users to integrate it with GPS and provides for defining, optimizing, and evaluating mathematical operations involving multi-dimensional arrays and matrix calculations. Theano has various features, including tight integration with NumPy, the transparent use of a GPU, effective symbolic differentiation, speed and stable optimization, dynamic C code generation, and extensive unit testing and self-verification. It is used extensively for deep learning projects and research due to its high-performance data-intensive calculations.
8. Deeplearning4j
Deeplearning4j (DL4J), short for Eclipse Deeplearning4j is an open-source distributed deep learning library that consists of a set of tools for running and building deep learning models on Java virtual machine (JVM). It was developed by Konduit.AI and a combined effort of a machine learning group including Adam Gibson, Alex D. Black, Vyacheslav Kokorin, and Josh Patterson. In 2017, Skymind, a San Franciso-based business intelligence and enterprise software firm, joined the Eclipse Foundation and updated DL4J to integrate it with Hadoop and Apache Spark. Among other deep learning libraries in Python, only DL4J allows training models in Java while interoperating with the Python ecosystem via a mix of Python executions of CPython bindings, model import support, and interoperability of other runtimes like TensorFlow-java and onnxruntime. It is written in Java, C++, C, CUDA and supports many neural networks, including CNN, RNN, and LSTM. The use cases of DL4J are many, from importing to retraining models of PyTorch, TensorFlow, and Keras and deploying these models in JVM microservice environments, mobile devices, IoT, and Apache Spark. Also, DL4J provides toolkits for vector space and topic modeling designed to handle large text sets and use them in natural language processing.
There are other deep learning libraries, including Lasagne, Chainer, Glucon, and more which did not made it to this list but perform efficiently.
