Intel today launched its latest 13th Gen Intel Core processor family in India, including six new unlocked desktop processors, led by the 13th Gen Intel Core i9-13900K – the world’s fastest desktop processor with 24 cores and 32 threads and a blazing fast clock speed of up to 5.8 GHz for the best recording, gaming, and streaming experience.
The 13th Gen Intel Core processor family is led by the Intel Core K series of unlocked processors, while the 13th Gen Intel Core desktop family consists of 22 processors and more than 125 partner system designs – providing a wide range of choices while delivering an uncompromising experience in both platform compatibility and application performance.
The highlight of the showcase is the 13th Gen Intel Core i9-13900K which promises to deliver the world’s best gaming, streaming, and recording experience. The new Intel Core i9-13900K is available with up to 24 cores (8 P-cores, 16 E-cores) and 32 threads. With up to 15% better single-thread performance and 5.8 GHz, it can deliver high frame rates and the world’s best gaming experience across top titles.
At the same time, enthusiasts can take advantage of 13th Gen Intel Core processors’ performance enhancements with existing Intel 600 or new Intel 700 series chipset motherboards. Combined with the continued DDR4 memory support and the latest DDR5 memory, users can reap the benefits of 13th Gen Intel Core by customizing their setup based on their own budget preferences and features.
Building on a matured Intel 7 process and Intel’s performance hybrid architecture, 13th Gen Intel Core desktop processors provide better system performance, even through the most demanding multitasking workloads. This includes up to 41% better multi-threaded performance.
and up to 15% better single-threaded performance.
The 13th Gen Intel Core desktop processors empower users with leading-edge performance and experiences across content creation, gaming, and work, with several new and improved features.
The Walmart-backed Bengaluru-headquartered Flipkart firm has partnered with eDAO, a Polygon-incubated firm, to launch the metaverse offering, which it calls Flipverse. The offering is in the pilot stage and aims to garner interest during this month’s festive season.
On Flipverse, which went live on Flipkart’s Android app on Monday, the company is offering gamified, interactive, and immersive experiences for consumers where they will be able to collect the company’s loyalty points, dubbed Supercoins, as well as digital collectibles from partner brands as they make purchases. Flipverse also features several casual games.
At a briefing Monday, Flipkart said that a wide range of brands, including Puma, Noise, Nivea, Lavie, Tokyo Talkies, Campus, VIP, Ajmal Perfumes, and Himalaya, are partnering to set up experience theaters on Flipverse.
Flipkart said that the idea is to have millions of users experience Flipverse and open the doors to the future of shopping. The company’s executives acknowledged that its web3 offerings are at an experimental stage. Still, they said they are confident that it has legs to become a critical part of Flipkart’s future eventually.
Flipkart and its chief rival in India, Amazon, are increasingly broadening their offerings to reach new customers in the South Asian market and retain a loyal base. Amazon launched a QVC-style livestream shopping in India late last month, bringing an army of more than 150 creators to host livestreams and plug products in the videos.
Meta India reported gross advertising revenues of around $2 billion or ₹16,189 crore for FY22. It is a rise of 74% year-on-year (YoY), according to the company’s filings with the Registrar of Companies (RoC). In FY21, Facebook India Online Services reported gross advertising revenues of ₹9,326 crore, a jump of 41% YoY.
As per RoC data, Meta India reported total profit before tax at ₹440 crore in FY22, growing 116% YoY, while its profit after tax grew 132% YoY to ₹297 crore. Advertising is Meta’s primary source of revenue in India. The company operates on the ad-reseller model in the country.
Meta’s three platforms, WhatsApp, Facebook, and Instagram, are very popular with Indians. According to the IT ministry, WhatsApp had 530 million users in India, Facebook had 410 million users, and Instagram had 210 million users in February 2021. One of its most popular features is Reels, its short video service on Instagram.
Meta’s primary source of income in India is advertising on Instagram and Facebook. Recently, Meta launched a tie-up between WhatsApp and JioMart to deliver groceries. JioMart has over 2 million merchant partners and is part of billionaire Mukesh Ambani’s Reliance Industries Ltd.
Vice President of the Global Business Group of Meta, Nicola Mendelsohn, called the partnership a lighthouse example for the world. One can go to the JioMart catalog, choose the things they want to be delivered and pay for them, all within WhatsApp. She added that it’s the first time they have done that globally.
Some of the most common paradigms to build and train language models use autoregressive decoder-only architectures such as PaLM or GPT-3, where the model is trained to predict the next word for a given phrase. Whereas other models, such as T5, ST-MoE span corruption-based encoder-decoder architectures. However, there remains an opportunity to create a practical unified framework for pre-training models.
According to the company’s blog, the UL2 forms different objective functions for training language models as denoising tasks, in which the model has to recover missing sub-sequences of a given input.
Furthermore, a novel mixture of denoisers is used during pre-training, which samples from various objectives, each with different configurations. The team then demonstrates the models trained using the framework in various language domains that include models fine-tuned for down-stream tasks and prompt-based few-shot learning.
According to Google AI, UL2 demonstrates superior performance on many fine-tuning and few-shot tasks. UL2 excels in the generation, language understanding, retrieval, long-text understanding, and question-answering tasks. Google AI publicly releases checkpoints of their best-performing UL2 model with 20 billion parameters, which can inspire faster progress in developing better language models in the machine learning community.
One of them is an ambitious air ambulance named ‘Aero108’ capable of carrying one operator, one paramedic (humanoid), one patient, and life support systems for the patient. The vehicle can also transport up to 350 kg of useful cargo.
According to ARTPARK, Aero108 will be equipped with state-of-the-art instruments. It can carry out autonomous operations by having interactive technology connecting patients and medical staff even when separated by vast distances. It added that controlled center hubs would monitor the Aero108s in various parts of the country. These stations will direct the nearest Aero108 to the location of the emergency.
The second is developing an unmanned aerial vehicle (UAV) for human organ transportation. The team at ARTPARK is working on developing a UAV that will enable quick human organ transport. The organ retrieval and organ transplantation hospitals will be connected through a network of aerial vehicles.
Apart from cutting a significant amount of time, using these UAVs will enable one to cover more than 200 km in one sortie with a velocity of 100 Km/h. Currently, this project is under development. ARTPARK will begin the manufacture of the prototype by the end of this year and start testing by the mid of 2023.
The AI-powered service to verify users’ age of those claiming to be 18 or older on Instagram is now ready to be rolled out to two key overseas markets, India and Brazil.
Instagram started testing this program in the US earlier this year. It uses techniques including authentication by running video selfies through an artificial intelligence system. Users can verify their age by submitting an ID. Instagram has a list of documents that it accepts for verification.
According to the market intelligence platform Sensor Tower, these countries have about 400 million monthly active users on Instagram. In an updated blog post, Instagram said that it plans to roll out the age verification program to the EU and UK before the end of the year.
The program enables users to upload a video of themselves, which Instagram runs through an AI system to determine whether they are 18 or older. For this, Instagram has partnered with the UK-based identity startup Yoti. Once users take a selfie video by following the on-screen instructions, Meta shares the same with Yoti for verification through its specially trained AI. Both companies claim that they delete the data later.
Instagram also said it is removing Social Vouching as an option to verify age. Social Vouching, one of the practical ways Instagram verified the age as part of the new program, allowed users to request their mutual followers, aged 18 or above, to vouch for the age. While it didn’t expand on the reason, some users were likely fooling the system by asking their mutual followers, 18 or above, to lie for them.
On Thursday, NATO Defence Ministers has agreed to the establishment of a Review Board to oversee the responsible use and development of AI and data across the NATO Enterprise.
The first task of the board will be to develop a user-friendly Responsible AI certification standard. It will include quality controls and risk mitigation, that will help align novel AI and data projects with NATO’s Principles of Responsible Use, which was approved in October 2021.
The Board will also act as a unique platform to exchange best practices and guide innovators as well as operational end-users throughout the development phase. This will contribute to trust within the innovation community. NATO is piloting AI in fields as diverse as cyber defense, climate change, and imagery analysis at present.
In response to the Strategic Concept’s call 2022 to expedite digital transformation, NATO Allies also approved NATO’s first Digital Transformation vision. By the year 2030, NATO’s Digital Transformation will allow the Alliance to conduct multi-domain operations. It will also facilitate political consultation and data-driven decision-making, ensure interoperability across all domains, and enhance situational awareness.
NATO’s efforts in emerging and disruptive technologies, AI Strategy, and data exploitation framework policy will contribute to bringing the vision to life. Additional steps were made with the Defence Ministers’ endorsement of priority areas for applying advanced data analysis, including enabling multi-domain operations, enhancing situational awareness, and approving NATO’s first autonomy implementation plan.
Autonomy, AI, and data exploitation are among the nine technological areas of priority to NATO. These include quantum-enabled technologies, biotechnology and human enhancements, hypersonic technologies, novel material and manufacturing, energy and propulsion, and space.
The Wall Street Journal reported on Saturday, Meta’s flagship Metaverse app Horizon Worlds has been struggling to gain new users. Horizon Worlds is a free virtual reality app created by Meta.
The publication, which reviewed internal documents, revealed Meta had initially targeted 500,000 monthly active users by year’s end. Later it revised the goal to 280,000. The current figure is less than 200,000 now, according to the report.
Horizon World’s user base has steadily declined since spring. Most users don’t return after the first month. Meta wanted users to build their own worlds, but that number is less than 1%. At least 50 people visit only 9% of the world, and most are never visited at all, claims the WSJ report.
“An empty world is a sad world,” noted one internal document. What is also making Meta worry is that retention rates for the Quest VR headset have dropped in the past three years. Shockingly, over half of the headsets aren’t used after six months.
The company has reportedly put Horizons on a “quality lockdown,” meaning the app won’t receive any new features. Meta will only focus on bugs and complaints for now.
The report comes when Meta’s stock has taken a nosedive. Meta shares are down over 60% over the past year. The Silicon Valley major has lost $700 billion in market value since September 2021.
Facebook founder Mark Zuckerberg announced a year ago that he was betting on the metaverse. So far, the company’s investment in creating the metaverse, a virtual world where users will play and work together, is a bad idea.
The world now realizes the value of machine learning algorithms in computing and forecasting, which has fueled a boom in machine learning research. According to the data mining blog, roughly 100 machine learning papers are published in a day on Arxiv, a well-known public repository of research papers. There are more open repositories, including Paper with Code, Crossminds, Connected Papers, and others.
Papers with Code is a website organizing free access to technical published papers and providing the software used in the papers. The objective of the website is to create a free and open resource for machine learning and computer vision researchers, including machine learning papers, code, datasets, methods, and evaluation tables. These resources are provided with the support of the natural language processing and machine learning community. Papers with Code has 79,817 papers, 9,327 benchmarks, and 3,681 tasks till now, and we expect to see more state-of-art papers in the future. Some significant methods and modules are popular for research and study in these papers. Here is the list of the popular machine learning papers on Papers with Code.
List of top machine learning papers on Papers with Code
This list contains the top 10 machine learning papers available on Papers with Code.
1. TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning
“TensorFlow Eager: A Multi-Stage, Python-Embedded DSL for Machine Learning” paper introduces TensorFlow Eager, a multi-stage Python-embedded domain-specific language for hardware accelerated machine learning suitable for both interactive research and production. TensorFlow has shown remarkable performance but requires users to represent competitions as dataflow graphs, hindering rapid prototyping and run-time dynamism. On the contrary, TensorFlow Eager excels TensorFlow and eliminates the usability cost without sacrificing the benefits of graphs. TensorFlow Eager provides a crucial front-end to TensorFlow used to execute operations immediately and a JIT tracker translating Python functions composed of TensorFlow operations. The paper concludes by providing TensorFlow Eager that is easier to interpolate between imperative and staged execution in a single package.
2. A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation
The “A Simple Single-Scale Vision Transformer for Object Localization and Instance Segmentation” paper put forth a simple vision transformer design to use object localization and instance segmentation tasks. With the adaptation of vision transformer (ViT) for object detection and dense prediction tasks, many models inherited multistage designs. The multi-stage design provides a better trade-off among computational costs and effective aggregation of multiscale global contexts. This paper comprises three architectural options in ViT: spatial reduction, doubled channels, and multiscale features, demonstrating that a vanilla ViT architecture can provide a better trade-off without multiscale features. Additionally, the paper proposes a simple and compact ViT architecture called Universal Vision Transformer (UViT) to achieve high performance on common objects in context (COCO) object detection and instance segmentation tasks.
“Visual Attention Network” paper proposes a novel linear attention named large kernel attention (LKA). LKA enables the self-adaptive and long-range correlations in self-attention while avoiding the three shortcomings: training images as 1D sequences neglecting their 2D structures, quadratic complexity is too expensive, and capturing only spatial adaptability but ignores channel adaptability. This research paper is authored by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, and Shi-Min Hu. The research is an attention VAN based on LKA, which is similar to ViTs and convolutional neural networks (CNNs). This paper shows that VANs outperform ViTs and CNNs in extensive experiments for tasks like image classification, semantic segmentation, pose estimation, and more.
The “A ConvNet for the 2020s” paper revolves around visual recognition tasks and introduces vision transformers or ViTs. The ViTs were found to overthrow ConNets or CNNs, the state-of-the-art image classification models in the 20s. Although ViTs showed great performance and gained popularity, ViTs got issues when applied to general computer vision tasks like objection detection and semantic segmentation. This paper contains the diligence of Zhuang Liu, Hanzi Mao, Chao-yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie to reexamine design spaces and test a pure ConvNet. The paper shows a gradual modernization of a standard residual network (ResNet), directing it to the design of a vision transformer. The conclusion of the paper brings out a family of pure ConvNet models called ConvNeXt. ConvNeXt models are entirely constructed in ConvNet and use depthwise convolution, which can achieve accuracy and scalability similar to transformers or even outperforms the vision transformers.
“Scikit-learn: Machine Learning in Python” is an old research paper released in 2012 that proposes the famous Python module Scikit-learn or Sklearn. The module integrates a wide range of state-of-the-art machine learning algorithms for medium-scaled supervised and unsupervised learning tasks. The creation of Sklearn was done by David Cournapeau, who is a co-author of this paper, and a group of researchers known as the Scikit-learn community. Also, the Scikit-learn website provides source code, binaries, and documentation. The package aims to help non-specialists implement machine learning using a high-level language. The focus of the paper remains on the package being user-friendly, great performance, documentation, and API consistency. The outcome of the paper is the popular Scikit-learn module which makes an essential Python library for building machine learning models today.
6. Adapting the Tesseract Open Source OCR Engine for Multilingual OCR
“Adapting the Tesseract Open Source OCR Engine for Multilingual OCR” is the machine learning paper where the authors Ray Smith, Daria Antonova, and Dar-Shyang Lee describe efforts to adapt the Tesseract open source optical character recognition (OCR) engine for multiple scripts and languages. The focus was centered on enabling generic multi-lingual operation, so there is negligible customization for new language beyond providing a corpus of text. The paper concluded to find that the Tesseract classifier easily adapts to simplified Chinese, and tests on English, European, and Russian languages were run and calculated a consistent word error rate between 3.72% to 5.78%.
7. COLD: A Benchmark for Chinese Offensive Language Detection
To maintain a healthy and safe social platform, offensive language detection and prevention models are important. Although much research is conducted on offensive language detection and prevention, most studies only focus on English. In the paper “COLD: A Benchmark for Chinese Offensive Language Detection,” Jiawen Deng, Jingyan Zhou, Hao Sun, Fei Mi, and Minlie Huang facilitate to create and evaluate a Chinese offensive language detection model. Here, the COLDataset is used, which is a Chinese offensive language dataset consisting of 37k annotated sentences, and to detect offensive language, a baseline classifier called COLDetector is used, having 81% accuracy. The paper concludes with two main findings around the popular Chinese language models, CPM and CDialGPT. The findings are that the CPM tends to give more offensive output in comparison to CDialGPT, and certain prompts such as anti-bias sentences, trigger the offensive outputs.
8. Gender Classification and Bias Mitigation in Facial Images
Gender classification algorithms are useful in domains like demographic research, law enforcement, and human-computer interaction. Recent research showed two issues in gender classification, biased benchmark datasets result in algorithmic bias, and the emergence of gender minorities like LGBTQ and non-binary has been left out in gender classification. The paper “Gender Classification and Bias Mitigation in Facial Images” sheds light on the two issues mentioned above. Through surveys conducted under this paper, it was discovered that the current benchmark datasets lack representation of gender minority subgroups, so bias occurs. Here, two new facial image databases were created, a radically balanced inclusive database with the addition of LGBTQ subset and an inclusive gender database collectively with non-binary people. In the paper, an ensemble model was created and evaluated, producing an accuracy of 90.39%.
9. DeepFaceLab: Integrated, flexible and extensible face-swapping framework
Deepfake defense requires research of detection and the efforts of generation methods, and the current Deepfake methods have obscure workflow and poor performance. The “DeepFaceLab: Integrated, flexible and extensible face-swapping framework” paper introduces DeepFaceLab as a solution to Deepfake defense issues. DeepFaceLab is the current dominant deepfake framework with the necessary tools to conduct high-quality face-swapping. Here, the focus is on the implementation of DeepFaceLab with detailed principles and introduces the whole control over pipeline from where one can modify to have customized pipelines. The performance of DeepFaceLab is excellent, and it can achieve cinema-quality results with high fidelity.
10. Generalized End-to-End Loss for Speaker Verification
“Generalized End-to-End Loss for Speaker Verification” paper proposes a new loss function called the generalized end-to-end (GE2E) loss. This paper found that the GE2E loss function is more efficient than previous tuple-based end-to-end (TE2E) loss functions in the training of speaker verification models. GE2E loss function updates the network focusing on examples that are difficult to verify at each step of training and so do not require the initial stage of example selection. With the GE2E loss function, the model decreases speaker verification EER by more than 10% and simultaneously reduces the training time by 60%. Also, the paper introduces the MultiReader technique allowing domain adaptation, in which a model can be trained more accurately with the support of multiple keywords and multiple dialects.
Python programming is a versatile programming language and is preferred by programmers because of its easy writability and usage. According to IEEE Spectrum, Python is the top pick in the list of programming languages by employers and developers. The programming language is simple to understand and provides freedom to explore. Thereby, to be an expert in Python, one needs to learn and practice it thoroughly, as Guido van Rossum, the inventor of the Python programming language, said, “Python is an experiment in how much freedom programmers need. Too much freedom and nobody can read another’s code; too little and expressiveness is endangered.”
Nowadays, YouTube serves as an excellent platform for learning programming, with video tutorials and easy access, anyone can learn Python. Here is the list of the top YouTube channels to learn Python programming.
1. freeCodeCamp.org
freeCodeCamp.org is a YouTube channel created by Quincy Larson in 2014 to make web development easier and accessible for anyone. FreeCodeCamp is a non-profit organization that helps in developing programming skills. It is a community with millions of people worldwide learning to code together absolutely free. The channel freeCodeCamp has 6.25M subscribers on Youtube and many more on their organization’s forum. The channel has uploaded 1300+ videos to date consisting of not only Python tutorials but also Python-related operations in deep learning and data analytics. The other domains freeCodeCamp provides tutorials for mathematics, data science, and other programming languages like Java, C, C++, and more. The videos are detailed and descriptive for at least one to two hours so that learners can catch and understand everything clearly.
CodeWithHarry is an Indian YouTube channel started in 2018 that aims to teach basics and coding techniques quickly and effectively. This is the only channel on the list which provides tutorial videos in Hindi, for which CodeWithHarry is one of the best coding channels on YouTube in the Indian region. The channel was created in 2018 by Haris Khan, a software engineer with the objective of teaching basics and techniques of coding to people efficiently, which may take years to learn. In this attempt to learn programming languages, Haris also created a website CodeWithHarry where one can find various coding courses in languages like HTML, C, C++, Python, and more. There are several Python programming tutorials available in the channel’s playlist, including Basic Python Programs, Python Data Science and Big Data Tutorials, Python Game Development using Pygame, and so on.
Programming with Mosh is one of the top YouTube channels to learn Python programming with a comprehensive and funny way of putting things. Mosh Hamedani created the channel in 2014 intending to train professional software engineers that companies love to hire. Programming with Mosh has 2.76M subscribers and has uploaded 170+ videos so far. The channel is by Code with Mosh, an e-learning provider that offers over 40+ coding courses and has taught more than 10M students until now. No doubt, the channel is popular among learners because of the essence of delivery by Mosh Hamedani. The teaching style and engagement of Mosh are both interesting and informative. Python learners appreciate his approach to breaking down complex topics to its simplest point and explaining where and why certain functions or libraries are used in the program. The channel consists of various tutorial playlists, including Python, JavaScript, Node js, and so on.
thenewboston is one of the best computer science YouTube channels started by Buckey Roberts in 2008 with the motivation to provide “tons of sweet computer related tutorials and some other awesome videos too!” as mentioned in the channel’s about description. The channel has 2.64M subscribers and 4400+ videos so far. It is a growing collection of tutorials on Docker, Ethereum, blockchain, and programming languages, including PHP, Django and more. The playlist ‘Python 3.4 Programming Tutorials’ on the channel can be a great start for beginners having basic concepts with easy-to-understand definitions. Then, learners can build real-world projects like web crawlers and scanners mentioned on the channel for practice.
Traversy Media is of the popular YouTube channels to learn Python programming for free. The channel was created by Brad Traversy in 2009 and turned into an E-learning platform Traversy Media. The idea behind the channel is ‘to show people that they don’t have to be a straight A student or a genius to learn code,’ states Brad. The channel has 1.94M subscribers and 900+ videos. The videos particularly focus on implementing programming concepts where complex concepts are broken down to show their use cases through project-based courses and tutorials. The channel presents various online web development and programming tutorials, including the building blocks of HTML, CSS & JavaScript. Also, tutorials on frontend frameworks like React, Vue and backend technologies like Node.js, Python and PHP.
CS Dojo is one of the best coding channels on YouTube, with unique code-along Python video tutorials. The code-along feature is preferred by beginners and intermediate learners, making it their stop to learn Python. The channel has 1.86M subscribers and 100+ videos and growing with its E-learning community CS Dojo. The YouTube channel CS Dojo was created in 2016 by YK Sugi, a former Microsoft intern and ex-googler. It has a playlist, ‘Python Tutorials for Absolute Beginners by CS Dojo,’ which provides the building blocks of Python programming in simple and easy ways. CS Dojo’s videos are easy to follow because of YK’s teaching style, which makes topics concise and clear and attracts a large audience. The other topics CS Dojo has tutorials for are data structures and algorithms, dynamic programming, and so on. YK includes important tips, frequently asked questions, and practice exercises in the videos, which help beginners to grasp the programming language completely. Also, the solutions to the practice exercises are provided with an in-depth explanation.
Telusko is a popular coding YouTube channel which makes learning fun and interesting. The channel was started in 2014 by Navin Reddy, senior VP at iNeuron.ai and founder of Telusko Edutech. Telusko was built with the aim of not just teaching but educating people. The channel has 1.81M subscribers and has 1600+ videos on various topics, including Java, Python, blockchain, NoSQL, and many more. The playlist ‘Python for Beginners (Full Course)’ is in the channel, where all Python concepts are covered and explained playfully and clearly. The number of videos, which is 112 on the playlist, could be overwhelming but start easy and take baby steps to learn Python programming.
Clever programmer is one of the best YouTube channels to learn Python programming. Rafeh Qazi, a full-time content creator started the channel in 2016 and has 1.17M subscribers so far, with 700+ videos on the channel. The videos are posted frequently and streaming is done for live coding projects. Rafeh then started the Clever programmer startup education platform, whose mission is to make coding simple, fun and take a project-based approach. There are many topics like Java, SQL, and Django tutorials on the YouTube channel, yet the main focus is Python programming. Several playlists are dedicated to Python and its use cases, including Python tutorial for beginners (2019), Python tips & tricks, Hour of Python – coding challenges, and more.
One of the best YouTube channels for coding Sentdex was created in 2012 by Harrison Kinsley. It offers hands-on video tutorials to learn Python programming and further train in machine learning, data analytics, robotics, web development, and more. Sentdex has 1.61M subscribers and 1200+ videos to date. Harrison is the founder of two programs Sentdex comprising of natural language processing, sentiment analysis, data mining and programming, liberating the idea to quantify the qualitative, and PythonProgramming.net, a platform for Python programming tutorials. Sentdex’s playlist is distributed among three main domains programming language basics, data analysis, and data visualization, which consists of a range of videos using Python. Moreover, the videos are helpful and can teach how to apply Python skills to solve practical problems.
Derek Banas is an extremely talented marketing consultant who started the YouTube channel Derek Banas in 2008 to help people solve their day-to-day problems. The channel is not only focused on computer science but also provides an understanding of mathematics, statistics, and different types of topics, including dungeons and dragons, marketing, WordPress tutorial, and many more. Python beginners will find the channel easy and not too technical if they are not from a computer science background, making the learning process comfortable. The unique point of this channel is the broad spectrum of videos, as Derek stated,” I make tutorials based on your requests. I will cover any topic you can imagine,” it gives the opportunity for subscribers to request videos on certain topics. In addition to the YouTube channel, Derek has a website New Think Tank, where he posts blogs on a wide range of topics.
