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More than Statistics, Machine Learning Needs Semantics: Explained

More than Statistics, Machine Learning Needs Semantics: Explained

Of late, machines have achieved somewhat human-like intelligence and accuracy. The deep learning revolution has ushered us into a new era of machine learning tools and systems that perfectly identifies the patterns and predicts future outcomes better than human domain experts. Yet, there exists a critical distinction between man and machines. The difference lies in the way we reason – we, humans like to reason through advanced semantic abstractions, while machines blindly depend on statistics.

The learning process of human beings is intense and in-depth. We prefer to connect the patterns we identify to high order semantic abstractions and our adequate knowledge base helps us evaluate the reason behind such patterns and determine the ones that are most likely to represent our actionable insights.

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On the other hand, machines blindly look for powerful signals in a pool of data. Lacking any background knowledge or real-life experiences, deep learning algorithms fail to distinguish between relevant and specious indicators. In fact, they purely encode the challenges according to statistics, instead of applying semantics.

This is why diverse data training is high on significance. It makes sure the machines witness an array of counterexamples so that the specious patterns get automatically cancelled out. Also, segmenting images into objects and practicing recognition at the object level is the order of the day. But of course, current deep learning systems are too easy to fool and exceedingly brittle, despite being powerful and highly efficient. They are always on a lookout for correlations in data instead of finding meaning.

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How to Fix?

The best way is to design powerful machine learning systems that can tersely describe the patterns they examine so that a human domain expert can later review them and cast their approval for each pattern. This kind of approach would enhance the efficiency of pattern recognition of the machines. The substantial knowledge of humans coupled with the power of machines is a game changer.

Conversely, one of the key reasons that made machine learning so fetching as compared to human intelligence is its quaint ability to identify a range of weird patterns that would look spurious to human beings but which are actually genuine signals worth considering. This holds true especially in theory-driven domains, such as population-scale human behavior where observational data is very less or mostly unavailable. In situations like this, having humans analyze the patterns put together by machines would be of no use.

End Notes

As closing thoughts, we would like to share that machine learning initiated a renaissance in which deep learning technologies have tapped into unconventional tasks like computer vision and leveraged superhuman precision in an increasing number of fields. And surely we are happy about this.

However, on a wider scale, we have to accept the brittleness of the technology in question. The main problem of today’s machine learning algorithms is that they merely learn the statistical patterns within data without putting brains into them. Once, deep learning solutions start stressing on semantics rather than statistics and incorporate external background knowledge to boost decision making – we can finally chop off the failures of the present generation AI.

Artificial Intelligence is the new kid on the block. Get enrolled in an artificial intelligence course in Delhi and kickstart a career of dreams! For help, reach us at DexLab Analytics.

 

The blog has been sourced from www.forbes.com/sites/kalevleetaru/2019/01/15/why-machine-learning-needs-semantics-not-just-statistics/#789ffe277b5c

 

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Deep Learning: Is It Still on the ‘Hype Cycle’?

Deep Learning: Is It Still on the ‘Hype Cycle’?

Interestingly, the last decade has witnessed some phenomenal leaps in the technology domain, notably in AI. As compared to the early days of speech recognition, smartphones we use today have transformed themselves entirely; they are more like our virtual assistants: the reason being quantum advancements in Deep Learning and Machine Learning.

The craze surrounding Deep Learning continues to grow. In this blog, we will evaluate whether the trend is going to stay for long and influence the future of AI or is it just a hype which will soon disappear into thin air.

The Hype Cycle

In simple terms, a ‘hype cycle’ refers to a curve that escalates to a peak at the start, then drops sharply and gets into a plateau. Perhaps not surprisingly, Deep Learning has been a part of diverse ‘hype cycles’. Currently, if you follow the tech market statistics, you will find that DL is yet to reach the plateau of productivity, where it would be largely accepted by the public and leveraged for daily work. As of now, DL hasn’t reached that stage, that’s why we can’t confirm whether the technology is going to stay or dwindle away.

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From a DL Enthusiast’s Perspective

Following present-day market trends, we can say that virtual reality and augmented reality are close to the plateau of productivity. Years back, when these advanced technologies were launched they exhibited the same hype as Deep Learning. However, with time and development, they are now on the verge of becoming main-stream and we expect the same for our new friend Deep Learning.

In fact, if we see from the perspective of a DL enthusiast, we will discover that DL has been more than just a hype – it has actually done wonders in diverse fields – from playing games to self-driven cars, DL technology is used in almost everything ‘technological’.

In 2016, an AI-driven Go-playing system won over Korean champion Lee Sodol. Not only did it defeated the opponent but also excelled to become the best of Go, acing the strategy game. Tesla too leverages the Deep Learning technology for their self-driving cars. Next, Amazon’s Alexa is heard to use the divine technology of DL to make love-life predictions. It will suggest you what went wrong between you and your consort.

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Put simply, Deep Learning is the revolutionary new-age technology. Organizations are investing funds and resources all over the world. Considering the current growth rate, DL technology is soon expected to break into the mainstream industry replacing all conventional modes of technology and communications.

Outlook

With AI being the topic of discussion in almost every industry verticals, DL has been gaining popularity. No wonder, it has proved tremendously beneficial in the past but the future expectations are pretty high as well. In this case, we have to wait and observe how Deep Learning manages to fulfil industry expectations and stay inside the ring!

Delhi is home to a bevy of reputable Deep learning training institutes. Browse over their course details and pan out the best from the lot.

The blog has been sourced from ― www.analyticsindiamag.com/why-is-deep-learning-still-on-the-hype-cycle/

 

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How Deep Learning is Solving Forecasting Challenges in Retail Industry

How Deep Learning is Solving Forecasting Challenges in Retail Industry

Known to all, the present-day retail industry is obsessed with all-things-data. With Amazon leading the show, many retailers are found implementing a data-driven mindset throughout the organization. Accurate predictions are significant for retailers, and AI is good in churning out value from retail datasets. Better accuracy in forecasts has resulted in widespread positive impacts.

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Below, we’ve chalked down how deep learning, a subset of machine learning addresses retail forecasting issues. It is a prime key to solve most common retail prediction challenges – and here is how:

  • Deep learning helps in developing advanced, customized forecasting models that are based on unstructured retail data sets. Relying on Graphic Processing Units, it helps process complex tasks – though GPUs area applied only twice during the process; once during training the model and then at the time of inference when the model is applied to new data sets.

  • Deep learning-inspired solutions help discover complex patterns in data sets. In case of big retailers, the impressive technology of Deep Learning supports multiple SKUs all at the same time, which proves productive on the part of models as they get to learn from the similarities and differences to seek correlations for promotion or competition. For example, winter gloves sell well when puffer jackets are already winning the market, indicating sales. On top of that, deep learning can also ascertain whether an item was not sold or was simply out of stock. It also possesses the ability to determine the larger problem as to why the product was not being sold or marketed.

  • For a ‘cold start’, historical data is limited but deep learning has the power to leverage other attributes and boost the forecasting. The technology works by picking similar SKUs and implement that information to bootstrap forecasting process.

Nonetheless, there exists an array of challenges associated with Deep Learning technology. The very development of high-end AI applications is at a nascent stage; it is yet to become a fully functional engineering practice.

A larger chunk of successful AI implementation depends on the expertise and experience of the breed of data scientists involved. Handpicking a qualified data scientist in today’s world is the real ordeal. Being fluent in the nuances of deep learning imposes extra challenges. Moreover, apart from being labor intensive in terms of feature engineering and data cleaning, the entire methodology of developing neural network models all manually is difficult and downright challenging. It may even take a substantial amount of time to learn the tricks and scrounge through numerous computational resources and experiments performed by data scientists. All this makes the hunt down for skilled data scientists even more difficult.

Fortunately, DexLab Analytics is here with its top of the line data science courses in Gurgaon. The courses offered by the prominent institute are intensive, well-crafted and entirely industry-relevant. For more information on data analyst course in Delhi NCR, visit our homepage.

 
The blog has been sourced from ―
www.forbes.com/sites/nvidia/2018/11/21/how-deep-learning-solves-retail-forecasting-challenges/#6cf36740db18
 

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Amazon Launches DeepRacer, an Autonomous Machine Learning Car

Amazon Launches DeepRacer, an Autonomous Machine Learning Car

Amazon leverages machine learning technology and develops an entirely remote-controlled autonomous car, DeepRacer. It joins the bandwagon of blockchain, processor chips and advanced data storage in the recently held global event.

Amazon is the latest tech bigwig that’s found experimenting with the genre: self-driving cars. However, there is a subtle point of distinction between Amazon and its tailing rivals and that is the former’s car is about the size of a shoebox, while the others are busy trying to replace already existing passenger cars.

Last week, Amazon Web Services launched DeepRacer implementing reinforced learning at its annual cloud computing conference in Las Vegas – it’s a one-18th scale model car that developers can drive using ML models and joins the rivalry against newly developed autonomous racing car range. This toy car is completely autonomous and they are selling it for $399.

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Developers can now experiment and learn more about reinforcement learning – it’s basically a process that uses trial and error method and trains the software to solve complicated and difficult tasks. Even customers can train it well, thanks to AWS’ reinforcement-learning models. In this way, DeepRacer can be used in real world solving difficult tasks in the easiest and cheapest manner.

“If you really want machine learning to be expansive across companies, you have to find a way to let everyday developers build machine-learning models and put them in production,” said Andy Jassy, chief executive of Amazon Web Services. “We wanted to make that easy for developers to take advantage of because that’s where all the innovation is going to happen… We said, how are they going to get hands-on experience and actually try it?”

Amazon’s DR is built on a monster truck chassis, contains a battery system, operates using Intel Atom processor and is mobile phone-monitored. The car’s AI module is constructed on AWS SageMaker and its 3D simulation environment is inspired by AWS RoboMaker.  It features a deep lens camera, which lets it maneuver through its surroundings – this too explains its weird shape.

Talking about deep lens camera, just a year ago, AWS released a cutting-edge image recognition camera, known as DeepLens. It helped a large number of developers to design a wide array of applications using image recognition and aided companies in solving challenges regarding autonomous driving. Soon, the company also marked its footsteps in the domain of self-driving cars and built this autonomous car to simulate driving and tackle issues regarding autonomous driving.

Interestingly, AWS is gearing up to introduce the world’s very first autonomous racing league – AWS DeepRacer League – in 2019. It will include 20 races and the winners will have to showcase their autonomous cars during the Championship cup.

Currently, DeepRacer is available only in the US but will soon be on sale for developers attending AWS hackathons. Surely, Amazon has big plans to take it global and for that, they are allowing you to pre-order yours on Amazon at a discounted price of $250. The original price appears to be more than $399.

DexLab Analytics is offering Deep Learning Training Courses in sync with current industry demands. Their deep learning certification in Gurgaon is fetching good marks – all thanks to an intensive knowledge-oriented curriculum, practical assistance and student-friendly approach.

 

The blog has been sourced from ― www.ft.com/content/934b73d2-f479-11e8-ae55-df4bf40f9d0d

 

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Deep Learning AI Is Not a Magic Potion but Machine Learning

Deep Learning AI Is Not a Magic Potion but Machine Learning

Frankly, today’s AI technology is nothing less than magic. Algorithms deciphering images, videos, speeches and texts, translating languages in between, driving cars, identifying cancer, playing games have unleashed a new era of digital transformation that’s creating awe-inspiring milestones each day. It seems that AI is ravaging every part our industry verticals, and we can’t be more excited!

Apparently, AI algorithms are similar to conventional machine learning algorithms. Even the most robust systems feature more artistry than science, calls for a wide array of carefully curated data, does generalize beyond their own training area and contains several unknown glitches that even their developers have little knowledge about.

In the last half decade, Deep Learning AI renaissance has reached new heights. With groundbreaking innovations and phenomenal feats in ML technology, AI has come to hold almost a mythical significance. Quite frequently, academicians are conducting ‘intelligent systems’ conferences, analysts are etching an AI solution for every problem, media personalities are conjecturing AI’s keenness to replace human superiority – all of these instances are happening each day. Not a week passes by when a research publication or academic editorial doesn’t document another application or algorithmic achievement.

Quite obviously, the ‘future of machines’ is knocking at our doors…

The field of Machine Learning and AI is expanding. Going beyond the paradigms of human intelligence, artificial intelligence is making machines perform tasks, which were previously impossible. But, how do machines work? After all, there exists no spell that would cast its enigma and make machines perform like humans.

Interestingly, the machines learn and imbibe skills by experience and through inspirations from the human brain. Deep leaning is a fraction of machine learning wherein artificial neural networks and algorithms learn from huge volumes of data. Similarly, like how we learn from experience, the advanced algorithms follow our footsteps and perform a task repeatedly unless they succeed. Just, like us, they too believe in learning from experiences and mistakes. What’s more, the term ‘deep learning’ originates from the notion that networks have numerous deep layers and they boost learning.

At present, the world is churning out data at a phenomenal speed. A state of data explosion is not too far. Every day, a staggering – 2.6 quintillion bytes of data is being generated. And this is the fuel for deep learning. As deep learning algorithms need a humongous amount of data, the increase in the levels of data creation is one of the key reasons for which deep learning capabilities and resources have grown manifold in recent years.

In a nutshell, Deep Learning, AI and ML in conjunction are rapidly advancing. Cutting edge algorithms are making life and death decisions, but yes once again we would like to say, they are not some magic potion that casts its spell around just like that. Instead, they are advanced, well-built models that identify underlying patterns and implement those patterns to relevant data. The more experience they gather, the more productive they become.

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The blog has been sourced from ― www.forbes.com/sites/kalevleetaru/2018/11/14/todays-deep-learning-ai-is-machine-learning-not-magic/#142cc4276875

 

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AI in Cyber Security: Knowing the Difference between Machine Learning and Deep Learning

AI in Cyber Security: Knowing the Difference between Machine Learning and Deep Learning

The need of the hour in business world is continuous innovation in the field of cyber security. Security vendors constantly brainstorm ideas and methods that’ll keep them ahead of cybercriminals. The gravity of the problem can be understood from a report by Sophos which mentions that almost 50% of Australian businesses were affected by ransomware attacks in 2017.

To keep functioning amidst such threats, businesses require innovative technologies, and artificial intelligence is one such tool that has become vital for cyber security.

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Artificial Intelligence

AI is a trendy term now, thanks to blockbuster Bollywood movies made on AI!

AI is an all-embracing principle that includes a number of technologies─ machine learning and deep learning being important ones among them. Basically, artificial intelligence enables machines to learn on their own from experience, modify techniques when fed with new data sets and carry out tasks that are human-like. When the principles of AI are applied to cyber security, we call it predictive security. AI helps to identify and check if files contain malware, which is carried out with the help of machine learning as well as deep learning. Although these two branches use similar AI principles, the two fields are fundamentally very different.

Moving on, let’s explore their basic differences.

Machine Learning

Machine learning is an artificial system that learns from examples and generates knowledge from past experiences. ML technology doesn’t simply memorize examples; rather it picks up laws and patterns and applies it later where relevant.

Considering today’s advanced threat landscape, conventional approaches fail to offer strong protection to a system. Malware programs are sometimes designed to make slight changes and breach traditional systems. In such situations, machine learning can be a better security option as it can detect these unknown and modified malwares too.

An important advantage of machine learning is that it keeps evolving and improving as it is used more and fed with more data. Machine learning algorithms scrutinize file elements in order to comprehend the nature of attacks, which includes simple things like file size as well as complex things like part of codes.

Deep Learning

The benefits of employing machine learning techniques in cyber security are numerous. However, it has some drawbacks too, which can be overcome with deep learning. The main limitations of ML are its inability to handle many variables at once, requirement of huge computing powers and using up a lot of space. In deep learning, unstructured data is stored in neural networks and decisions are made using predictive reasoning, which is modeled on the workings of human brain. This structure has potential to manage numerous points of information without hampering speed of the system.

Deep learning can form better idea of the big picture because it doesn’t include programs designed to solve a particular problem, rather it includes mathematical models that learn over time. A model is developed such that it can explain well what it ‘’sees’’. For this, large amount of data is used, such as trends, malicious URLs and other modes of attacks.

Cyber attackers need to be correct in their methods only once in order to breach an enterprise. On top of that, security threats are becoming more innovative each day. Hence, technologies like deep learning and machine learning need to be the founding stones of modern security systems. Understandably, these skills are also very high in demand. Artificial Intelligence certification courses are hugely popular. If this subject interests you, then don’t delay in enrolling for deep learning courses in Delhi or machine learning courses in Gurgaon from leading institute DexLab Analytics.

 
Reference: www.cso.com.au/article/648861/artificial-intelligence-vs-machine-learning-vs-deep-learning-what-difference
 

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Google’s Deep Learning Tool Now Increases Accuracy for Breast Cancer Detection

Google’s Deep Learning Tool Now Increases Accuracy for Breast Cancer Detection

Google has finally developed a deep learning tool that identifies breast cancer that has spread to lymph nodes in pathology slides with 99% accuracy. It would surely reduce the average slide review time.

Detecting how far cancer has spread within a patient’s body is a Herculean task. Especially, for breast cancer. In this case, we’ve to detect how far cancer has spread from a primary region to neighboring lymph nodes. Nodal metastasis is the key here. It influences observations circulating radiation and chemotherapy, resulting in timely and proper detection.

Nevertheless, clinicians have always struggled to determine correctly how far the disease has spread. Fortunately, Google’s AI team proved better and productive at determining metastatic breast cancer with a greater accuracy. Two research papers by Google AI team have implemented deep learning methods to address the consequential challenge, and have lent a helping hand to the pathologists for effectively detecting breast cancer.

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An algorithm, known as LYNA, Lymph Node Assistant has been developed to identify the regions of tumors that have spread or metastasized. Till now, they were extremely difficult to be detected by normal clinicians. As a well-known fact, out of half a million deaths across the globe owing to breast cancer, more than 90% are as a result of metastasis.

The abovementioned technology from Google first appeared in 2017. According to a recent publication, the AI research team at Google was influenced by “gigapixel-sized pathology slides of lymph nodes from breast cancer patients” for curating such an advanced algorithm. Moreover, the blog post revealed that the system was also able to “accurately pinpoint the location of both cancers and other suspicious regions within each slide.” In some cases, the locations are so minute that pathologists may have a hard time trying to detect them accurately.

The best part about LYNA system is regarding the area of concern for clinicians, doctors and how to enhance the entire process of review and ultimate diagnosis. According to Google, the underlying principle of this technology is to help doctors detecting metastatic breast cancer instead of replacing the human workforce. Thanks to the study and of course LYNA, the pathologists are in a better shape to accurately detect the micrometastases.

“Pathologists with LYNA assistance were more accurate than either unassisted pathologists or the LYNA algorithm itself,” reveals the blog post. This means the algorithms will become more productive when implemented by people, rather than working on their own.

However, the robust deep learning technology in question here does have some limitations – it works for limited dataset sizes. Further, only a single lymph node was scrutinized for every patient rather than multiple slides that would be common for a comprehensive clinical case. Thus, more detailed work needs to be done on LYNA before being applied to real-life patient situations.

For a detailed report, study “Artificial Intelligence Based Breast Cancer Nodal Metastasis Detection: Insights into the Black Box for Pathologists” as well as “Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer.”

To know more about deep learning and how machine learning fuels the state of the art technology of deep learning, enroll in Deep Learning Training in Gurgaon. DexLab Analytics is one of the well-recognized deep learning training institutes in Delhi that offers in-demand skill training courses. For more information, visit their official site now.

 

The blog has been sourced from — indianexpress.com/article/technology/science/google-new-deep-learning-algorithm-could-improve-detection-of-breast-cancer-5412456

 

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4 FAQs on Deep Learning

4 FAQs on Deep Learning

AI is revolutionizing the world. And Deep Learning is right in the heart of it. Yes, Deep Learning is a paradigm of machine learning, where algorithms are developed in a manner resembling the structure and function of human brain, and the entire phenomenon is called Artificial Neural Network.

From Facebook’s research to Netflix’s movie recommendations to DeepMind’s iconic algorithms, Deep Learning has indeed come a long way. Legendary innovations, awe-inspiring breakthroughs and latest technologies added to the flight. So, now that it became one of the hottest trends in the IT industry, you might be wondering what exactly in this nuanced AI concept… or how much does it includes studying mathematics and statistics… or how much deep is Deep Learning…

To answer all your questions and introduce you to the intricacies of such an in-demand IT skill, we’re here – this blog should help you in your quest for knowledge on Deep Learning.

Let’s get started!

What is Deep Learning and what makes it so popular?

Deep Learning is a significant part of AI that involves imitating the way a human brain functions, while learning some kind of knowledge. Put simply, this new branch of science has a lot to do with automating predictive analytics.

The superiority of human brain is unbeatable; this is why Deep Learning models is considered to be the most versatile and self-efficient man-made models ever been created. Using such an eccentric model, deriving crucial information from a humongous amount of data is what makes Deep Learning so special, and of course popular.

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What are some of its real-life applications?

  • Facebook and Google are translating text into various different languages, all at the same time.
  • Siri, Cortana, Alexa are effortless working towards simplifying speech recognition techniques – their voice commands have ignited a whole new world of possibilities for a machine.
  • Deep Learning is increasingly influencing impactful computer vision applications, including OCR (Optical Character Recognition) and real time language translation.
  • Snapchat and Instagram use facial feature detection – which involves a larger chunk of Deep Learning technology.
  • In the healthcare domain, detection of malignant cells has been possible because of this latest technology.

What are the prerequisites to get started with Deep Learning?

 Starting a career in this hottest field of science is not as difficult as it sounds to be. Deep Learning requires you to possess some knowledge on the following fields of study:

  • Mathematics
  • Statistics
  • Machine Learning
  • Basic skills for Coding

Which tools to possess to ace Deep Learning?

Hailing from the field of data science, I would always recommend Python certification course – because it is simple, robust, efficient, and has its own open source libraries and supports a large, active community of users. That being said, Python is a universal programming language; it can be used for development as well as implementation.

Besides Python, aspiring newbies are free to grasp top notch libraries, such as Keras. It simplifies the experimentation task and ensures access to the parameters that amplified the performance of similar models.

DexLab Analytics is a leading Python training institute in Delhi; if you are interested, you can browse through their course itinerary and make a well-informed decision.

 
The blog has been sourced from —

www.analyticsvidhya.com/blog/2018/05/deep-learning-faq

searchenterpriseai.techtarget.com/definition/deep-learning-deep-neural-network
 

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Deep Learning: A Comprehensive Study

Deep Learning: A Comprehensive Study

Deep Learning is a subdivision of machine learning, under the category of artificial intelligence. It’s based on a fixed set of algorithms that strives to model advanced level abstractions in data. In a simple model, you would be having two sets of neurons, where if the input layer receives any input, it transmits a revamped version of input to the next layer. However, in a deep network, there exists a web of many layers between input and output, compelling the algorithm to rely on multiple processing layers, made of numerous layers and non-linear transformations.

No wonder, Deep Learning has triggered a revolution in the machine learning realm. Interesting works are being carried on in this field. Innovative technology is modifying speech recognition, object detection, visual object recognition and other sectors, like genomics and drug discovery. And, yes, we are excited about all the new good things that’s happening around!!

For more detailed analysis, scroll below:

About Deep Learning Architecture

  • Generative deep architectures are created to characterize high-order correlation attributes of visible data for all sorts of pattern analysis as well as synthetic purposes.
  • Discriminative deep architectures are specialized in offering discriminative power for pattern classification, mostly by showcasing posterior distribution of classes subject to visible data.
  • Hybrid deep architectures are designed for discrimination but are aided with results of generative architectures through better optimization as well as regularization.

A Few Applications of Deep Learning

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Colorization of BW Images

Deep learning has the ability to recreate an image with the addition of color. The cutting edge technology uses the objects and the entire context within a picture for coloring the whole image, quite similar to a human approach. For this, extensive supervised layers and convulational neural network have to be put to use, of course.

Generative Model Chatbots

They are in hype. A sequence-to-sequence model is widely used to design chatbots which are capable of generating their own answer when trained on a wide set of real-live interactive datasets.

Machine Translations

Text translation is very easy to perform without following any proper sequence, allowing algorithms to ace dependencies between words and plotting to a new language.

Automatic Game Playing

Here, a model is trained to play a computer game formulated on the pixels on the screen. The task is fairly challenging and is one of the most fascinating domains of deep reinforcement models, Deep Mind.

Automatic Handwriting Generation

Here, you have to generate a new handwriting for a particular word or phrase using this technology. The handwritting is given as a sequence of coordinates written by a pen once the samples are done.

As parting thoughts, Deep Learning is still in a nascent stage in India. But, its diverse uses and capabilities will surely put it in the industry frontline some day soon. So, if you are looking for good deep learning training courses in Gurgaon, DexLab Analytics offers some out of the box kind of learning experience. Do check out their deep learning certification courses, they are excellent!

 

The blog has been sourced from — medium.com/@shridhar743/a-beginners-guide-to-deep-learning-5ee814cf7706

www.zdnet.com/article/what-is-deep-learning-everything-you-need-to-know
 

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