[{"data":1,"prerenderedAt":75},["ShallowReactive",2],{"i-material-symbols:language":3,"i-material-symbols:person":8,"i-mdi:instagram":10,"i-mdi:youtube":12,"i-ri:linkedin-fill":14,"i-ri:twitter-x-line":16,"i-ri:facebook-fill":18,"post-machine-learning-vs-deep-learning":20,"i-material-symbols:person-outline":69,"i-material-symbols:calendar-month":71,"i-mdi:schedule":73},{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":7},0,24,false,"\u003Cpath fill=\"currentColor\" d=\"M8.125 21.213q-1.825-.788-3.187-2.15t-2.15-3.188T2 11.988t.788-3.875t2.15-3.175t3.187-2.15T12.013 2t3.875.788t3.175 2.15t2.15 3.175t.787 3.875t-.787 3.887t-2.15 3.188t-3.175 2.15t-3.875.787t-3.888-.787M12 19.95q.65-.9 1.125-1.875T13.9 16h-3.8q.3 1.1.775 2.075T12 19.95m-2.6-.4q-.45-.825-.787-1.713T8.05 16H5.1q.725 1.25 1.813 2.175T9.4 19.55m5.2 0q1.4-.45 2.488-1.375T18.9 16h-2.95q-.225.95-.562 1.838T14.6 19.55M4.25 14h3.4q-.075-.5-.112-.987T7.5 12t.038-1.012T7.65 10h-3.4q-.125.5-.187.988T4 12t.063 1.013t.187.987m5.4 0h4.7q.075-.5.113-.987T14.5 12t-.038-1.012T14.35 10h-4.7q-.075.5-.112.988T9.5 12t.038 1.013t.112.987m6.7 0h3.4q.125-.5.188-.987T20 12t-.062-1.012T19.75 10h-3.4q.075.5.113.988T16.5 12t-.038 1.013t-.112.987m-.4-6h2.95q-.725-1.25-1.812-2.175T14.6 4.45q.45.825.788 1.713T15.95 8M10.1 8h3.8q-.3-1.1-.775-2.075T12 4.05q-.65.9-1.125 1.875T10.1 8m-5 0h2.95q.225-.95.563-1.838T9.4 4.45Q8 4.9 6.912 5.825T5.1 8\"\u002F>",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":9},"\u003Cpath fill=\"currentColor\" d=\"M9.175 10.825Q8 9.65 8 8t1.175-2.825T12 4t2.825 1.175T16 8t-1.175 2.825T12 12t-2.825-1.175M4 20v-2.8q0-.85.438-1.562T5.6 14.55q1.55-.775 3.15-1.162T12 13t3.25.388t3.15 1.162q.725.375 1.163 1.088T20 17.2V20z\"\u002F>",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":11},"\u003Cpath fill=\"currentColor\" d=\"M7.8 2h8.4C19.4 2 22 4.6 22 7.8v8.4a5.8 5.8 0 0 1-5.8 5.8H7.8C4.6 22 2 19.4 2 16.2V7.8A5.8 5.8 0 0 1 7.8 2m-.2 2A3.6 3.6 0 0 0 4 7.6v8.8C4 18.39 5.61 20 7.6 20h8.8a3.6 3.6 0 0 0 3.6-3.6V7.6C20 5.61 18.39 4 16.4 4zm9.65 1.5a1.25 1.25 0 0 1 1.25 1.25A1.25 1.25 0 0 1 17.25 8A1.25 1.25 0 0 1 16 6.75a1.25 1.25 0 0 1 1.25-1.25M12 7a5 5 0 0 1 5 5a5 5 0 0 1-5 5a5 5 0 0 1-5-5a5 5 0 0 1 5-5m0 2a3 3 0 0 0-3 3a3 3 0 0 0 3 3a3 3 0 0 0 3-3a3 3 0 0 0-3-3\"\u002F>",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":13},"\u003Cpath fill=\"currentColor\" d=\"m10 15l5.19-3L10 9zm11.56-7.83c.13.47.22 1.1.28 1.9c.07.8.1 1.49.1 2.09L22 12c0 2.19-.16 3.8-.44 4.83c-.25.9-.83 1.48-1.73 1.73c-.47.13-1.33.22-2.65.28c-1.3.07-2.49.1-3.59.1L12 19c-4.19 0-6.8-.16-7.83-.44c-.9-.25-1.48-.83-1.73-1.73c-.13-.47-.22-1.1-.28-1.9c-.07-.8-.1-1.49-.1-2.09L2 12c0-2.19.16-3.8.44-4.83c.25-.9.83-1.48 1.73-1.73c.47-.13 1.33-.22 2.65-.28c1.3-.07 2.49-.1 3.59-.1L12 5c4.19 0 6.8.16 7.83.44c.9.25 1.48.83 1.73 1.73\"\u002F>",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":15},"\u003Cpath fill=\"currentColor\" d=\"M6.94 5a2 2 0 1 1-4-.002a2 2 0 0 1 4 .002M7 8.48H3V21h4zm6.32 0H9.34V21h3.94v-6.57c0-3.66 4.77-4 4.77 0V21H22v-7.93c0-6.17-7.06-5.94-8.72-2.91z\"\u002F>",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":17},"\u003Cpath fill=\"currentColor\" d=\"M10.488 14.651L15.25 21h7l-7.858-10.478L20.93 3h-2.65l-5.117 5.886L8.75 3h-7l7.51 10.015L2.32 21h2.65zM16.25 19L5.75 5h2l10.5 14z\"\u002F>",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":19},"\u003Cpath fill=\"currentColor\" d=\"M14 13.5h2.5l1-4H14v-2c0-1.03 0-2 2-2h1.5V2.14c-.326-.043-1.557-.14-2.857-.14C11.928 2 10 3.657 10 6.7v2.8H7v4h3V22h4z\"\u002F>",{"postType":21,"id":22,"author":23,"date":26,"modified":27,"title":28,"slug":29,"seoTitle":30,"seoDescription":31,"excerpt":32,"trimmedExcerpt":33,"content":34,"readingTime":35,"featuredImage":36,"postCategory":42,"relatedPosts":47},"post",64496,{"id":24,"name":25},"author-0301142015","Canto","2024-03-22T14:06:42","2025-11-13T09:48:55","Decoding the future: machine learning vs deep learning","machine-learning-vs-deep-learning","Machine learning vs deep learning explained","Find out the different real-world applications of machine learning vs deep learning and how tomorrow’s problems might be solved by AI.","When people talk about AI, they use a puzzling collection of terms. You may have heard about deep learning as a subset of something called machine learning. What is so deep about deep learning? Also, how do all these different flavors of AI use data to learn? A history of machine learning Pretend you are […]","When people talk about AI, they use a puzzling collection of terms. You may have heard about deep learning as...","\u003Cp class=\"wp-block-paragraph\">When people talk about AI, they use a puzzling collection of terms. You may have heard about deep learning as a subset of something called machine learning.\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">What is so deep about deep learning?\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">Also, how do all these different \u003Cem>flavors of AI use data to learn\u003C\u002Fem>?\u003C\u002Fp>\u003Ch2 class=\"wp-block-heading\" id=\"toc-1--a-history-of-machine-learning\">A history of machine learning\u003C\u002Fh2>\u003Cp class=\"wp-block-paragraph\">Pretend you are a data scientist about 20 years ago. You look around for problems to solve with the existing technologies, and those problems generally had a finite set of possible answers. Some examples of problems you could solve included:\u003C\u002Fp>\u003Cul class=\"wp-block-list\">\u003Cli>\u003Cstrong>Loan qualification:\u003C\u002Fstrong> Understanding whether someone is qualified for a loan they have applied for. This would involve training a machine learning model on decades of history of good and bad loans and feeding in someone’s application. The model would output either yes (make the loan) or no (decline the loan).\u003C\u002Fli>\u003Cli>\u003Cstrong>SPAM email identification:\u003C\u002Fstrong> Detecting if an email is spam by using data to train a spam detection model. The model would output either yes (the email is SPAM) or no (the email is legitimate).\u003C\u002Fli>\u003C\u002Ful>\u003Cp class=\"wp-block-paragraph\">Both examples are called \u003Cem>classifiers\u003C\u002Fem> because they output one of a finite set of classifications.\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">Other kinds of models can be trained to answer questions like what someone’s house is worth based on its location, size, and age. The output is a single number (and we have lots of records of comparable houses and what they sold for).\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">These are all examples of \u003Cem>useful \u003Ca href=\"\u002Fblog\u002Fbasics-of-machine-learning\u002F\">machine learning\u003C\u002Fa> applications\u003C\u002Fem>, with decent sizes of training data available, and these applications are just limited by the magnitude of the problem we are trying to solve.\u003C\u002Fp>\u003Ch2 class=\"wp-block-heading\" id=\"toc-2--machine-learning-evolves\">Machine learning evolves\u003C\u002Fh2>\u003Cp class=\"wp-block-paragraph\">Let’s move forward a decade or so, and our ambitions have grown: we want to tackle much bigger problems using machine learning! As a data scientist, you must figure out what it will take to solve this new set of problems.\u003C\u002Fp>\u003Cfigure class=\"wp-block-image aligncenter\">\u003Cimg loading=\"lazy\" decoding=\"async\" width=\"880\" height=\"450\" src=\"\u002Fcdn\u002Fen\u002F2024\u002F03\u002F19183931\u002Fmachine-learning-evolves-into-deep-learning.jpg\" alt=\"Image of a man at a laptop with big data, a neural network, and a GPU hovering around him over an orange background.\" class=\"wp-image-64501\" srcset=\"\u002Fcdn\u002Fen\u002F2024\u002F03\u002F19183931\u002Fmachine-learning-evolves-into-deep-learning.jpg 880w, \u002Fcdn\u002Fen\u002F2024\u002F03\u002F19183931\u002Fmachine-learning-evolves-into-deep-learning-450x230.jpg 450w, \u002Fcdn\u002Fen\u002F2024\u002F03\u002F19183931\u002Fmachine-learning-evolves-into-deep-learning-768x393.jpg 768w\" \u002F>\u003C\u002Ffigure>\u003Cp class=\"wp-block-paragraph\">Turns out, it takes three things:\u003C\u002Fp>\u003Ch3 class=\"wp-block-heading\">1. Deep learning\u003C\u002Fh3>\u003Cp class=\"wp-block-paragraph\">Previously, we got our feet wet using \u003Cem>neural networks\u003C\u002Fem>: arrays of virtual (because they are written in software) nodes that recognize patterns in data and \u003Cem>learn\u003C\u002Fem> from them how to classify different things. Machine learning problems that were tackled back then used \u003Cem>shallow models\u003C\u002Fem> that didn’t have many nodes.\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">To solve today’s gigantic problems that sometimes can have infinite answers, data scientists realized it was going to take many layers of many of these nodes for models to do the job. That is where the term \u003Cem>deep\u003C\u002Fem> comes from. \u003Ca href=\"\u002Fblog\u002Funcovering-the-mystery-of-deep-learning\u002F\">Deep learning\u003C\u002Fa> refers to the depth of the nodes in the models we are going to have to create.\u003C\u002Fp>\u003Ccto-card white=\"true\" shadow=\"false\">\u003Ch3 class=\"wp-block-heading\">“Today, we are in the world of deep learning.”\u003C\u002Fh3>\u003C\u002Fcto-card>\u003Cp class=\"wp-block-paragraph\">But what we need for deep learning to work does not stop with building deep models!\u003C\u002Fp>\u003Ch3 class=\"wp-block-heading\">2. Big Data\u003C\u002Fh3>\u003Cp class=\"wp-block-paragraph\">Any flavor of machine learning needs data. After all, how can there be \u003Cem>learning\u003C\u002Fem> without data to learn from? Previously in the machine learning world, we needed to train our models on data, but now that we are tackling much deeper problems, it follows we need much more data. Here are two examples of ambitious problems to data scientists are now trying to solve:\u003C\u002Fp>\u003Cul class=\"wp-block-list\">\u003Cli>\u003Cstrong>Object identification in photography:\u003C\u002Fstrong> For a model to identify all the objects in a photo, it must know about all possible objects that might be in the photo.\u003C\u002Fli>\u003Cli>\u003Cstrong>Translation between languages:\u003C\u002Fstrong> To translate from English to Italian, a model would have to know the full vocabulary in both languages, how their grammars work, and what the idioms of each language are (telling a friend to “break a leg” before a performance might not translate well into other languages).\u003C\u002Fli>\u003C\u002Ful>\u003Cp class=\"wp-block-paragraph\">These models would require a massive data training set to work well (bigger than all the entries in Wikipedia)! Fortunately, we live in the time of \u003Cem>big data\u003C\u002Fem>.\u003C\u002Fp>\u003Ch3 class=\"wp-block-heading\">3. Graphics processing units (GPUs)\u003C\u002Fh3>\u003Cp class=\"wp-block-paragraph\">Last, but not least, to train a model with these enormous sets of data, we need massive computing resources. Traditionally, computers are powered by a CPU (central processing unit). CPUs make computers run fast, but they mostly work on one task at a time and are optimized to handle numbers.\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">Most deep learning models work with things called \u003Cem>vectors\u003C\u002Fem>, which you can think of as arrows of a specific length pointing in a specific direction. You can add, subtract, and multiply vectors, but doing it on a CPU is incredibly slow.\u003C\u002Fp>\u003Ccto-card white=\"true\" shadow=\"false\">\u003Ch3 class=\"wp-block-heading\">“It could take dozens of years to train a language translator using CPUs. That just isn’t viable.”\u003C\u002Fh3>\u003C\u002Fcto-card>\u003Cp class=\"wp-block-paragraph\">Luckily, starting about 20 years ago, a market evolved for passionate computer gamers. These were people who demanded ever-improving graphics for their games. They are great for 2D and 3D graphics, and they scale to meet different display sizes well. But a different kind of chip was needed for the unique task for vector computation.\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">GPUs (graphics processing units) were created and are terrific at vector manipulation. Not just that, but vector operations lend themselves well to parallelization: two GPUs can work together to get a job done twice as fast. GPUs can handle vector operations a thousand times faster than CPUs, and you can run hundreds or thousands of GPUs in parallel.\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">Powered by GPUs, a language translator can be trained in a reasonable amount of time.\u003C\u002Fp>\u003Ch2 class=\"wp-block-heading\" id=\"toc-3--the-success-of-ai-today\">The success of AI today\u003C\u002Fh2>\u003Cp class=\"wp-block-paragraph\">After decades of disappointment and unfulfilled promises (after all, AI research started in 1956), AI has finally hit the big time. We now have immense stores of data (much of it publicly accessible), clever data scientists, and GPUs for vector manipulation.\u003C\u002Fp>\u003Cp class=\"wp-block-paragraph\">Just imagine: many problems that humans have struggled with for centuries might be solved with the help of AI.\u003C\u002Fp>",5,{"url":37,"url_md":38,"alt":39,"height":40,"width":41},"https:\u002F\u002Fwww.canto.com\u002Fcdn\u002Fen\u002F2024\u002F03\u002F19183932\u002Fmachine-learning-vs-deep-learning-feature.jpg","https:\u002F\u002Fwww.canto.com\u002Fcdn\u002Fen\u002F2024\u002F03\u002F19183932\u002Fmachine-learning-vs-deep-learning-feature-450x225.jpg","An image of a laptop in a shallow node machine learning model vs a deep node deep learning model over a green background with gears.",600,1200,[43],{"id":44,"slug":44,"title":45,"path":46},"ai","Artificial Intelligence","\u002Fpost-category\u002Fai\u002F",[48,55,62],{"featuredImage":49,"postId":50,"slug":51,"title":52,"description":53,"path":54},"https:\u002F\u002Fwww.canto.com\u002Fcdn\u002Fen\u002F2023\u002F12\u002F19184016\u002FAI-face-recognition_feature-450x263.jpg",64089,"ai-face-recognition","How to unlock the power of AI facial recognition","Learn how teams can leverage AI facial recognition to speed up content search and retrieval, save money, and create better content programs.","\u002Fblog\u002Fai-face-recognition\u002F",{"featuredImage":56,"postId":57,"slug":58,"title":59,"description":60,"path":61},"https:\u002F\u002Fwww.canto.com\u002Fcdn\u002Fen\u002F2020\u002F05\u002F19191819\u002Fartificial-intelligence-management-450x299.jpg",44850,"artificial-intelligence-management","Artificial intelligence management – a growing necessity","Artificial intelligence can only boost your company so much without powerful management processes. Learn more here about these important tools.","\u002Fblog\u002Fartificial-intelligence-management\u002F",{"featuredImage":63,"postId":64,"slug":65,"title":66,"description":67,"path":68},"https:\u002F\u002Fwww.canto.com\u002Fcdn\u002Fen\u002F2020\u002F04\u002F19191912\u002Fai-advertising-450x311.jpg",44377,"ai-advertising","The most important ways AI is changing advertising","Artificial intelligence is a growing technological component that has unique benefits for companies. Check out how it's revolutionizing advertisements.","\u002Fblog\u002Fai-advertising\u002F",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":70},"\u003Cpath fill=\"currentColor\" d=\"M9.175 10.825Q8 9.65 8 8t1.175-2.825T12 4t2.825 1.175T16 8t-1.175 2.825T12 12t-2.825-1.175M4 20v-2.8q0-.85.438-1.562T5.6 14.55q1.55-.775 3.15-1.162T12 13t3.25.388t3.15 1.162q.725.375 1.163 1.088T20 17.2V20zm2-2h12v-.8q0-.275-.137-.5t-.363-.35q-1.35-.675-2.725-1.012T12 15t-2.775.338T6.5 16.35q-.225.125-.363.35T6 17.2zm7.413-8.587Q14 8.825 14 8t-.587-1.412T12 6t-1.412.588T10 8t.588 1.413T12 10t1.413-.587M12 18\"\u002F>",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":72},"\u003Cpath fill=\"currentColor\" d=\"M12 14q-.425 0-.712-.288T11 13t.288-.712T12 12t.713.288T13 13t-.288.713T12 14m-4.712-.288Q7 13.426 7 13t.288-.712T8 12t.713.288T9 13t-.288.713T8 14t-.712-.288M16 14q-.425 0-.712-.288T15 13t.288-.712T16 12t.713.288T17 13t-.288.713T16 14m-4 4q-.425 0-.712-.288T11 17t.288-.712T12 16t.713.288T13 17t-.288.713T12 18m-4.712-.288Q7 17.426 7 17t.288-.712T8 16t.713.288T9 17t-.288.713T8 18t-.712-.288M16 18q-.425 0-.712-.288T15 17t.288-.712T16 16t.713.288T17 17t-.288.713T16 18M5 22q-.825 0-1.412-.587T3 20V6q0-.825.588-1.412T5 4h1V2h2v2h8V2h2v2h1q.825 0 1.413.588T21 6v14q0 .825-.587 1.413T19 22zm0-2h14V10H5z\"\u002F>",{"left":4,"top":4,"width":5,"height":5,"rotate":4,"vFlip":6,"hFlip":6,"body":74},"\u003Cpath fill=\"currentColor\" d=\"M12 20a8 8 0 0 0 8-8a8 8 0 0 0-8-8a8 8 0 0 0-8 8a8 8 0 0 0 8 8m0-18a10 10 0 0 1 10 10a10 10 0 0 1-10 10C6.47 22 2 17.5 2 12A10 10 0 0 1 12 2m.5 5v5.25l4.5 2.67l-.75 1.23L11 13V7z\"\u002F>",1787461311185]