There is often confusion between deep learning and machine learning. And this is reasonable. Both topics are quite complex, and they do share many similarities.<\/p>\n\n\n\n
So to understand the differences, let\u2019s first take a look at two high-level aspects of machine learning and how they relate to deep learning. First of all, while both usually require large amounts of data, the types are generally different.<\/p>\n\n\n\n
Take the following example: Suppose we have photos of thousands of animals and want to create an algorithm to find the horses. Well, machine learning cannot analyze the photos themselves; instead, the data must be labeled. The machine\u00a0learning\u00a0algorithm will then be trained to recognize horses, through a process known as supervised learning (covered in Chapter\u00a03).<\/p>\n\n\n\n
Even though machine learning will likely come up with good results, they will still have limitations. Wouldn\u2019t it be better to look at the pixels of the images themselves\u2014and find the patterns? Definitely.<\/p>\n\n\n\n
But to do this with machine learning, you need to use a process called feature extraction. This means you must come up with the kinds of characteristics of a horse\u2014such as the shape, the hooves, color, and height\u2014which the algorithms will then try to identify.<\/p>\n\n\n\n
Again, this is a good approach\u2014but it is far from perfect. What if your features are off the mark or do not account for outliers or exceptions? In such cases, the accuracy of the model will likely suffer. After all, there are many variations to a horse. Feature extraction also has the drawback of ignoring a large amount of the data. This can be exceedingly complicated\u2014if not impossible\u2014for certain use cases. Look at computer viruses. Their structures and patterns, which are known as signatures, are constantly changing so as to infiltrate systems. But with feature extraction, a person would somehow have to anticipate this, which is not practical. This is why cybersecurity software is often about collecting signatures after a virus has exacted damage.<\/p>\n\n\n\n
But with deep learning, we can solve these problems. This approach analyzes all the data\u2014pixel by pixel\u2014and then finds the relationships by using a neural network, which mimics the human brain.<\/p>\n\n\n\n
Let\u2019s take a look.<\/p>\n\n\n\n
Deep learning is a subfield of machine learning. This type of system allows for processing huge amounts of data to find relationships and patterns that humans are often unable to detect. The word \u201cdeep\u201d refers to the number of hidden layers in the neural network, which provide much of the power to learn.<\/p>\n\n\n\n
When it comes to the topic of AI, deep learning is at the cutting-edge and often generates most of the buzz in mainstream media. \u201c[Deep learning] AI is the new electricity,\u201d extolled Andrew Yan-Tak Ng, who is the former chief scientist at Baidu and co-founder of\u00a0Google\u00a0Brain.3<\/sup><\/p>\n\n\n\n But it is also important to remember that deep learning is still in the early stages of development and commercialization. For example, it was not until about 2015 that Google started using this technology for its search engine.<\/p>\n\n\n\n As we saw in Chapter\u00a01, the\u00a0history\u00a0of\u00a0neural networks\u00a0was full of ebbs and flows. It was Frank Rosenblatt who created the perceptron, which was a fairly basic system. But real academic progress with neural networks did not occur until the 1980s, such as with the breakthroughs with\u00a0backpropagation, convolutional neural networks, and recurrent neural networks. But for deep learning to have an impact on the real world, it would take the staggering growth in data, such as from the Internet, and the surge in computing power.<\/p>\n","protected":false},"excerpt":{"rendered":" There is often confusion between deep learning and machine learning. And this is reasonable. Both topics are quite complex, and they do share many similarities. So to understand the differences, let\u2019s first take a look at two high-level aspects of machine learning and how they relate to deep learning. First of all, while both usually […]<\/p>\n","protected":false},"author":1,"featured_media":3327,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[442],"tags":[],"class_list":["post-3438","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-deep-learning"],"jetpack_featured_media_url":"https:\/\/workhouse.sweetdishy.com\/wp-content\/uploads\/2024\/08\/deep-learning-1.png","_links":{"self":[{"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/posts\/3438","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/comments?post=3438"}],"version-history":[{"count":1,"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/posts\/3438\/revisions"}],"predecessor-version":[{"id":3439,"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/posts\/3438\/revisions\/3439"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/media\/3327"}],"wp:attachment":[{"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/media?parent=3438"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/categories?post=3438"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/workhouse.sweetdishy.com\/index.php\/wp-json\/wp\/v2\/tags?post=3438"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}