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Deep learning (DL) is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost.
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In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.
- In a word, accuracy. Deep learning achieves recognition accuracy at higher levels than ever before. This helps consumer electronics meet user expectations, and it is crucial for safety-critical applications like driverless cars. Recent advances in deep learning have improved to the point where deep learning outperforms humans in some tasks like classifying objects in images.
Deep learning already implemented in many industries and sectores, such as.
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Virtual assistants Whether it’s Alexa or Siri or Cortana, the virtual assistants of online service providers use deep learning to help understand your speech and the language humans use when they interact with them.
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Translations In a similar way, deep learning algorithms can automatically translate between languages. This can be powerful for travelers, business people and those in government.
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Vision for driverless delivery trucks, drones and autonomous cars The way an autonomous vehicle understands the realities of the road and how to respond to them whether it’s a stop sign, a ball in the street or another vehicle is through deep learning algorithms. The more data the algorithms receive, the better they are able to act human-like in their information processing—knowing a stop sign covered with snow is still a stop sign.
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Chatbots and service bots Chatbots and service bots that provide customer service for a lot of companies are able to respond in an intelligent and helpful way to an increasing amount of auditory and text questions thanks to deep learning.
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Image colorization Transforming black-and-white images into color was formerly a task done meticulously by human hand. Today, deep learning algorithms are able to use the context and objects in the images to color them to basically recreate the black-and-white image in color. The results are impressive and accurate.
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Facial recognition Deep learning is being used for facial recognition not only for security purposes but for tagging people on Facebook posts and we might be able to pay for items in a store just by using our faces in the near future. The challenges for deep-learning algorithms for facial recognition is knowing it’s the same person even when they have changed hairstyles, grown or shaved off a beard or if the image taken is poor due to bad lighting or an obstruction.
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Medicine and pharmaceuticals From disease and tumor diagnoses to personalized medicines created specifically for an individual’s genome, deep learning in the medical field has the attention of many of the largest pharmaceutical and medical companies.
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Personalized shopping and entertainment Ever wonder how Netflix comes up with suggestions for what you should watch next? Or where Amazon comes up with ideas for what you should buy next and those suggestions are exactly what you need but just never knew it before? Yep, it’s deep-learning algorithms at work.
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Most deep learning methods use neural network architectures, which is why deep learning models are often referred to as deep neural networks.
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The term “deep” usually refers to the number of hidden layers in the neural network. Traditional neural networks only contain 2-3 hidden layers, while deep networks can have as many as 150.
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Deep learning models are trained by using large sets of labeled data and neural network architectures that learn features directly from the data without the need for manual feature extraction.
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One of the most popular types of deep neural networks is known as convolutional neural networks (CNN or ConvNet). A CNN convolves learned features with input data, and uses 2D convolutional layers, making this architecture well suited to processing 2D data, such as images.
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CNNs eliminate the need for manual feature extraction, so you do not need to identify features used to classify images. The CNN works by extracting features directly from images. The relevant features are not pretrained; they are learned while the network trains on a collection of images. This automated feature extraction makes deep learning models highly accurate for computer vision tasks such as object classification.
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Deep learning is a specialized form of machine learning. A machine learning workflow starts with relevant features being manually extracted from images. The features are then used to create a model that categorizes the objects in the image. With a deep learning workflow, relevant features are automatically extracted from images. In addition, deep learning performs “end-to-end learning” – where a network is given raw data and a task to perform, such as classification, and it learns how to do this automatically.
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Another key difference is deep learning algorithms scale with data, whereas shallow learning converges. Shallow learning refers to machine learning methods that plateau at a certain level of performance when you add more examples and training data to the network.
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Basics of Deep Learning Free Udemy Course
- Deep learning is a subset of artificial intelligence which is creating neural networks that mimic the human brain to solve complex problems like recognizing faces and objects. This course will teach you the foundation of this science without the need for any prior experience. Starting with the fundamentals and key concepts of this science called deep learning then you will move to practical lessons where you will see also how to improve the deep learning models like improving its accuracy.
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Neural Networks and Deep Learning Andrew Ng Course Course
- This course is part of Deep Learning Specialization on Coursera which is created by Andrew Ng and his DeepLearning.ai company. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, levels up your technical career, and take the definitive step in the world of AI.
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Artificial Intelligence Markup Language Free Udemy Course
- The course uses an AIML language to create the chatbot which is an XML-based programming language and you will start by learning the fundamentals such as using the tags and how to call the other categories as well as allowing the chatbot to learn from the users.
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Machine Learning with Python Free edX course
- This course created by the IBM company is going to help you learn more about this field using python language. You will start by understanding this science and its different models like supervised and unsupervised learning. Then you will move to practical lessons such as linear regressions and classification algorithms like a decision tree and logistic regression.
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Artificial Intelligence: The Big Picture of AI Pluralsight
- You will get an overview of artificial intelligence and its types as well as the component and different applications of this technology. You will also see the artificial intelligence history how it was and how it is right now like deep learning, reinforcement learning that makes machines learn by their mistakes.Finally, you will learn how artificial intelligence created and works and how models training with data as well as the future of this revolution.
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Applied Deep Learning: Build a Chatbot — Theory, Application Udemy
- This course will teach you how to apply Deep Learning. It’s an intermediate-level course so I basic knowledge of Deep Learning and Neural Networks. If you are already familiar, then you ready to start this journey!
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Tensorflow 2.0 | Recurrent Neural Networks, LSTMs, GRUs Udemy
- This is another awesome free online course to learn Tensorflow 2.0 on Udemy. You can use this 1-hour long free course to learn things like RNN, LSTM, GRU, NLP, Seq2Seq, Attention, and Time-series prediction. Recurrent Networks are an exciting type of neural network that deals with data that come in the form of a sequence. Sequences are all around us such as sentences, music, videos, and stock market graphs.
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Deep Learning Crash Course for Beginners FreeCodeCamp
- This course is developed by Jason Dsouza and in this course, you will learn the key ideas behind deep learning without any code. This course is designed for absolute beginners with no experience in programming and you will learn the fundamental concepts and terminology of Deep Learning, a sub-branch of Machine Learning. In short, a fantastic free deep learning course to learn about Neural Networks, Machine Learning constructs like Supervised, Unsupervised, and Reinforcement Learning, the various types of Neural Network architectures, and more.
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Data Science: Intro To Deep Learning With Python In 2022 Udemy
- This is a beginner-level course to create Deep Learning Algorithms in Python. This 2-hour long course is great for beginners to learn Deep Learning in the Python programming language.
- Models will be more like programs, and will have capabilities that go far beyond the continuous geometric transformations of the input data that we currently work with. These programs will arguably be much closer to the abstract mental models that humans maintain about their surroundings and themselves, and they will be capable of stronger generalization due to their rich algorithmic nature.
- In particular, models will blend algorithmic modules providing formal reasoning, search, and abstraction capabilities, with geometric modules providing informal intuition and pattern recognition capabilities. AlphaGo (a system that required a lot of manual software engineering and human-made design decisions) provides an early example of what such a blend between symbolic and geometric AI could look like.
- They will be grown automatically rather than handcrafted by human engineers, using modular parts stored in a global library of reusable subroutines—a library evolved by learning high-performing models on thousands of previous tasks and datasets. As common problem-solving patterns are identified by the meta-learning system, they would be turned into a reusable subroutine—much like functions and classes in contemporary software engineering—and added to the global library. This achieves the capability for abstraction.
- This global library and associated model-growing system will be able to achieve some form of human-like "extreme generalization": given a new task, a new situation, the system would be able to assemble a new working model appropriate for the task using very little data, thanks to 1) rich program-like primitives that generalize well and 2) extensive experience with similar tasks. In the same way that humans can learn to play a complex new video game using very little play time because they have experience with many previous games, and because the models derived from this previous experience are abstract and program-like, rather than a basic mapping between stimuli and action.
- As such, this perpetually-learning model-growing system could be interpreted as an AGI—an Artificial General Intelligence. But don't expect any singularitarian robot apocalypse to ensue: that's a pure fantasy, coming from a long series of profound misunderstandings of both intelligence and technology. This critique, however, does not belong here.
- https://www.mathworks.com/discovery/deep-learning.html
- https://medium.com/javarevisited/10-free-deep-learning-courses-for-beginners-37b5de61f8dc
- https://forbes.com/sites/bernardmarr/2018/10/01/what-is-deep-learning-ai-a-simple-guide-with-8-practical-examples/
- https://blog.keras.io/the-future-of-deep-learning.html