Deep learning is essentially a neural network with three or more layers, included in the subset of machine learning. These neural networks aim to imitate the behavior of the human brain, although they are far from reaching its capability, enabling them to acquire knowledge from vast quantities of data. While a neural network with a solitary layer can still provide approximate predictions, incorporating extra hidden layers can enhance and fine-tune accuracy.
Deep learning is the force propelling numerous artificial intelligence (AI) applications and services, enhancing automation by executing analytical and physical tasks without the need for human involvement. Everyday products and services, including digital assistants, voice-enabled TV remotes, and credit card fraud detection, owe their functionality to deep learning technology. Additionally, deep learning plays a vital role in advancing technologies such as self-driving cars.
Deep learning vs. machine learning
How does deep learning differ from classical machine learning, considering that it is a subset of machine learning? Deep learning sets itself apart from classical machine learning through the type of data it handles and the methodologies it adopts for learning.
Machine learning algorithms rely on structured and labeled data to make predictions. This implies that certain characteristics are identified from the input data and arranged into tables for the model. It is important to note that machine learning algorithms can still utilize unstructured data, but typically require pre-processing to arrange it in a structured format.
Deep learning reduces the need for data pre-processing in machine learning. It allows algorithms to handle and analyze unstructured data such as text and images, while also automating the extraction of features. This eliminates the reliance on human experts to establish the hierarchy of features. For instance, if we had a collection of pet photos and wanted to classify them as “cat,” “dog,” “hamster,” etc., deep learning algorithms can identify which attributes (e.g., ears) are crucial for distinguishing each animal. In contrast, in traditional machine learning, a human expert would need to manually establish this hierarchy of features.
Afterward, by means of gradient descent and backpropagation, the deep learning algorithm adapts and aligns itself in order to enhance its accuracy, enabling it to make more precise predictions about a novel animal photograph.
Machine learning and deep learning models can be classified into three types of learning, namely supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, labeled datasets are used to classify or make predictions, necessitating human intervention for accurate labeling of input data. On the other hand, unsupervised learning does not need labeled datasets and instead identifies patterns in the data, grouping them based on distinctive features. Reinforcement learning involves the model learning and improving its accuracy in performing actions within an environment by receiving feedback with the aim of maximizing rewards.
How deep learning works
Deep learning neural networks, also known as artificial neural networks, strive to imitate the functioning of the human brain by utilizing data inputs, weights, and bias simultaneously. By collaborating, these components enable precise identification, categorization, and depiction of objects present in the given data.
Deep neural networks are composed of interconnected nodes organized into multiple layers, with each layer enhancing and optimizing the prediction or categorization based on the previous one. This sequential passing of calculations throughout the network is referred to as forward propagation. The visible layers in a deep neural network are the input and output layers. The input layer is responsible for receiving and processing the data in the deep learning model, while the output layer is where the final prediction or classification occurs.
Backpropagation, which involves using algorithms such as gradient descent, is employed to calculate prediction errors and subsequently modifies the weights and biases of the function by moving backwards through the layers in order to train the model. The combined use of forward propagation and backpropagation enables a neural network to make predictions and rectify any errors as needed. Consequently, the accuracy of the algorithm improves gradually over time.
the aforementioned explanation covers the most basic form of deep neural network using straightforward language. Nevertheless, deep learning algorithms are highly intricate, and there exist various kinds of neural networks designed to tackle specific problems or datasets. To illustrate this,
- Convolutional neural networks (CNNs), used primarily in computer vision and image classification applications, can detect features and patterns within an image, enabling tasks, like object detection or recognition. In 2015, a CNN bested a human in an object recognition challenge for the first time.
- Recurrent neural network (RNNs) are typically used in natural language and speech recognition applications as it leverages sequential or times series data.
Best Deep Learning Courses
1. Best Overall Course: Deep Learning Nanodegree (Udacity)
This course is open to those with a basic understanding of Python programming, but beginners can also learn programming alongside the course as it starts with easy lessons. Deep learning expertise is not necessary to enroll.
This course is ideal for individuals seeking to incorporate a complete and extensive deep learning certification into their skillset. While covering various aspects of artificial intelligence, the course mainly focuses on in-depth understanding of neural networks. Initially, students gain a broad understanding of neural networks, and later the course delves further into convolutional neural networks and recurrent neural networks as separate topics.
This course instructs students on setting up neural networks for various tasks like image recognition and classification, using five customized projects. Furthermore, you will gain knowledge on establishing the fundamental systems of AI-assisted tasks and implementing projects utilizing PyTorch and Amazon Sagemaker as resources.
Upon successful completion of this course, you will automatically gain entry into Udacity’s advanced programs on AI, namely the Self-Driving Car Engineer and Flying Car and Autonomous Flight Engineer programs. It is important to note, however, that despite the automatic admission, separate payment will be required for these programs.
2. Best Free Access: Deep Learning Specialization (Coursera)
This course on deep learning from Coursera is designed for students who have a basic understanding of Python programming and algebra. While having previous knowledge in deep learning is advantageous, it is not a requirement.
Throughout this deep learning course, you will acquire knowledge on several subjects concerning A.I and deep learning, including:
- Neural Networks (Convolutional)
- Hyperparameter tuning, Regularization, and Optimization
- Structuring Deep Learning Projects
- Sequence Modelling (in the context of natural language processing)
Although the names of these subjects may initially appear perplexing, the syllabus has been effectively simplified and explained by the course instructor. The content initiates with fundamental concepts and gradually progresses to the more advanced aspects of deep learning.
Upon completion of this course, you will acquire a strong basis of skills, enabling you to initiate the construction of your own convolutional neural networks. Furthermore, the incorporation of lectures on natural language processing in the course outline is highly appreciated. Despite being regarded as a crucial subject in deep learning, numerous courses in this selection neglect the provision of comprehensive NLP coverage.
3. Best for Beginners: Complete Guide to TensorFlow for Deep Learning with Python (Udemy)
This course is ideal for anyone interested in exploring Google’s TensorFlow system. While prior programming knowledge is not necessary, it can be advantageous as the course material is introductory.
This comprehensive online training program introduces the fundamentals of deep learning and neural networks, including Python, A.I, and mathematics. The course, taught by Jose Portilla, utilizes the TensorFlow framework to provide a practical understanding of deep learning without abstract concepts. It offers a strong foundation and empowers students to develop their own neural networks.
The course provides a thorough understanding of deep learning, starting with the basics and gradually progressing to technical skills and exercises that empower you to create your own neural networks. Although it may not be the most advanced deep learning course, it effectively addresses the foundational concepts.
4. Deep Learning A-Z: Hands-On Artificial Neural Networks (Udemy)
This course is suitable for students who are eager to delve into coding their own deep learning algorithms. Moreover, individuals seeking a program that instructs deep learning training with PyTorch and TensorFlow will discover ample knowledge in this course. The content is moderately fundamental, thus making it suitable for beginners.
In this course, you will be taught the fundamentals of neural networks and the significance of deep learning in software development. Subsequently, a comprehensive blend of TensorFlow and PyTorch exercises is provided. These exercises enable students to replicate practical instances of deep learning software, including recommender systems and image recognition programs. The step-by-step exercises are designed to ensure simplicity in comprehending the technical aspects, while the theory classes are easily comprehendible.
5. Deep Learning, by 3Blue1Brown (YouTube)
This course on deep learning is distinct from others on the list as it is incredibly easy to comprehend, demands no prior knowledge, and is suitable for individuals of any background who are interested in the concepts of deep learning and neural networks.
In this video course, developed by 3Blue1Brown, you will gain knowledge on the fundamentals of neural networks, as well as understanding of the workings of the human brain and its problem-solving abilities. The course content is presented with the aid of visual aids such as pictures, graphs, and animations, ensuring that it is easily comprehensible and accessible.
In total, there are four video chapters, with each addressing a distinct question.
- What is a neural network?
- Gradient descent, how do neural networks learn?
- What is backpropagation really doing?
- Backpropagation calculus
The videos are all beautifully illustrated, demonstrating that even complex topics can be taught using simple techniques. This deep learning course stands out as the most beginner-friendly option in terms of accessibility.
Deep learning applications
Examples of real-world deep learning applications are so seamlessly integrated into products and services that users are often oblivious to the intricate data processing happening behind the scenes, which is now an integral part of our daily lives. Some instances of such applications are as follows:
Law enforcement
By examining transactional data, deep learning algorithms have the capability to detect harmful patterns that may suggest potential fraudulent or criminal behavior. Applying deep learning techniques such as speech recognition and computer vision can enhance the efficiency and efficacy of investigative analysis by extracting patterns and evidence from various sources like audio and video recordings, images, and documents. This aids law enforcement in analyzing vast volumes of data with greater speed and precision.
Financial services
Predictive analytics is frequently utilized by financial institutions to execute algorithmic trading of stocks, evaluate business risks when approving loans, identify fraudulent activities, and assist in the management of credit and investment portfolios for customers.
Customer service
A large number of organizations integrate deep learning technology into their customer service procedures. Chatbots, which can be applied in various applications, services, and customer service platforms, are a simple type of artificial intelligence. Traditional chatbots make use of natural language and visual recognition, often seen in menus resembling call centers. Nevertheless, advanced chatbot solutions aim to identify, by means of learning, whether ambiguous questions may have multiple answers. Depending on the received responses, the chatbot endeavors to provide direct answers to these questions or transfer the conversation to a human user.
By incorporating speech recognition functionality, virtual assistants such as Apple’s Siri, Amazon Alexa, or Google Assistant expand upon the concept of chatbots and offer a personalized means of interacting with users.
Healthcare
Since the digitization of hospital records and images, the healthcare industry has experienced significant advantages from the advanced abilities of deep learning. Medical imaging specialists and radiologists can receive support from image recognition applications, enabling them to analyze and evaluate a larger quantity of images within a shorter timeframe.
Deep learning hardware requirements
To effectively utilize deep learning, a significant level of computational capacity is necessary. The most suitable option is using high-performance graphical processing units (GPUs) as they possess the ability to efficiently perform extensive calculations across multiple cores and have ample memory capacity. Nonetheless, handling multiple GPUs within a company premises can place a significant strain on internal resources and prove to be highly expensive when expanding the scale.
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