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    Deep Learning: Advancing Robotics Through Intelligent Systems
    Deep Learning: Advancing Robotics Through Intelligent Systems
    Deep Learning: Advancing Robotics Through Intelligent Systems
    Ebook336 pages3 hoursRobotics Science

    Deep Learning: Advancing Robotics Through Intelligent Systems

    By Fouad Sabry

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    About this ebook

    "Deep Learning" is an essential guide to the evolving world of robotics, offering indepth insights into the revolutionary field of artificial intelligence. Whether you're a professional, a student, or an enthusiast, this book provides the foundation necessary to understand the complex principles behind machine learning and neural networks. Explore how these technologies are shaping the future of robotics, from speech recognition to quantum neural networks, and gain the knowledge needed to stay ahead in a rapidly advancing field.


    Chapters Brief Overview:


    1: Deep learning: Introduction to deep learning and its applications in robotics and AI.


    2: Neural network (machine learning): Understanding the fundamental structure and learning processes of neural networks.


    3: Speech recognition: How deep learning powers speech recognition technologies, enabling more intuitive humanrobot interaction.


    4: Jürgen Schmidhuber: A deep dive into the contributions of Jürgen Schmidhuber, a key figure in neural network advancements.


    5: Recurrent neural network: The role of recurrent neural networks (RNNs) in processing sequential data and time series.


    6: Quantum neural network: Exploring the intersection of quantum computing and neural networks, opening new dimensions for AI.


    7: Echo state network: A look into echo state networks (ESNs) and their efficiency in complex dynamic systems.


    8: Long shortterm memory: An exploration of LSTM networks and their ability to retain longterm information, critical in robotics.


    9: Types of artificial neural networks: Overview of various neural network types and their specific applications in robotics.


    10: Convolutional neural network: Understanding CNNs and their impact on image processing and visual recognition in robotics.


    11: Bidirectional recurrent neural networks: A study of bidirectional RNNs and their ability to process data from both past and future contexts.


    12: Alex Graves (computer scientist): Focusing on the pioneering work of Alex Graves in neural networks and AI, and its impact on robotics.


    13: AI accelerator: Examining the hardware advancements, such as AI accelerators, that enhance deep learning model performance.


    14: Timeline of machine learning: A historical overview of key milestones in the development of machine learning and AI.


    15: Differentiable neural computer: A look at differentiable neural computers (DNCs) and their potential to revolutionize memory and problemsolving in robots.


    16: AlexNet: Understanding the groundbreaking AlexNet model and its role in popularizing deep learning for image classification.


    17: Connectionist temporal classification: An exploration of CTC for speech and sequence processing, vital for humanrobot communication.


    18: Highway network: The significance of highway networks in overcoming the limitations of deep architectures for improved learning.


    19: Residual neural network: Studying residual networks and how they help train very deep neural networks for robotics.


    20: History of artificial neural networks: A comprehensive history of neural networks, from their inception to their dominance in modern AI.


    21: Attention Is All You Need: A deep dive into the transformer model, which has revolutionized natural language processing in robotics.


    The world of robotics is rapidly transforming, and the advancements in deep learning are driving much of this change. This book serves as a comprehensive resource for professionals, students, and hobbyists interested in understanding the theoretical and practical aspects of deep learning in robotics. Gain insights from the experts, discover cuttingedge technologies, and see how deep learning is poised to shape the future of AI and robotic

    LanguageEnglish
    PublisherOne Billion Knowledgeable
    Release dateJan 1, 2025
    Deep Learning: Advancing Robotics Through Intelligent Systems

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      Book preview

      Deep Learning - Fouad Sabry

      Chapter 1: Deep learning

      Deep learning, also known as deep structured learning, is a subset of a larger family of machine learning approaches that are based on artificial neural networks and representation learning. Deep learning is also known as deep structured learning. It's possible to learn in a supervised, semi-supervised, or unsupervised setting.

      Deep-learning Architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, and convolutional neural networks have been applied to fields such as computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection, and board game programs, where they have produced results comparable to and, in some cases, exceeding those produced by humans.

      The employment of numerous layers in the network is what is meant by the term deep when referring to deep learning. Earlier research shown that a linear perceptron cannot perform as a universal classifier; however, a network with a nonpolynomial activation function and one hidden layer that has an unlimited width may. Deep learning is a relatively recent development that is concerned with an endless number of layers that are constrained in size. This allows for practical application and efficient implementation, while yet maintaining theoretical universality under moderate circumstances. Deep learning allows for the layers to be heterogeneous and to diverge significantly from physiologically informed connectionist models. This is done for the purpose of improving efficiency, trainability, and understandability, which is where the structured portion of the term comes from.

      Deep learning is a class of machine learning algorithms that: 199–200 uses multiple layers to progressively extract higher-level features from the raw input.

      For example, in the field of image processing, edges may be identified on lower levels, while higher layers could recognize human-relevant things like numerals, characters, or faces, such as they are, for example,.

      Deep learning models can also include propositional formulas or latent variables organized layer-wise in deep generative models, such as the nodes in deep belief networks and deep Boltzmann machines. The vast majority of contemporary deep learning models are based on artificial neural networks, more specifically convolutional neural networks (CNNs). After that point, adding more layers to the network does not improve its capacity to approximate functions. Since deep models (CAP > 2) are able to extract better features than shallow models, the addition of more layers assists in efficiently learning the features.

      The greedy layer-by-layer strategy may be used to develop deep learning architectures.

      Deep learning methods eliminate the need for feature engineering when applied to supervised learning tasks. They do this by translating the data into compact intermediate representations, which are analogous to principal components. These methods also derive layered structures that remove redundancy in representation.

      Unsupervised learning problems are a good fit for the use of deep learning techniques. This is a significant advantage on account of the greater availability of unlabeled data in comparison to labeled data. Deep belief networks are one example of a kind of deep structure that is capable of being taught in an unsupervised way.

      If the width of a deep neural network with ReLU activation is strictly larger than the input dimension, then the network can approximate any Lebesgue integrable function; if the width of a deep neural network is smaller than or equal to the input dimension, then a deep neural network is not a universal approximator. Deep neural networks are generally interpreted in terms of the universal approximation theorem.

      The probabilistic interpretation

      According to a few different accounts, Frank Rosenblatt was the one who first invented and investigated each of the fundamental components that are used in today's deep learning systems. In the book he authored titled Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms, which was released by Cornell Aeronautical Laboratory, Inc., Cornell University in 1962, he provided an explanation of this phenomenon.

      In 1967, Alexey Ivakhnenko and Lapa presented the first generic, functioning learning method for supervised, deep, feedforward, multilayer perceptrons. This algorithm was developed by Alexey Ivakhnenko. by use of linkages both forward and backward in order to facilitate the flexible incorporation of context into choices and the iterative resolution of local issues.

      Because of the high computational cost of artificial neural networks (ANN) and a lack of understanding of how the brain wires its biological networks, simpler models that use task-specific handcrafted features like Gabor filters and support vector machines (SVMs) were a popular choice in the 1990s and 2000s.

      ANNs have been investigated for a great number of years using both shallow and deep learning (for example, recurrent networks). Additional challenges were a dearth of relevant training data and a scarcity of computational resources.

      The majority of research into voice recognition has shifted away from neural networks and toward generative modeling. One notable exception occurred during the late 1990s at SRI International. Deep neural networks were investigated by SRI for its potential use in voice and speaker detection. This research was made possible by funding from the NSA and DARPA. During the speaker recognition assessment that took place in 1998 at the National Institute of Standards and Technology (NIST), the team working on speaker recognition that was headed by Larry Heck claimed substantial progress using deep neural networks in audio processing. Learning for deep belief networks was the topic that was discussed in the publications.

      Deep learning is an essential component of the most cutting-edge systems in a variety of fields, including computer vision and automated voice recognition in particular (ASR). Results have gradually increased across a variety of large-vocabulary voice recognition tasks, as well as evaluation sets that are routinely utilized, such as TIMIT (automated speech recognition) and MNIST (image classification). However, they do better in terms of computer vision.

      According to Yann LeCun, the first signs of the influence that deep learning would have on business started to emerge in the early 2000s. At that time, CNNs were already processing an estimated 10–20 percent of all the checks that were signed in the United States. Around the year 2010, businesses first began applying deep learning techniques to large-scale voice recognition projects.

      The limitations of deep generative models of speech, as well as the possibility that deep neural nets (DNN), given more capable hardware and large-scale data sets, might become practical, served as the impetus for the 2009 NIPS Workshop on Deep Learning for Speech Recognition. This workshop was organized by the National Institute for Pattern Recognition (NIPS). It was thought that the most significant challenges associated with neural nets might be circumvented by pre-training DNNs using generative models of deep belief networks (DBN). However, it was discovered that replacing pre-training with large amounts of training data for straightforward backpropagation when using DNNs with large, context-dependent output layers produced error rates that were significantly lower than the then-state-of-the-art Gaussian mixture model (GMM)/Hidden Markov Model (HMM) and also than more advanced generative model-based systems. This was accomplished by using DNNs with large, context-dependent output layers.

      In 2012, a group that George E. Dahl was leading took first place in the Merck Molecular Activity Challenge by successfully predicting the biomolecular target of a single medication using multi-task deep neural networks. Following a pattern seen in large-scale voice recognition, the error rate on the ImageNet test that used deep learning saw a further reduction in 2013 and 2014 respectively.

      After then, image classification was expanded to the more difficult goal of producing descriptions (captions) for pictures, often using a mix of CNNs and LSTMs. Image classification has come a long way since then.

      Yoshua Bengio, Geoffrey Hinton, and Yann LeCun were honored with the Turing Award in March of 2019, for their contributions to the conceptual and technical achievements that have made deep neural networks an essential component of computing.

      Computing systems known as artificial neural networks (ANNs), also known as connectionist systems, take their inspiration from the biological neural networks that make up animal brains. Typically, task-specific programming is not required for these kinds of systems since they learn to do tasks by analyzing samples and gradually improving their capabilities. For instance, in image recognition, they could learn to recognize images that contain cats by analyzing example images that have been manually labeled as cat or no cat, and then using the results of the analysis to recognize cats in other images. In this way, they could learn to identify images that contain cats. Applications that are difficult to represent using a standard computer algorithm employing rule-based programming are the ones that have found the most usage for them.

      The foundation of an ANN is made up of a network of interconnected components known as artificial neurons (analogous to biological neurons in a biological brain). A signal may be sent from one neuron to another through each link (synapse) that exists between neurons. The signal (or signals) may be processed by the receiving neuron, also known as a postsynaptic neuron, which can then signal downstream neurons attached to it. There is some evidence that neurons may have states, which are normally represented by real numbers, typically ranging from 0 to 1. The intensity of the signal that a neuron or synaptic connection transmits downstream may be amplified or diminished depending on whether or not the neuron or synaptic connection's weight changes as learning progresses.

      In most cases, neurons are arranged in layered structures. The inputs to the various levels may be subjected to a variety of changes, depending on the layer. Signals are sent from the first (input) layer to the final (output) layer, maybe after traveling through the layers more than once.

      The aim of the neural network technique, when it was first conceived, was to find solutions to problems in the same manner as a human brain would. Backpropagation, or the process of conveying information in the other way and altering the network to reflect that information, is one example of a divergence from biology that arose as a result of the gradual shift in emphasis, through time, to the matching of certain mental talents.

      Computer vision, voice recognition, machine translation, social network filtering, playing board games and video games, and medical diagnosis are just some of the applications that have made use of neural

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