Neural networks explained with the example of feedforward vs. recurrent networks. Feedforward networks propagate data, while recurrent models allow loops for cascade effects. Recurrent networks are less influential but closer to the brain's function. Introduction to handwritten digit classification

0 views • 55 slides

Explore the evolving landscape of 5G technology, from enhanced mobile broadband to groundbreaking use cases and standalone networks. Learn how supportive regulations and spectrum allocation are vital for unlocking 5G's full potential. Discover the transformative impact of Standalone 5G networks on i

8 views • 10 slides

Explore the fundamentals of neural networks, including artificial neurons and activation functions, in the context of deep learning. Learn about multilayer perceptrons and their role in forming decision regions for classification tasks. Understand forward propagation and backpropagation as essential

2 views • 74 slides

This document discusses the need to enhance relay frameworks in Wi-Fi networks to improve coverage, reliability, and performance of stations in different ranges. It highlights the challenges of S1G-based relays, proposes enhancements to the relay framework, and introduces new types of relay framewor

1 views • 12 slides

Wireless Communication Networks (WCN) is a fundamental aspect of modern telecommunication, allowing information transfer without physical connections. Dr. K. Gopi, an Associate Professor at the Department of ECE at SITAMS, introduces concepts like multiple access techniques, traffic routing, and the

1 views • 13 slides

Explore the concept of computer networks, how data is transmitted between computers, historic communication methods, common daily activities using computer networks, key milestones in internet history, and more in this informative lesson. Discover the evolution of communication from carrier pigeons

8 views • 12 slides

In the realm of computer networks, nodes share resources through digital telecommunications networks. These networks enable lightning-fast data exchange and boast attributes like speed, accuracy, diligence, versatility, and vast storage capabilities. Additionally, various types of networks exist tod

9 views • 12 slides

Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) are powerful tools for sequential data learning, mimicking the persistent nature of human thoughts. These neural networks can be applied to various real-life applications such as time-series data prediction, text sequence processing,

15 views • 34 slides

The convergence of wireless and wireline networks in the context of 5G brings about significant changes and improvements. This evolution involves the integration of 5G core networks, new access network functions, enhanced interfaces, and the introduction of new devices like 5G residential gateways.

7 views • 25 slides

Delve into the realm of mechanistic interpretability in neural networks, exploring how models can learn human-comprehensible algorithms and the importance of deciphering internal features and circuits to predict and align model behavior. Discover the goal of reverse-engineering neural networks akin

4 views • 31 slides

Graph Neural Networks (GNNs) are a versatile form of neural networks that encompass various network architectures like NNs, CNNs, and RNNs, as well as unsupervised learning models such as RBM and DBNs. They find applications in diverse fields such as object detection, machine translation, and drug d

2 views • 48 slides

Understanding the dynamics of opinion formation and control in social networks is a critical area of research. This study, supervised by Susana Gomes and Marie-Therese Wolfram, explores the manipulation of collective behavior through various models including ODE, agent-based, and stochastic analysis

0 views • 29 slides

As the number of IoT devices grows rapidly, the need for securing these devices from cyber threats like DoS attacks becomes crucial. This study aims to evaluate the effectiveness of machine learning algorithms such as Gaussian Naive Bayes, K-Nearest Neighbors, Support Vector Machine, and Neural Netw

1 views • 13 slides

Communication over distance has always been crucial for civilization, with electronic means playing an increasingly vital role. Telecom services are essential for businesses, social interactions, and entertainment, with public operators like Ethio Telecom providing services through telecom networks.

1 views • 22 slides

Graph-based data science offers a powerful approach to analyzing data by leveraging graph structures. This involves using graph representation, analysis algorithms, ML/AI techniques, kernels, embeddings, and neural networks. Real-world examples show the utility of data graphs in various domains like

3 views • 37 slides

Learn how to build artificial neural networks from scratch, focusing on multi-level feedforward networks like multi-level perceptrons. Discover how neural networks function, including training large networks in parallel and distributed systems, and grasp concepts such as learning non-linear function

1 views • 33 slides

Artificial Neural Networks aim to mimic brain processing. Back-propagation is a key method to train these networks, optimizing weights to minimize loss. Multi-layer networks enable learning complex patterns by creating internal representations. Historical background traces the development from early

1 views • 24 slides

The Samsung SmartThings hub is a versatile device connecting Zigbee and Zwave networks, offering secure access to SkySpark via HTTPS. Zigbee and Zwave networks operate on distinct frequencies, enabling efficient communication without interference with WiFi. These networks support various devices for

0 views • 19 slides

Wireless Wide Area Networks (WWAN) utilize cellular network technology like GSM to facilitate seamless communication for mobile users by creating cells in a geographic service area. Cellular networks are structured with backbone networks, base stations, and mobile stations, allowing for growth and c

2 views • 17 slides

Interconnection networks are essential in multiprocessor systems, linking processing elements, memory modules, and I/O units. They enable data exchange between processors and memory units, determining system performance. Fully connected interconnection networks offer high reliability but require ext

1 views • 19 slides

Computer networks are vital for sharing resources, exchanging files, and enabling electronic communications. This content explores the basics of computer networks, the components involved, advantages like file sharing and resource sharing, and different network computing models such as centralized a

1 views • 96 slides

This course focuses on computer communication networks at Anjuman College of Engineering and Technology in Tirupati, covering topics such as basic concepts, network layers, IP addressing, hardware aspects, LAN standards, security, and administration. Students will learn about theoretical and practic

0 views • 72 slides

This presentation provides an introduction to neural networks in IBM SPSS Modeler 14.2. It covers the concepts of directed data mining using neural networks, the structure of neural networks, terms associated with neural networks, and the process of inputs and outputs in neural network models. The d

0 views • 18 slides

Global and regional knowledge networks play a vital role in agriculture by facilitating information sharing, collaboration, capacity building, and coordination among stakeholders. These networks improve access to information, foster collaboration, enhance capacity building, and strengthen coordinati

0 views • 5 slides

Router routing tables are crucial for directing packets to their destination networks. These tables contain information on directly connected and remote networks, as well as default routes. Routers use this information to determine the best path for packet forwarding based on network/next hop associ

0 views • 48 slides

Analyzing the concept of structural similarity within Information Networks (INs), the study introduces P-Rank as a more advanced alternative to SimRank. By addressing the limitations of SimRank and offering a more efficient computational approach, P-Rank aims to provide a comprehensive measure of si

0 views • 17 slides

This content explores the concept of advanced classifiers like Neural Networks which compose complex relationships through combining perceptrons. It delves into the workings of the classic perceptron and how modern neural networks use more complex decision functions. The visuals provided offer a cle

0 views • 26 slides

Relational Bayesian networks play a crucial role in predicting ground facts and frequencies in complex relational data. Through first-order and ground probabilities, these networks provide insights into individual cases and categories. Learning Bayesian networks for such data involves exploring diff

0 views • 46 slides

Overlay networks are logical networks built on top of lower-layer networks, allowing for efficient data lookup and reliable communication. They come in unstructured and structured forms, with examples like Gnutella and BitTorrent. Distributed Hash Tables (DHTs) are used in real-world applications li

0 views • 45 slides

Networks define the structure of interactions between agents, portraying relationships as ties or links. Various examples such as the 9/11 terrorists network, international trade network, biological networks, and historical marriage alliances in Florence illustrate the power dynamics within differen

0 views • 46 slides

Explore the concepts of graph theory and management science, focusing on networks, spanning trees, and their practical applications. Learn about the difference between a snowplow tracing streets, a traveler visiting cities, and connecting towns with cables. Discover how networks like Facebook evolve

0 views • 15 slides

Explore the construction and comparisons of various parallel prefix networks in divide-and-conquer algorithms, such as Ladner-Fischer, Brent-Kung, and Kogge-Stone. These networks optimize computation efficiency through parallel processing, showcasing different levels of latency, cell complexity, and

1 views • 21 slides

This research focuses on mining diverse social entities from linked data in social networks using a DF-tree structure and DF-growth mining algorithm. The study explores the extraction of important linked data in social networks and the mining of various social entities such as friends. Prominence va

0 views • 13 slides

Bayesian networks, also known as Bayes nets, provide a powerful tool for modeling uncertainty in complex domains by representing conditional independence relationships among variables. This outline covers the semantics, construction, and application of Bayesian networks, illustrating how they offer

0 views • 17 slides

In the realm of computer vision, the integration of machine learning and artificial neural networks has enabled significant advancements in face verification tasks. Leveraging the brain's inherent pattern recognition capabilities, AI systems can analyze vast amounts of data to enhance face detection

0 views • 13 slides

Explore the differences between Whole Networks and Ego Networks in social network analysis. Whole Networks provide comprehensive information about all nodes and links, enabling the computation of network-level statistics. On the other hand, Ego Networks focus on a sample of nodes, limiting the abili

0 views • 31 slides

Embrace the future of networking by transitioning to Software-Defined Networks (SDN), overcoming drawbacks of current paradigms. Explore SDN's motivation, OpenFlow API, challenges, and use-cases. Compare the complexities of today's distributed, error-prone networks with the simplicity and efficiency

0 views • 36 slides

Addressing the underrepresentation of women and people of color in STEM, this study explores the impact of peer networks on the persistence of underrepresented high school students of color in STEM at the postsecondary level. It delves into how race and gender intersect to influence the creation and

0 views • 16 slides

This study explores innovative methods in training complex-valued neural networks, including a model of complex-valued neurons, network architecture, error analysis, Adam optimizer, gradient calculation, and activation function selection. Simulation results compare real-valued and complex-valued net

0 views • 12 slides

Bayesian networks, also known as graphical models, provide a compact and efficient way to represent complex joint probability distributions involving hidden variables. By depicting conditional independence relationships between random variables in a graph, Bayesian networks facilitate Bayesian infer

0 views • 33 slides