Data Analytics: 7 Manuscripts – Data Analytics for Beginners, Deep Learning with Keras, Analyzing Data with Power BI, Reinforcement Learning with Python, Artificial Intelligence Python, Text Analytics with Python, Convolutional Neural Networks in Python
Book 1: Data Analytics For Beginners In this book you will learn: What is Data Analytics Types of Data Analytics Evolution of Data Analytics Big Data Defined Data Mining Data Visualization Cluster Analysis And of course much more!
Big Data: 4 Manuscripts – Data Analytics for Beginners, Deep Learning with Keras, Analyzing Data with Power BI, and Convolutional Neural Networks in Python
Data Analytics for Beginners In this book you will learn: Putting Data Analytics to Work The Rise of Data Analytics Big Data Defined Cluster Analysis Applications of Cluster Analysis Commonly Graphed Information Data Visualization Four ...
Deep Learning with Python: The Ultimate Guide to Understand Deep Neural Networks with Python Through PyTorch, TensorFlow and Keras. Discover the Ethical Implications of Deep Learning in the New World
This book offers you the basic knowledge about Python and Deep Learning Neural Networks that you will need to lay the foundation for future studies. This book will start you on the road to mastering the art of deep learning neural networks.
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I
... hidden neurons that get activated are shown. (Color figure online) 4.3 Hidden State Analysis and DFA Extraction In this section we use a 2-dimensional Isomap projection to analyze the activation space in the hidden layer. We have ...
Practical Convolutional Neural Networks: Implement advanced deep learning models using Python
This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector.
Keras Reinforcement Learning Projects: 9 projects exploring popular reinforcement learning techniques to build self-learning agents
9 projects exploring popular reinforcement learning techniques to build self-learning agents Giuseppe Ciaburro ... Cartesian coordinate plane (x; y). What interests us is to know the probability that this point is inside the graph ...
Machine Learning with Python: A Step by Step Guide for Absolute Beginners to Program Artificial Intelligence with Python. Neural Networks and Data Science from Pre-Processing to Deep Learning
Have you been hearing about some of the buzzwords in the world of business like data science, data analysis, and machine learning, but worry that this is going to be too hard for you to catch onto and learn more about?
Convolutional neural networks and deep learning for crop improvement and production
... image which is needed to include important plant features and (2) the image ... label image classifier. Fastai is built on PyTorch and provides a high-level ... herbicide sprayed on non-weed areas, whereas increasing recall will ensure a ...
Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part IV
... summarization model that decouples the syntactic analysis process from the ... sentence. Although this approach can theoretically handle both extractive and abstractive ... Fast abstractive summarization with reinforce-selected sentence ...
Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models
This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical ...
Deep Learning and Artificial Intelligence: A Beginners' Guide to Neural Networks and Deep Learning
Welcome to this book on Deep Learning and Neural Networks.
Python Machine Learning: A Practical Beginner's Guide to Understanding Machine Learning, Deep Learning and Neural Networks with Python, Scikit-Learn, Tensorflow and Keras
... learning to predict the future profits or losses that the company may face. They can also use it to see when they should stock a specific product or when they should release a new product. Let us look at how a retailer can improve his ...
The Artificial Intelligence in a Nutshell: A brief introduction to Artificial Intelligence, Machine Learning, Neural Networks, Deep Learning, and Robots
... words to use: MaxWords = 10000 Let's create the Tokenizer: Tok = Tokenizer(num_words=MaxWords) Finally, we can perform the tokenization operation: XTrain = Tok.sequences_to_matrix(XTrain, mode='binary') XTest = Tok.sequences_to_matrix ...
Learn Data Analytics for Beginners to Core Advance: Data Analyst, Deep Learning, Data Science, Neural Network, Python Data Analytics
This book is about the basics of R programming. you'll start with the fundamentals of the language, find out how to control datasets, the way to write functions, and the way to debug and optimize code.
Hands-On Convolutional Neural Networks with TensorFlow: Solve computer vision problems with modeling in TensorFlow and Python
This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time!
Artificial Neural Networks and Machine Learning – ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I
... OMG emotion dataset F-score Arousal CCC Valence CCC Barros et al. [3] 0.39 0.07 0.04 OMG emotion challenge - 0.29 0.36 This work 0.73 0.17 0.16 Table 3 presents the results from the executions with the EmoDB dataset and the comparison ...
Artificial Neural Networks and Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26–29, 2023, Proceedings, Part II
... F1-score heavily. Near 1 balanced accuracy score for both experiment setups ... 2011-0117. References 1. Ahangaryan, F.P., Mohammadpour, T., Kianisarkaleh ... (2011) 5. Brodersen, K.H., Ong, C.S., Stephan, K.E., Buhmann, J.M.: The balanced accu ...
Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part III
... dropout layers only in the two layers just before the YOLOv3 detection ... uncertainty representing variance when detecting multiple times using MC ... Bayesian convolutional neural networks with Bernoulli approximate variational ...
Artificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part V
... abstractive summarization. In: Proceedings of the 58th Annual Meeting of the ... Fast abstractive summarization with reinforce-selected sentence rewriting ... abstractive summarization using inconsistency loss. In: ACL 2018: 56th Annual Meeting ...
Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part I
... Introduction to Algorithms, 3rd edn. The MIT Press, Cambridge (2009) Daniel ... Newman, M.E.J.: Community structure in social and biological networks. In ... Networks: An Introduction. Oxford University Press, New York (2010) Newman ...
Artificial Neural Networks and Machine Learning – ICANN 2023: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26–29, 2023, Proceedings, Part VIII
... OMG utilizing boxplots with blue, green, and pink hues correspondingly. Within these boxplots, the mean and median ... OMG = 90.2 ± 4.4%; When SNR = 0.6, for Data B#, ACC = 91.4±3.2%, DSC = 92.4±2.7%, OMG = 90.6 ± 3.7%; When SNR = 0.6 ...
Artificial Neural Networks and Machine Learning – ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III
... Beatles - Ob-La-Di, Ob-La-Da.3 Most generated samples are similar to the illustrated one from The Beatles - Ob-La-Di, Ob-La-Da, where the generated notes are in the same key of the original composition, including the timings of chord ...
Artificial Neural Networks and Machine Learning – ICANN 2020: 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part II
... Dropout on an FPGA using edge computing. References 1. Feng, D., Rosenbaum, L., Dietmayer, K.: Towards safe autonomous driving: capture uncertainty in the deep neural network for Lidar 3D vehicle detection. In: 2018 21st International ...
Artificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I
... Training a neural network from scratch requires learning weights and biases for all functions f (1),..., f (L). However ... Caltech 101 and Caltech 256 - reduced image classification. The reduced version of the Caltech 256 dataset was ...
Artificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part IV
... HGR applications since the parameter estimation in non-stationary processes is difficult. Therefore, machine learning (ML) and deep learning (DL) techniques are typically used to classify EMG signals [4,7]. In particular, supervised ...
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