دانلود کتاب بینش های جدید در یادگیری ماشین و شبکه های عصبی عمیق

Several decades ago, linear regression models and rule-based algorithms were the primary tools in the arsenal of researchers and technologists for deciphering patterns in data and predicting outcomes. These methodologies, rooted in classical statistics and rudimentary algorithms, were effective for tackling straightforward predictive challenges but lacked the sophistication to understand complex relationships, high-dimensional data, and non-linear interactions. However, with the advent of machine learning in the late 20th century, and the subsequent rise of deep neural networks in the recent decade, the landscape dramatically shifted. These contemporary techniques, harnessing the power of advanced algorithms and vast computational resources, offer a more nuanced approach to data analysis. They now allow researchers and practitioners to delve into complex, high-dimensional spaces, making sense of data in ways that were previously thought inconceivable. As a result, they are capable of addressing challenges steeped in ambiguity and subtlety, effectively bridging the gap between raw data and human-like interpretative understanding. In the evolving realm of machine learning and deep neural networks, research papers often converge around thematic clusters, reflecting the dominant trends and pressing challenges of the time. The following groups present a curated categorization of recent manuscripts, shedding light on the multifaceted dimensions of contemporary research in this domain. Generative Techniques, Synthetic Data, and Advanced Feature Extraction: Delving into the complexities of synthetic data generation, GANs, and advanced feature extraction, this cluster presents manuscripts that push the envelope of data processing and object recognition. Figueira and Vaz provide a comprehensive survey on GANs, highlighting their applications and training challenges, with an emphasis on tabular data. Lee and Yoo introduce an emotion recognition framework that leverages independent component analysis and latent codes to combat performance degradation. Nazari, Branco, and Jourdan take GAN innovations a step further with AutoGAN, which automates the GAN training process across different data modalities. Meanwhile, Kamran, Rehman, and their colleagues delve into object segmentation, proposing a sophisticated framework that harnesses parallelism in feature extraction, refining global feature maps to enhance the recognition of camouflaged objects. Medical Innovations through Deep Learning: Medical advancements through the lens of deep learning are the core focus of this cluster. Pinheiro, Silva, and their team introduce a semi-supervised approach that predicts EGFR mutations in lung cancer patients using 3D CT scans, merging variational autoencoders and adversarial training to improve classification. Xie and Lin pivot to cardiac health, presenting an optimized CNN for ectopic beat classification, with their correlation-based oversampling method ensuring a balanced dataset for improved performance. Optimization and Analysis in Dynamic Environments: These manuscripts tackle the challenges of real-world, dynamic environments through advanced machine learning techniques. Silva and Pedroso craft a deep reinforcement learning algorithm suited for the particulars of crowd shipping’s last-mile delivery, demonstrating efficacy in large uncertain data instances. Balakrishnan and his team analyze the digital sprawl of Twitter, dissecting various ensemble learning models to detect disaster-related communications, highlighting the effectiveness of simpler transformer variants. Aziz’s team navigates the waters of sociolinguistics, using cutting-edge vectorization techniques to predict political hate speech in Roman Urdu.