Jianxi Gao’s Post

I am delighted to announce that our latest publication, "Forecasting Stock Trends with Feedforward Neural Networks," presented at #FedCSIS 2024—the 19th Conference on Computer Science and Intelligence Systems—has received a Special Award in the #FedCSIS 2024 Data Science Challenge. Stock market prediction is a complex yet critical task that significantly contributes to the stability and efficiency of financial markets by providing essential insights into market trends and movements. In this work, we propose a straightforward yet powerful model utilizing feedforward neural networks to address this challenge. Our approach draws on recent advances in machine learning and deep learning to analyze and process large financial datasets, achieving promising results in forecasting stock trends. Direct link to the paper: https://lnkd.in/dJCDzTV3 #FedCSIS #stockmarketprediction #artificialintelligence #computerscience #neuralnetworks #deeplearning #forecasting #classification #researchpaper #awards

Curious about neural networks? Our FREE course notes are your guide to the basics of this pivotal machine learning tool. We start with core concepts, simplifying neural networks for anyone keen to understand and apply them. Our Course Notes are designed for budding data scientists and engineers who want to incorporate machine learning into their skills. They cover everything from algorithm building blocks to the nuances of regression and classification. Get a solid grasp on key machine learning concepts that will serve as the foundation for more complex models. FREE Download Link 👉 https://bit.ly/3WkOEKO #machinelearning #neuralnetworks #datascienceeducation #mlalogorithms #learndatascience

Hey Connections !!! 🖥️ 🚀 Just attended an insightful workshop on machine learning organised by MLSA LNCTU ! 💡 Learned about cutting-edge techniques and practical applications in the field. Excited to implement these learnings in my work and contribute to pushing the boundaries of technology. Major Takeaways were - 1) Introduction to Machine Learning and Computer Vision 2) Building Convolutional Neural Networks (CNNs) for Image Classification 3) Training and Evaluating Fast Food Classification Models 4) Fine-tuning Models and Hyperparameter Optimization 5) Deploying Models for Inference Special thanks to Rishav kumar & Utkarsh Saxena for this amazing session. #mlsa #machinelearning #ml

🎓 Delighted to announce the completion of an intensive Convolutional Neural Networks certificate program! 🚀 Delved deep into cutting-edge CNN architectures, mastering advanced feature extraction and pattern recognition techniques pivotal in crafting precise predictive models. From real-world projects to theoretical foundations, I've honed my skills to drive data-driven solutions with confidence. Ready to leverage this expertise to tackle complex challenges and innovate in the realm of #DeepLearning, #MachineLearning, #DataScience, and #ComputerVision! 💡🌟 #ContinuousLearning

Proud to share that I’ve completed an intensive Artificial Intelligence course, equipping me with the latest skills and knowledge in machine learning, neural networks, and data science. This journey has been a blend of challenging concepts, practical projects, and innovative problem-solving. Ready to leverage this expertise to drive technological advancements and create intelligent solutions. #AIMastery #MachineLearning #DataScience #CareerGrowth #ContinuousLearning #AItoolsmasteryprogram #promptengineering

Excited to share my completion of the Machine Learning Specialization Course from DeepLearning.AI. During this course, I explored a wide range of modern machine learning concepts, from supervised learning techniques like linear regression and neural networks to unsupervised methods such as clustering and anomaly detection. Additionally, I gained insights into recommender systems and reinforcement learning.

Proud to share that I’ve completed an intensive Artificial Intelligence course, equipping me with the latest skills and knowledge in machine learning, neural networks, and data science. This journey has been a blend of challenging concepts, practical projects, and innovative problem-solving. Ready to leverage this expertise to drive technological advancements and create intelligent solutions. #AIMastery #MachineLearning #DataScience #CareerGrowth #ContinuousLearning #AItoolsmasteryprogram #promptengineering

🌟This week, I completed the class COMP 381 - Machine Learning 🤖, culminating in a project focused on implementing Computer Vision 👁️🗨️. My team and I constructed 3 Machine Learning and Deep Learning models to identify facial emotions from videos 🎬 and images 🖼️. Our target was to compare the efficiency of classification models on the same task, including Convolutional Neural Network (CNN), Decision Tree, and K-nearest neighbors (KNN). Utilizing the transfer learning approach 💡, we successfully created the emotion recognition system based on the pre-trained face detection models (Haarcascades and MTCNN). The video below is a demo of the working CNN model on recognizing emotion through live videos I developed and trained on the FER 2013 dataset from Kaggle. If interested, you can find the project here: 👉 https://lnkd.in/gTSfz7Q8 #machinelearning #deeplearning #computervision #datascience #transferlearning #cnn #knn #decisiontree

Built on top of PyTorch, Bayesian-Torch is the first open-source framework to support low-precision quantization of Bayesian deep neural network models for efficient inference. With Bayesian-Torch, developers can seamlessly convert a deep learning neural network model of any architecture to be uncertainty-aware using simple APIs. Learn more about improving the trustworthiness and robustness of deep learning models with Bayesian-Torch here. https://lnkd.in/g3C_7mbT     #Developer #DeepLearning #PyTorch