SilicoGenesis’ Post

🚀 Excited to Share My Latest Project: Building a Perceptron from Scratch! This is my second project using numpy, and I'm thrilled about how much I've learned. While researching neural networks, I was fascinated to discover that I could implement them using numpy itself! 🔍 Key Highlights: - Utilized numpy for efficient numerical computations. - Trained the perceptron on a simple dataset to predict values based on linear relationships. - Implemented weight and bias updates to minimize prediction errors over multiple epochs. - Tested the model with unknown data to validate its performance. Even though the mathematics was challenging to grasp, I'm proud to say that I completed this project. It has deepened my understanding of how neural networks learn and adapt. I look forward to exploring more complex models and datasets in the future! Do check it out on my Github profile... And Ofcc I am open to any suggestions! #MachineLearning #Perceptron #NeuralNetworks #DataScience #Python #Numpy

Sergio Pablo-García introduces our latest featured #DigitalDiscovery article: We are pleased to announce the publication of an article on rNets, a Python package designed for visualizing reaction networks, in Digital Discovery. rNets enhances the representation of complex neural networks by combining graphs with visual encoding, effectively condensing thermodynamic and kinetic information obtained from computational simulations. This tool aims to improve the analysis and understanding of reaction networks, providing a valuable resource for researchers and practitioners.   Key features of rNets include:   - Integration of graph structures with visual encoding for comprehensive network representationCondensation of crucial thermodynamic and kinetic data - Fully documented code available on GitHub - Three detailed use-case examples discussed in the article   The rNets package is fully documented and freely accessible in the GitHub repository, offering researchers a powerful tool for their computational chemistry needs.   We invite you to read the full article and explore the capabilities of rNets: https://lnkd.in/enADAKEG

Medical Image Segmentation using U-Net | Deep Learning Projects | Codersarts 🚗🤖 This course focuses on Medical Image Segmentation using U-Net. Throughout the course, participants will learn: Gain expertise in medical image segmentation, Python programming, and U-Net architecture while mastering lung tumor segmentation and model evaluation techniques, vital for healthcare applications. For further details on prerequisites, covered topics, and utilized libraries, please refer to the provided PDF file. Should you have any inquiries, feel free to reach out to us at [email protected] #MedicalImageSegmentation #UNet #DeepLearning #PythonProgramming #HealthcareTech #DataPreprocessing #LungTumorSegmentation #ConvolutionalNeuralNetworks #AIProjects #CodersartsTraining #codersarts

Day 15/50 update on my 50 DAYS CODING CHALLENGE ✌️😄 My project on Potatoes Disease Classification is going smoothly. It's a lengthy project. Today I trained a convolutional neural network in tensorflow using potato plant images. The goal of this model is to classify these images as either healthy or early blight or late blight. In a nutshell, today I learnt to:- 👉Build and train a CNN model 👉Plot training history on graph Looking forward to day 16/50 with full enthusiasm 🙌 #codingchallenge #codechallengeaccepted #codechallenge #challengeaccepted #coding

🌟 Empowering Biology with Programming Tools: I recently attended an insightful workshop on Python for Data Science and Machine Learning, organized by Decode Life. A heartfelt thank you to the organizers and mentors for creating such a valuable learning experience! The workshop provided hands-on training in applying computational tools to biological data analysis. Some of the key highlights include: 👉 Leveraging Pandas and Bio-Python for efficient handling and analysis of biological datasets 👉 Performing Differential Expression Analysis and visualizing genomic data 👉 Building machine learning models, including Convolutional Neural Networks (CNNs), for protein stability predictions This experience has deepened my understanding of how programming can be integrated with biology to enable advanced analysis in areas like genomics and synthetic biology. The skills I’ve gained will be instrumental in my research journey, especially as I work toward tackling global challenges in microbiology and biotechnology. Thank you, Decode Life, for this incredible opportunity! #Python #Bioinformatics #MachineLearning #Genomics #ComputationalBiology

🧠 Excited to share my latest project: a Brain Tumor Detector built using Python! 🌟 I developed this detector to leverage the power of machine learning and image processing in medical diagnostics. Using Python along with libraries like TensorFlow and OpenCV, I trained the model to accurately identify potential brain tumors in medical imaging data. 🔍 Key Features: Image Processing: Leveraging OpenCV for preprocessing medical images. Machine Learning: Utilizing TensorFlow for training a deep learning model. Accuracy and Efficiency: Achieving 78% accuracy in detecting brain tumors. 🌐 This project aims to contribute to healthcare by offering a tool that assists medical professionals in early diagnosis, potentially improving patient outcomes. 📈 Learning Journey: Throughout this project, I deepened my understanding of machine learning techniques and honed my Python coding skills. 🤝 I'm eager to hear your thoughts and feedback on this project! Let's connect and discuss how technology can continue to make a positive impact in healthcare. #Python #MachineLearning #HealthTech #DataScience #MedicalImaging #DeepLearning

Day 4 of ML for Drug Discovery. I am impressed at how this field's state-of-the-art tech can be done with open source. Couple that with the billions going into pharma yearly - food for thought. It is encouraging that, with effort, anyone can learn this (even if not able to do it at scale). BTW - Python is the way to go :-) #ai #airesearch #ai4science

Title: Completed Plant Disease Detection Project "I've just completed a Plant Disease Detection Project using Machine Learning! The model can identify plant diseases from images using a Convolutional Neural Network (CNN). This project is aimed at helping farmers and gardeners detect plant diseases early. Check it out and let me know your thoughts!" https://lnkd.in/gRAcM9pE #MachineLearning #AI #PlantDiseaseDetection #Python #DeepLearning #GitHub #DataScience

I am happy to announce the completion of my recent project aimed at detecting brain tumors from MRI images using Convolutional Neural Networks (CNN). This project was developed using Python, TensorFlow, and Streamlit, showcasing a practical application of AI in the biomedical field. Check out the GitHub repository for detailed code: https://lnkd.in/dBziyn6P Here’s a quick demo of the app in action! #MachineLearning #DeepLearning #ComputerVision #BiomedicalImaging #AI #Python #Streamlit

Do you want to apply deep learning-based methods to your own data and image analysis problems? Then #EMBLDeepLearning is for you! 🔹 Learning outcomes - Understand the fundamentals of applying deep learning to image analysis in microscopy - Learn where the state-of-the-art of the field stands for the most important image analysis problems - Advise users in strategies to obtain ground truth - Select and train a neural network on a bioimage analysis task - Devise a validation strategy for the results 📣 Apply by 25 November 2024 📣 🗓️ 17 – 21 February 2025 📍 EMBL Heidelberg ➡️ https://lnkd.in/eWY8GY-f Prerequisites for this workshop are programming experience in Python, including solid knowledge of operations with images, and first experience of applying deep learning algorithms. #2Dsegmentation #3Dsegmentation #tracking #generativemodels