Ethical Spectacle Research’s Post

6th part is out, watch now! https://lnkd.in/gX7_inmB Do you want to learn Generative AI? then this free course will teach the practical implementation and hands-on coding, how to build a generative AI. In this video, I will introduce you to the theory Variational Encoders and its architecture, and how to generate images with the help of VAEs. If you have any doubts, or discussions or want to collaborate on a project, write in the comments or DM me...

🚀 𝗝𝗼𝗶𝗻 𝘂𝘀 𝗳𝗼𝗿 Omdena 𝗟𝗶𝘃𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 𝗦𝗲𝘀𝘀𝗶𝗼𝗻: 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗲 𝗟𝗹𝗮𝗺𝗮 𝟯.𝟮 🚀 🗓 𝗗𝗮𝘁𝗲: 𝗢𝗰𝘁𝗼𝗯𝗲𝗿 𝟭𝟵, 𝟮𝟬𝟮𝟰 ⏰ 𝗧𝗶𝗺𝗲: 𝟭𝟰:𝟬𝟬 𝗨𝗧𝗖 Get ready for an exciting Omdena 𝗟𝗶𝘃𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 𝗦𝗲𝘀𝘀𝗶𝗼𝗻! We'll dive into the world of LLaMA models and explore how to fine-tune the powerful 𝗟𝗹𝗮𝗺𝗮 𝟯.𝟮 from Meta for various applications. 🌟 Perfect for developers, data scientists, and AI enthusiasts eager to advance their knowledge and coding skills while working with state-of-the-art AI. 𝗬𝗼𝘂'𝗹𝗹 𝗹𝗲𝗮𝗿𝗻 𝘁𝗼: 🌟Fine-tune LLaMA models for specific tasks and industries 🌟Understand the capabilities of LLaMA 3.2 and its performance improvements 🌟Apply advanced techniques for training and optimizing large language models 🌟Use LLaMA 3.2 with Groq API & Streamlit 👉 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗵𝗲𝗿𝗲: https://lu.ma/96b7zzw2 Don’t miss out on this hands-on opportunity to learn and enhance your AI skillset! Let's push the boundaries of AI innovation together! 💡 #Omdena #AI #ArtificialIntelligence #MachineLearning #LiveCoding #LLaMA #TechInnovation #AIForGood #CodingCommunity #DataScience #ModelFineTuning #AICommunity

Finished the course “AI Workshop: Hands-on with GANs with Deep Convolutional Networks” by Janani Ravi! Check it out: https://lnkd.in/gpB4TAtg #convolutionalneuralnetworks #generativeadversarialnetworks.

Excited to attend the "Demystifying AI for Software Engineering" Tech Lab at PLG Disrupt Summit! 🚀 This workshop dives deep into machine learning and deep learning, blending theory with practical applications. It covers data preprocessing, feature selection, model building, and algorithm optimization, with hands-on activities and interactive lectures. Looking forward to learn about building ML models and staying ahead in AI and ML advancements. #AI #MachineLearning #DeepLearning #SoftwareEngineering #TechLab #Innovation #PLGDisruptSummit

I've been thinking a lot about how generative AI could reduce the learning curve for getting started with data analysis. This summer I got a chance to try out these ideas in a new set of Coursera courses. These courses cover the types of topics that I usually discuss in an intro data analysis class, but instead of having students learn a statistical programming language, the course uses generative AI to analyze a dataset and help interpret the results. The first course covers the basics of understanding how to structure research questions and the types of data we're likely to see: https://lnkd.in/d4i4GBCy The second course covers making comparisons using data, with a focus on conditional means: https://lnkd.in/dqqn6Zzg The last course gets the student started using regression for the purposes of prediction: https://lnkd.in/dSA3Zqqh So much of the difficulty in learning applied stats is just getting the program to do what you want-- with generative AI the student can just focus on the key patterns in the data they're interested in.

Hello #Everyone, Delighted to present the Speech Emotion Recognition (SER) project, undertaken at #ZidioDevelopment 🚀,aimed to develop a system capable of accurately identifying and classifying human emotions based on speech signals. By analyzing vocal expressions, the system seeks to recognize emotions such as happiness, sadness, anger, surprise, and neutrality with applications in human-computer interaction, customer service, and mental health monitoring. 1. Data Preparation: - Data Collection: Downloaded a comprehensive dataset from Kaggle, containing a mix of emotional speech data. - Visualization: Took a deep dive into the data, visualizing each emotion to get a better understanding. - Feature Extraction: Leveraged the librosa library to extract key audio features, including MFCC, mel-spectrogram, Zero-Crossing Rate (ZCR), contrast, and chroma. These features are crucial as they capture the nuances in speech that reflect different emotions. - Preprocessing: Applied label encoding to categorize emotions, followed by a train-test split and feature scaling to prepare the data for modeling. 2. Model Building: - Initial Attempts with ANN: Started with simple and complex Artificial Neural Networks (ANNs) with multiple hidden layers. Unfortunately, these models tended to underfit the data, highlighting the challenges of using AN for sequential data like speech. - Switching to CNN-LSTM: Realizing the need for a more sophisticated approach, I designed a CNN-LSTM model. 3. Training & Results: - On the Torento Emotion Speech Set (TESS): The CNN-LSTM model achieved a remarkable 98.99% training accuracy and 98% validation accuracy. - On Combined Datasets: After combining the TESS, RAVDESS, and SAVEE datasets and addressing class imbalance by downscaling the neutral class, the model achieved 87% training accuracy and 78% validation accuracy. This project was a deep dive into the world of speech emotion recognition and gave me hands-on experience with advanced deep learning techniques. I learned a lot about the importance of feature extraction, model selection, and the intricacies of working with audio data. #DataScience #MachineLearning #SpeechEmotionRecognition #DeepLearning #Python #ZidioDevelopment

Just finished AI Workshop: Hands-on with GANs with Deep Convolutional Networks! Check it out: https://lnkd.in/dYRnaCcQ #convolutionalneuralnetworks #generativeadversarialnetworks

𝗝𝗼𝗶𝗻 𝘂𝘀 𝗳𝗼𝗿 Omdena 𝗟𝗶𝘃𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 𝗦𝗲𝘀𝘀𝗶𝗼𝗻: 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗲 𝗟𝗹𝗮𝗺𝗮 𝟯.𝟮 🚀 🗓 𝗗𝗮𝘁𝗲: 𝗢𝗰𝘁𝗼𝗯𝗲𝗿 𝟭𝟵, 𝟮𝟬𝟮𝟰 ⏰ 𝗧𝗶𝗺𝗲: 𝟭𝟰:𝟬𝟬 𝗨𝗧𝗖 Get ready for an exciting Omdena 𝗟𝗶𝘃𝗲 𝗖𝗼𝗱𝗶𝗻𝗴 𝗦𝗲𝘀𝘀𝗶𝗼𝗻! We'll dive into the world of LLaMA models and explore how to fine-tune the powerful 𝗟𝗹𝗮𝗺𝗮 𝟯.𝟮 from Meta for various applications. This is perfect for developers, data scientists, and AI enthusiasts eager to advance their knowledge and coding skills while working with state-of-the-art AI. 𝗬𝗼𝘂'𝗹𝗹 𝗹𝗲𝗮𝗿𝗻 𝘁𝗼: - Fine-tune LLaMA models for specific tasks and industries - Understand the capabilities of LLaMA 3.2 and its performance improvements - Apply advanced techniques for training and optimizing large language models - Use LLaMA 3.2 with Groq API & Streamlit 👉 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗵𝗲𝗿𝗲: https://lu.ma/96b7zzw2 Don’t miss out on this hands-on opportunity to learn and enhance your AI skillset! Let's push the boundaries of AI innovation together! #Omdena #AI #ArtificialIntelligence #MachineLearning #LiveCoding #LLaMA #TechInnovation #AIForGood #CodingCommunity #DataScience #ModelFineTuning #AICommunity

Kudos to Sakana AI. Just read this "AI Scientist" that's out here writing research papers like it's no big deal. Get this - it's brainstorming ideas, coding experiments, AND writing up the results. All for less than the cost of my coffee order! The kicker? Some of its papers are apparently good enough for top ML conferences. Highly recommended reading through some of the generated diffusion papers to get a sense of its strengths and weaknesses. Paper : https://lnkd.in/dzyBGd4j GitHub : https://lnkd.in/dBsgs5Ug

Just finished the course “AI Workshop: Hands-on with GANs with Deep Convolutional Networks” by Janani Ravi! Check it out: https://lnkd.in/eRiwGdJE #convolutionalneuralnetworks #generativeadversarialnetworks.