recurrent neural networks (rnn) - explained #maths #dataanlysis #datascience #machinelearning

📚 *RECOMMENDED BOOKS TO START WITH MACHINE LEARNING* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ If you're new to ML, here are the 3 best books I recommend (I've personally read all of these): 1. Hands-On Machine Learning – the go-to practical ML guide: https://amzn.to/3UcGqSS 2. Mathematics for Machine Learning – deep dive into the theoretical aspects of ML: https://amzn.to/3IZgHe7 3. Designing Machine Learning Systems - practical strategies for building scalable ML solutions: https://amzn.to/4ojEqFX These are affiliate links, so buying through them helps support the channel at no extra cost to you — thanks 🙏 *Summary* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ This video explains Recurrent Neural Networks (RNNs), a fundamental deep learning architecture designed for sequence data such as time series, text, speech, and natural language processing. Learn how RNNs work, how hidden states and backpropagation through time (BPTT) function, why vanishing gradients occur, and how RNNs are applied in NLP, forecasting, and AI systems. A clear and practical introduction to recurrent neural networks for machine learning and deep learning enthusiasts. *Follow Me* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ 🐦 Twitter: @datamlistic https://twitter.com/datamlistic 📸 Instagram: @datamlistic https://www.instagram.com/datamlistic 📱 TikTok: @datamlistic https://www.tiktok.com/@datamlistic 👔 Linkedin: https://www.linkedin.com/company/datamlistic *Channel Support* ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ The best way to support the channel is to share the content. ;) If you'd like to also support the channel financially, donating the price of a coffee is always warmly welcomed! (completely optional and voluntary) ► Patreon: https://www.patreon.com/datamlistic ► Bitcoin (BTC): 3C6Pkzyb5CjAUYrJxmpCaaNPVRgRVxxyTq ► Ethereum (ETH): 0x9Ac4eB94386C3e02b96599C05B7a8C71773c9281 ► Cardano (ADA): addr1v95rfxlslfzkvd8sr3exkh7st4qmgj4ywf5zcaxgqgdyunsj5juw5 ► Tether (USDT): 0xeC261d9b2EE4B6997a6a424067af165BAA4afE1a