I have always been passionate about deep learning. Creating neural networks that learn and improve over time is like feeding a newborn. But sometimes I wonder “but what is really happening”. Yes we say how it happens, the concept in broad terms but nothing more. We use it and that's it! Of course you don't have to reinvent the wheel but sometimes it is essential to understand the internal mechanism to better take advantage of the technology offered to you. Today, I found my happiness in this book, and I hope it will be the same for you. Good reading
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FrAIday Fun! Here are some brain teasers. Top left is the hardest so I'll give you a clue -- AI's way of learning by taking small steps downhill to find the best answer... Free to use/copy/share! Answers? ok.... below! gradient descent, machine learning, deep learning, neural net
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At the risk of being an “old man shouting at a cloud”… In the old days, people used to complain that everyone rushed into deep learning without first learning the basics of linear algebra and statistics. But now, we have 'AI experts' who don’t even know what deep neural networks are.
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Two months ago, I only had a vague idea of how neural networks were constructed and used. After this course, I've been able to create a full transformer model and I am super proud of this achievement that required hours of work, watching videos, and hands-on labs practicing. If you strive to learn and be curious like me, I can only recommend this course to understand better the world of deep learning.
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I have completed a certification in Artificial Intelligence Fundamentals from Great Learning. In this amazing course, I learned about deep learning, neural networks, and their types, such as CNNs and RNNs. I also explored NLP and Computer Vision, along with their applications. I highly recommend this course to anyone looking to understand deep learning.
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Simple tips I would give anyone who wants to start exploring deep learning technologies: - Understand the basics of neural networks 🤖 - Experiment with open-source tools 🛠️ - Collaborate with experts in the field 👥 - Stay updated with the latest trends 📈 - Apply insights to real-world business challenges 🌍 Yes, it’s this easy to get started. Explore our client portfolio. Share if you're excited about deep learning! #DeepLearning #Innovation #webdev #webdesign #appdevelopment
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Simple tips I would give anyone who wants to start exploring deep learning technologies: - Understand the basics of neural networks 🤖 - Experiment with open-source tools 🛠️ - Collaborate with experts in the field 👥 - Stay updated with the latest trends 📈 - Apply insights to real-world business challenges 🌍 Yes, it’s this easy to get started. Explore our client portfolio. Share if you're excited about deep learning! #DeepLearning #Innovation #webdev #webdesign #appdevelopment
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A couple of years back, I stumbled upon an outstanding playlist by 3blue1brown on Linear Algebra basics, and I loved it! The animations and explanations were exceptionally well done. Recently, as I started learning GenAI/ ML, I discovered another fantastic playlist by them. I highly recommend it for building intuition on topics like Neural networks, Back propagation, and Attention models. Check it out here: https://bit.ly/4eBQhtX (Total time: ~2.5 hours) For those keen on a concise history of NLP and understanding the shift from models like RNNs to the revolutionary attention model, I recommend starting with this playlist: https://bit.ly/4h10txO (Total time: ~32 minutes) The clarity of concepts and engaging animations make learning these intricate subjects a truly fun experience. #GenAI #AttentionModel #NeuralNetworks #BuildIntuition #Learning
But what is a neural network? | Deep learning chapter 1
https://www.youtube.com/
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Top machine learning models include: Supervised Learning: Linear Regression, Logistic Regression, SVM, Decision Trees, Random Forest, Gradient Boosting Machines, k-NN, and Neural Networks. Unsupervised Learning: k-Means Clustering, Hierarchical Clustering, PCA, and Autoencoders. Deep Learning:CNNs, RNNs, LSTMs, and GANs. Ensemble Methods: Bagging (e.g., Random Forests) and Boosting (e.g., AdaBoost, XGBoost). Specialized Models:Transformer Models (e.g., BERT, GPT-3) for NLP and Graph Neural Networks for graph-based data. These models are chosen based on the problem type, data, and desired outcomes.
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Finally, I have another lesson on deep learning.
Completion Certificate for Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
coursera.org
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Hi Linkers, I have successfully completed the "Introduction to Neural Networks and Deep Learning" course offered by Great Learning Academy. This course provides an in-depth exploration of the fundamentals and advanced concepts in neural networks and deep learning. hashtag #GreatLearningAcademy hashtag #greatlearning hashtag #glacertificate
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