New update ! Episode 8 from this summer: Reusing an existing pre-trained model How to leverage the power of "transfer learning", a widely adopted technique in deep learning ? https://lnkd.in/eBHX7UvM
terradue’s Post
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🔍 Unsupervised Deep Learning: Dive into Auto-Encoders and Their Application for Clustering Image Data! 📊 In my latest Medium article, I explore how Deep Auto-Encoders can enhance clustering analysis for high-dimensional datasets, such as images, by learning lower-dimensional representations of the data. Starting with a basic overview of the architecture and training process, I guide you through implementing Auto-Encoders in PyTorch - a state-of-the-art deep learning library. Discover how to achieve more accurate clustering results on the high-dimensional MNIST dataset 📊💡 Read more here: https://lnkd.in/eStJfrHx #MachineLearning #DeepLearning #DataScience #Clustering #AutoEncoders #PyTorch #ContinuousLearning
Deep Auto-Encoders for Clustering: Understanding and Implementing in PyTorch
medium.com
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Implemented 10171259_credit_risk_prediction_based_on_DL_and_SMOTE Journal used for the implementation Journal title: Proposal of a model for credit risk prediction based on deep learning methods and SMOTE techniques for imbalanced dataset. This is an IEEE paper published in 2021 and this was implemented using neural network https://lnkd.in/dGyxWJvt
GitHub - pushkar243/DNN-creditrisk-analysis
github.com
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Design an Easy-to-Use Deep Learning Framework
Design an Easy-to-Use Deep Learning Framework
towardsdatascience.com
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Design an Easy-to-Use Deep Learning Framework
Design an Easy-to-Use Deep Learning Framework
towardsdatascience.com
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Published a new article on hyperparameters. If you are someone who jumped into deep learning without exploring the early history, this is for you. That said, this is a very beginner friendly article which anyone can read to unravel a few mysteries in deep learning. I'm sure you'll find a bunch of surprising or interesting origin stories in this article. All explained in simple English and some basic level math. We explore the fundamental origin of the concept of training a neural network and how and why hyperparameters came into existence, at their grassroot levels.
Explained: Hyperparameters in Deep Learning
medium.com
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Recently, I see and hear a lot about artificial intelligence, machine learning and deep learning. Talking about science and knowledge is very good and it makes me happy that the world community is interested in science, but what is important here is the scientific opinion. they criticize that they have no science and knowledge about even programming, let alone artificial intelligence and data science and deep learning. How beautiful it is if when we are interested in a field of knowledge, we eagerly go to learn it and become an expert in that field, then we talk about it and reject or accept the knowledge of other scientists. And if we are not experts, we should refer to experts and not talk about ourselves.
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In the data science community, TensorFlow and Keras are two widely used deep learning libraries. Although both enjoy great popularity, there are fundamental differences between them that determine their suitability for different applications. TensorFlow, a product of Google Brain, is an open-source library for machine learning and deep learning. It stands out for its flexibility and ability to work with a wide range of algorithms. It allows users to create deep learning models from scratch, providing complete control over the modeling process. However, this flexibility can be challenging as it requires a deep understanding of deep learning concepts and more detailed coding. Keras is a high-level interface for TensorFlow, designed to simplify the creation and training of deep learning models. It focuses on simplicity and ease of use, with clear and consistent APIs. It offers predefined and preprocessed modules that allow users to quickly prototype, making it especially useful for beginners. Nonetheless, Keras may not be the most suitable option for more complex applications due to its level of abstraction. The choice between TensorFlow and Keras largely depends on the project needs and the user's experience level. Below, I summarize the key points: - TensorFlow offers greater flexibility and control, making it more suitable for complex and customized applications. - Keras focuses on simplicity and speed, making it ideal for rapid prototyping and simpler projects. - The choice between TensorFlow and Keras largely depends on the project needs and the user's experience level. #machinelearning #ai #llmop #llm #artificialintelligence #datascience
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Multiresolution object detection with Esri's adapted open-source deep learning conversion tool, Text SAM. Adjusting hyperparameters helps enhance your feature extraction workflow to take it from unconvincing to production-ready results. https://ow.ly/1OCC50S2sYo
Multiresolution Object Detection with Text SAM
esri.com
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I have completed DataCamp's Introduction to Deep Learning with Keras!
null null's Statement of Accomplishment | DataCamp
datacamp.com
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Ever wished to learn deep learning through a structured approach while accumulating a complete framework from scratch? what if I told "Dive into Deep Learning" (D2l) is that perfect source? D2l is a 1,000+ page FREE book that teaches everything you need to know to become a 'real' deep learning practitioner while also taking you through the stages of building models completely from scratch with little to no reliance on existing deep learning frameworks! check it out! ->
Dive into Deep Learning ¶
d2l.ai
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