Welcoem to posts!!
in the future - u will be able to do some more stuff here,,,!! like pat catgirl- i mean um yeah... for now u can only see others's posts :c
π Introducing "Dev Readers Notebook" β A brand-new series dedicated to Python and web programming! π This series is designed to cover all the essential concepts and guide you through your programming journey.
π What to Expect:
10 Topics: Each topic will be covered in detail over a 10-day period.
20 Concepts per Video: Each video will delve into 20 key concepts, ensuring a comprehensive understanding.
Wide Range of Subjects: From SQL and databases to deployment, design, programming, Docker, Figma, frameworks, and more.
This series is packed with valuable insights and practical advice to help you master Python and web development. Iβve put in a lot of time and effort to create this content, and I hope it will be a great resource for you.
π
Starting Tomorrow: Watch each video daily to build your skills progressively. And donβt worry β the Pandas series is still in development and will be available after this series.
Thanks for tuning in, and I hope you find this series informative and useful!
#Python #WebProgramming #PythonSeries #SQL #Databases #Deployment #Design #Programming #Docker #Figma #Frameworks #TechTutorials #Coding #ProgrammingBasics #PythonTips #LearningPython #DevReadersNotebook
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Two new series maybe coming next week before the Java and Python intermediate playlist. Stay tuned for those! π
#NewSeries #Java #Python #Programming #TechUpdates #Coding #LearnToCode #TechCommunity #StayTuned
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Boosting Neural Network Performance: Width vs. Depth :
When it comes to enhancing the capacity of a neural network, you have two main options: making it wider or making it deeper. Each approach has its own advantages and is better suited for different types of data and tasks.
Wider Networks >
Wider networks involve adding more units (neurons) to the existing layers. This expansion allows the network to capture more detailed linear relationships. These networks can handle large amounts of data with many features and are often easier to train because they can approximate complex functions with fewer layers.
Example:
Imagine you're working on a dataset where the relationship between inputs and outputs is mostly linear, like predicting house prices based on square footage, number of rooms, and location. A wider network can easily learn these straightforward correlations.
Deeper Networks >
On the other hand, deeper networks involve adding more layers to the architecture. This depth enables the network to learn and represent more complex, nonlinear relationships. Deeper networks excel at capturing intricate patterns and interactions in the data.
Example:
Consider a dataset for image recognition, where you need to identify objects within pictures. The relationship between pixel values and object identification is highly nonlinear. A deeper network, with its multiple layers, can progressively extract higher-level features (edges, textures, shapes) from raw pixel data.
Which One to Choose?
The choice between a wider or deeper network depends on your specific dataset and task. If your data exhibits mostly linear relationships, a wider network might be more efficient and easier to train. Conversely, if your data contains complex, nonlinear patterns, a deeper network could provide better performance.
#MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #DataScience #TechTips #AI #BigData #DataAnalysis
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Which of the following best defines "Overfitting" in machine learning?
#MachineLearning #MLQuiz #AIQuiz #Overfitting
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What's Your Favorite Deep Learning Tool? Vote Now!
π€ Dive into the world of Deep Learning with us! Which tool do you find most exciting for your projects? Cast your vote and let's explore together! π
Join the discussion and discover more about Deep Learning tools that power AI innovation!
#DeepLearning #MachineLearning #AI #TensorFlow #PyTorch #Keras #DataScience
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Training a Neural Network with Stochastic Gradient Descent
#DeepLearning #NeuralNetworks #SGD #StochasticGradientDescent #MachineLearning #DataScience #AI #KaggleCourses #DLCourse
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AI and ML Stuffs π