DeepLearning Notes.pdf
19.1 MB
DeepLearning Notes
Matrix Theory and Linear Algebra, 2018.pdf
8.7 MB
Matrix Theory and Linear Algebra
Peter Selinger, 2018
Peter Selinger, 2018
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Forwarded from Python Projects & Resources
𝗚𝗼𝗼𝗴𝗹𝗲 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
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Whether you’re a beginner or looking to sharpen your skills, these resources will help you stay ahead in the tech game.
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Essential Python Libraries for Data Analytics 😄👇
Python Free Resources: https://news.1rj.ru/str/pythondevelopersindia
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
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Python Free Resources: https://news.1rj.ru/str/pythondevelopersindia
1. NumPy:
- Efficient numerical operations and array manipulation.
2. Pandas:
- Data manipulation and analysis with powerful data structures (DataFrame, Series).
3. Matplotlib:
- 2D plotting library for creating visualizations.
4. Scikit-learn:
- Machine learning toolkit for classification, regression, clustering, etc.
5. TensorFlow:
- Open-source machine learning framework for building and deploying ML models.
6. PyTorch:
- Deep learning library, particularly popular for neural network research.
7. Django:
- High-level web framework for building robust, scalable web applications.
8. Flask:
- Lightweight web framework for building smaller web applications and APIs.
9. Requests:
- HTTP library for making HTTP requests.
10. Beautiful Soup:
- Web scraping library for pulling data out of HTML and XML files.
As a beginner, you can start with Pandas and Numpy libraries for data analysis. If you want to transition from Data Analyst to Data Scientist, then you can start applying ML libraries like Scikit-learn, Tensorflow, Pytorch, etc. in your data projects.
Share with credits: https://news.1rj.ru/str/sqlspecialist
Hope it helps :)
👍2
Forwarded from Python Projects & Resources
𝟰 𝗙𝗥𝗘𝗘 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
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These free, Microsoft-backed courses are a game-changer!
With these resources, you’ll gain the skills and confidence needed to shine in the data analytics world—all without spending a penny.
𝐋𝐢𝐧𝐤 👇:-
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Ai revolution and learning path 📚
The current AI revolution is exhilarating 🚀, pushing the boundaries of what's possible across different sectors. Yet, it's essential to anchor oneself in the foundational elements that enable these advancements:
- Neural Networks: Grasp the basics and variations, understanding how they process information and learning about key types like CNNs and RNNs 🧠.
- Loss Functions and Optimization: Familiarize yourself with how loss functions measure model performance and the role of optimization techniques like gradient descent in improving accuracy 🔍.
- Activation Functions: Learn about the significance of activation functions such as ReLU and Sigmoid in capturing non-linear patterns 🔑.
- Training and Evaluation: Master the nuanced art of model training, from preventing overfitting with regularization to fine-tuning hyperparameters for optimal performance 🎯.
- Data Handling: Recognize the importance of data preprocessing and augmentation in enhancing model robustness. 💾
- Stay Updated: Keep an eye on emerging trends, like transformers and GANs, and understand the ethical considerations in AI application. 🌐
Immersing yourself in these core areas not only prepares you for the ongoing AI wave but sets a solid foundation for navigating future advancements. Balancing a strong grasp of fundamental concepts with an awareness of new technologies is key to thriving in the AI domain.
The current AI revolution is exhilarating 🚀, pushing the boundaries of what's possible across different sectors. Yet, it's essential to anchor oneself in the foundational elements that enable these advancements:
- Neural Networks: Grasp the basics and variations, understanding how they process information and learning about key types like CNNs and RNNs 🧠.
- Loss Functions and Optimization: Familiarize yourself with how loss functions measure model performance and the role of optimization techniques like gradient descent in improving accuracy 🔍.
- Activation Functions: Learn about the significance of activation functions such as ReLU and Sigmoid in capturing non-linear patterns 🔑.
- Training and Evaluation: Master the nuanced art of model training, from preventing overfitting with regularization to fine-tuning hyperparameters for optimal performance 🎯.
- Data Handling: Recognize the importance of data preprocessing and augmentation in enhancing model robustness. 💾
- Stay Updated: Keep an eye on emerging trends, like transformers and GANs, and understand the ethical considerations in AI application. 🌐
Immersing yourself in these core areas not only prepares you for the ongoing AI wave but sets a solid foundation for navigating future advancements. Balancing a strong grasp of fundamental concepts with an awareness of new technologies is key to thriving in the AI domain.
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3. Support multi-currency, smart investment income 25%% up to 40% income.
4. Quantification is reset every 24 hours, and each person can participate in quantitative trading income once a day.
5. Recommend three-level agent invitation rewards, the more invitations, the more rewards, there is no upper limit [A reward 10%, B reward 5%, C reward 3% = 18% reward], send the invitation link to share to your social software, such as: Tiktok, Facebook, Twitter, YouTube, Instagram, WhatsApp group, Telegram group, etc.
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𝗟𝗲𝗮𝗿𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 & 𝗘𝗹𝗲𝘃𝗮𝘁𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗚𝗮𝗺𝗲!😍
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Want to turn raw data into stunning visual stories?📊
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Here are 5 key Python libraries/ concepts that are particularly important for data analysts:
1. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data. Pandas offers functions for reading and writing data, cleaning and transforming data, and performing data analysis tasks like filtering, grouping, and aggregating.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is often used in conjunction with Pandas for numerical computations and data manipulation.
3. Matplotlib and Seaborn: Matplotlib is a popular plotting library in Python that allows you to create a wide variety of static, interactive, and animated visualizations. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive and informative statistical graphics. These libraries are essential for data visualization in data analysis projects.
4. Scikit-learn: Scikit-learn is a machine learning library in Python that provides simple and efficient tools for data mining and data analysis tasks. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. Scikit-learn also offers tools for model evaluation, hyperparameter tuning, and model selection.
5. Data Cleaning and Preprocessing: Data cleaning and preprocessing are crucial steps in any data analysis project. Python offers libraries like Pandas and NumPy for handling missing values, removing duplicates, standardizing data types, scaling numerical features, encoding categorical variables, and more. Understanding how to clean and preprocess data effectively is essential for accurate analysis and modeling.
By mastering these Python concepts and libraries, data analysts can efficiently manipulate and analyze data, create insightful visualizations, apply machine learning techniques, and derive valuable insights from their datasets.
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1. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data. Pandas offers functions for reading and writing data, cleaning and transforming data, and performing data analysis tasks like filtering, grouping, and aggregating.
2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently. NumPy is often used in conjunction with Pandas for numerical computations and data manipulation.
3. Matplotlib and Seaborn: Matplotlib is a popular plotting library in Python that allows you to create a wide variety of static, interactive, and animated visualizations. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive and informative statistical graphics. These libraries are essential for data visualization in data analysis projects.
4. Scikit-learn: Scikit-learn is a machine learning library in Python that provides simple and efficient tools for data mining and data analysis tasks. It includes a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. Scikit-learn also offers tools for model evaluation, hyperparameter tuning, and model selection.
5. Data Cleaning and Preprocessing: Data cleaning and preprocessing are crucial steps in any data analysis project. Python offers libraries like Pandas and NumPy for handling missing values, removing duplicates, standardizing data types, scaling numerical features, encoding categorical variables, and more. Understanding how to clean and preprocess data effectively is essential for accurate analysis and modeling.
By mastering these Python concepts and libraries, data analysts can efficiently manipulate and analyze data, create insightful visualizations, apply machine learning techniques, and derive valuable insights from their datasets.
Credits: https://news.1rj.ru/str/free4unow_backup
ENJOY LEARNING 👍👍
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𝗜𝗻𝗳𝗼𝘀𝘆𝘀 𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀😍
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Infosys Springboard is offering a wide range of 100% free courses with certificates to help you upskill and boost your resume—at no cost.
Whether you’re a student, graduate, or working professional, this platform has something valuable for everyone.
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https://pdlink.in/4jsHZXf
Enroll For FREE & Get Certified 🎓