Mahdi Aghaei’s Post

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

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.

XGBoost: A Champion Among Machine Learning Algorithms XGBoost (eXtreme Gradient Boosting) isn't necessarily the top model in machine learning for every task, but it's a highly competitive and popular choice for several reasons: Strong Performance: Accurate: XGBoost excels at achieving high accuracy on various machine learning tasks, including classification and regression problems. Robust: It's less prone to overfitting compared to some other algorithms, leading to better generalization on unseen data. Efficiency: Scalability: XGBoost can handle large datasets efficiently due to its optimized implementation and parallel processing capabilities. Speed: Training XGBoost models can be faster than some other algorithms, especially for larger datasets. Flexibility: Regularization: It offers built-in techniques like L1 and L2 regularization to control model complexity and prevent overfitting. Customization: You can fine-tune various hyperparameters to optimize the model for your specific problem. Interpretability: Feature Importance: XGBoost provides insights into feature importance, helping you understand which features contribute most to the model's predictions. Ease of Use: Mature Libraries: Widely available libraries like scikit-learn and XGBoost's own Python library offer user-friendly interfaces for working with the algorithm. However, it's important to consider other factors when choosing a machine learning model: Problem Type: Different algorithms are better suited for specific tasks. For example, while XGBoost excels in many areas, it might not be the best choice for all deep learning applications. Data Characteristics: The size, type, and quality of your data can influence which model performs best. Computational Resources: Training complex models like XGBoost can require significant computational power, which might not be feasible for everyone. Here's a comparison with some other popular algorithms: Random Forest: Similar performance to XGBoost, but can be slower and less interpretable. Support Vector Machines (SVM): Can achieve high accuracy but may not scale well for large datasets and can be more sensitive to hyperparameter tuning. Deep Neural Networks: Often outperform XGBoost on complex problems with abundant data, but require significant computational resources and expertise. In conclusion, XGBoost is a powerful and versatile tool for various machine learning tasks due to its accuracy, efficiency, interpretability, and ease of use. But, it's not a one-size-fits-all solution. Consider your specific problem, data characteristics, and resource constraints when choosing the best model for your needs

"Key Takeaways❗️ There are four main types of machine learning algorithms: supervised, semi-supervised, unsupervised, and reinforcement. Different algorithms can be used to perform different tasks, such as data classification or regression. Among the most recent ML techniques, Afraz Jaffri highlighted joint embedding predictive architecture (JEPA), graph neural networks (GNNs), neuro-symbolic AI, and quantum ML. Knowing your way around the top ML algorithms can help enrich your understanding of how AI-driven applications like ChatGPT work under the hood. Want to know how machine learning algorithms work? Use Techopedia’s cheat sheet and ML algorithms list as an introduction to some of the top machine learning algorithms." https://lnkd.in/dhM7pXzr

Day 96 of my WTF classes. We started our a new topic today. Our topic today is machine learning. ◼ Today’s topic is machine learning wrap-up and introduction to deep learning. ◼ The first thing we had a recap on was what is linear regression. We looked at linear regression in relation to deep learning. ◼ We went over k nearest neighbor and support vector machine. ◼ We went over logistic regression. We looked at logistic regression in relation to deep learning. ◼ We looked at the difference between deep learning and machine learning, and we were also given reason why a data scientist should have good knowledge on deep learning. ◼ Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. First thing first I learnt what is a neural network and how it works. ◼ I was also taught types of deep learning. Today is the first class of deep learning. It was intense, it was just all about introduction to deep learning. As I continue to embark on this journey, I would continue to stay focused and wax on stronger than ever. Shout out to Tech4Dev thank you for this wonderful opportunity. A big shout out to my facilitator Mohamed Abd El-Mohsen.   #WTFC24 #WomenTechstersFellowship   #WomenTechsters   #AfricanWomaninTechnology   #WTFC4Orientation   #Tech4dev

Book: Efficient Deep Learning

Thank you all for reading my articles on Machine Learning. As some of you have requested, here is the recommended order for reading the articles. Simplified ML -> https://lnkd.in/gPDCCt7y ML Model Ingredients -> https://lnkd.in/g_Z_aw4T ML Dataset Nomenclature -> https://lnkd.in/gGb-hbYu Lets get hands-on -> https://lnkd.in/gTPky6TF Some Dos and Don'ts as per my personal experience :- In world of LLMs, traditional ML algorithms, Deep learning and NLP may seem irrelevant. But LLMs are build on top these foundations. So learning the foundations sets the context. Feel free to choose any dataset and run across any ML algorithm.  If you are new to ML, avoid getting too caught up in the implementation details of ML algorithms. Skip articles or pages that are heavy on statistics and mathematical notations to prevent frustration 😊. The dataset plays a crucial role in determining the accuracy of ML predictions. Therefore, understanding your data is essential #MachineLearning, #DataScience, #Algorithms

Title: Unlocking the Power of Deep Learning with 01_mathmul.ipynb 🔓 Attention all deep learning enthusiasts! 🚨 If you're looking to take your skills to the next level, you won't want to miss the "01_mathmul.ipynb" notebook from the fastai course "From Deep Learning Foundations to Stable Diffusion" 🌟 This notebook is a treasure trove of deep learning gems 💎, packed with insights and techniques that will help you optimize your models and push the boundaries of what's possible. From mastering matrix multiplication to harnessing the power of broadcasting and Einstein summation notation, this notebook has it all! 🧠💪 Have you discovered any mind-blowing insights from this notebook or the fastai course? Share your experiences in the comments below! 👇 Let's learn from each other and push the boundaries of what's possible in deep learning! 🤝🔬 #deeplearning #fastai #matrixmultiplication #optimization #knowledgesharing https://lnkd.in/gc_egf9Z

WTF is Regularization and What is it For? This article explains the concept of regularization and its significance in machine learning and deep learning.

A Visual Introduction to Deep Learning: Key Concepts and Fundamentals Understanding deep learning can be simplified with a visual-first approach. Here are the core concepts covered in the document: --- 🌟 Core Concepts of Deep Learning: 📌 Linear Regression:   The foundation for understanding relationships between variables and predicting outcomes. 📌 Non-Linear Regression:   Captures complex patterns in data that linear regression cannot. 📌 Binary Classification:   Differentiates between two distinct classes or categories. 📌 Multi-Class Classification:   Handles scenarios with more than two classes, providing a broader range of applications. --- 🔧 Foundational Aspects: 📊 Neurons: The basic units of neural networks that process inputs and generate outputs. 📊 Weighted Sum:  Combines inputs with weights to produce a single output value. 📊 Weights and Biases:   Parameters that the model adjusts during training to improve accuracy. 📊 Activation Functions: Introduce non-linearity into the model, enabling it to learn complex patterns. Examples include ReLU, sigmoid, and tanh. 📊 Datasets: The foundation of training and testing models, consisting of input data and corresponding outputs. 📊 Training and Testing:   The processes of teaching the model with data and evaluating its performance on unseen data. --- 💡 Learning Approach: 📌 Visual-First: The document uses diagrams and visual explanations to make deep learning concepts more intuitive. 📌 Concise Understanding: Focused on providing clear and brief explanations of complex topics. 📌 Machine Learning Algorithms: Offers insights into how different algorithms work and how they can be applied. --- This visual introduction serves as a valuable resource for anyone looking to grasp the essentials of deep learning and machine learning algorithms. #deeplearning #machinelearning #ai #neuralnetworks #datascience #datasciencecommunity #training #python #sql #al #ml Credit : Meor Amer - kDimensions