Maciej Satkiewicz’s Post

What are the different types of machine learning? Classical machine learning is typically categorized by the manner in which algorithms improve their predictive accuracy. The four fundamental types of machine learning are: ✅ Supervised learning ✅ Unsupervised learning ✅ Semi-supervised learning ✅ Reinforcement learning The selection of an algorithm is influenced by the characteristics of the data and the problem at hand. Furthermore, many algorithms are versatile and can be applied across multiple learning paradigms. For example, deep learning architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can be employed in supervised, unsupervised, and reinforcement learning contexts, depending on the specific requirements of the task and the data available.

PyTorch Features PyTorch is a popular deep learning framework offering a wide range of features for building and training neural networks. Let’s explore its top 10 features and why it’s a preferred choice for many. https://lnkd.in/gkujYMnh

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.

𝐀 C𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐯𝐞 O𝐯𝐞𝐫𝐯𝐢𝐞𝐰 𝐨𝐟 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: It's history, relation to neural networks, DL algorithms, and its applications Article Link: https://lnkd.in/gnNbcEbG Key topics covered in the article: 🔅 What exactly is Deep Learning? 🔅 How does it relate to neural networks? 🔅 How does a Deep Learning model work? 🔅 Can you name a few important Deep Learning models? 🔅 Where can I apply Deep Learning? 🔅 Is this field new or does it have a long history? 🔅 What factors led to the Deep Learning revolution as we see today? 🔅 Video Explanations #deeplearning #machinelearning #neuralnetworks

I just earned new credentials from Coursera! 🎉 Last week, I received a badge and a certificate for completing the "Introduction to Deep Learning and Neural Networks with Keras" course. During this course, I reinforced and learned concepts such as: - Understand unsupervised deep learning models (e.g., autoencoders, restricted Boltzmann machines) - Understand supervised deep learning models (e.g., convolutional neural networks, recurrent networks) - Build deep learning models and networks using the Keras library. This course reminds me of my CNN project for multi-classification of bird images: https://lnkd.in/dzPsVKYz. Soon, I will look into improving this project and implementing more of what I’ve learned from the course. https://lnkd.in/dUHqxqvA https://lnkd.in/dJtWiYFr

Machine learning boasts a wide range of practical applications across various industries. In finance, for example, ML algorithms enable banks to identify fraudulent transactions by processing large volumes of data in real time, achieving levels of speed and accuracy that far exceed human capabilities.

Week#5 Deep Learning Basics II: Backpropagation and Gradient Descent. In this part of deep learning, backpropagation and gradient descent are essential for training neural networks. Backpropagation calculates the error's impact on each weight, while gradient descent adjusts these weights to minimize the loss function. Through iterative weight updates, the network becomes more accurate over time. Here's a simple implementation of gradient descent for linear regression, showcasing how weight adjustments reduce prediction errors. Mastering these concepts is key to building efficient neural networks.

*** I Like To Teach, Not Criticize *** ~ I recently saw a post on LinkedIn with an interesting table that lists standard machine learning algorithms. See the post image. ~ The problem is the algorithms are not machine learning. ~ The table provides a good, albeit incomplete, reference for classical statistics methods covered in Stat 101 - 201 classes. ~ Neural Networks is the basis of deep learning, which is the underpinning of A. I. ~ User caveat (Latin, user beware). --- B. Noted

Just finished the course “Deep Learning: Getting Started”.  It was an amazing journey diving into foundational concepts of deep learning, including neural networks, hidden layers, nodes, gradient descent, batches, cost functions, and epochs. I also explored validation and testing techniques and got hands-on experience with the Keras library. Excited to apply these learnings to real-world machine learning projects!   https://lnkd.in/dxPACt6F #machinelearning #deeplearning.

*** I Like To Teach, Not Criticize *** ~ I recently saw a post on LinkedIn with an interesting table that lists standard machine learning algorithms. See the post image. ~ The problem is the algorithms are not machine learning. ~ The table provides a good, albeit incomplete, reference for classical statistics methods covered in Stat 101 - 201 classes. ~ Neural Networks is the basis of deep learning, which is the underpinning of A. I. ~ User caveat (Latin, user beware). --- B. Noted