Backpropagation is the core learning mechanism behind neural networks In this video, we break down how loss is computed by measuring the error between predicted output and the actual target using Mean Squared Error (MSE). You’ll see how predictions flow through the network, how error is calculated, and why minimizing this loss is essential for training deep learning models. A must-watch if you’re building strong fundamentals in Machine Learning and AI #Backpropagation #ComputeLoss #MeanSquaredError #MSE #NeuralNetworks #DeepLearning #MachineLearning #AI #DataScience #MLBasics #AIFundamentals #ArtificialIntelligence #LearningAI #MathForML #Engineering #CS #TechEducation #StudyML

Pen & Paper Exercises in Machine Learning - with a focus on graphs We may have all heard the saying “use it or lose it”. We experience it when we feel rusty in a foreign language or sports that we have not practised in a while. Practice is important to maintain skills but it is also key when learning new ones. This is a reason why many textbooks and courses feature exercises. However, the solutions to the exercises feel often overly brief, or are sometimes not available at all. Rather than an opportunity to practice the new skills, the exercises then become a source of frustration and are ignored. This book contains a collection of exercises in Machine Learning with detailed solutions. The level of detail is, hopefully, sufficient for the reader to follow the solutions and understand the techniques used. The exercises, however, are not a replacement of a textbook or course on machine learning. The author, Michael Gutmann, assumes that the reader has already seen the relevant theory and concepts and would now like to deepen their understanding through solving exercises. While coding and computer simulations are extremely important in machine learning, the exercises in the book can (mostly) be solved with pen and paper. The focus on pen and-paper exercises reduced length and simplified the presentation. Moreover, it allows the reader to strengthen their mathematical skills. However, the exercises are ideally paired with computer exercises to further deepen the understanding. The exercises do not comprehensively cover all of machine learning but focus strongly on unsupervised methods, inference and learning. With chapters dedicated to Directed Graphical Models, Undirected Graphical Models, the Expressive Power of Graphical Models, and Factor Graphs and Message Passing. linkedin.com/feed/update/urn… #Book #MachineLearning #AI #DataScience #Graphs #UnsupervisedLearning #MLEducation, #StudyML -- The Year of the Graph's next newsletter on all things Knowledge Graph, Graph Analytics / Data Science / AI and Semantic Tech is due in Autumn 2025. Subscribe and follow to be in the know. Reach out if you'd like to be featured 👇 yearofthegraph.xyz/newslette…

Full room in our Modern Languages Programme Talk at @PoLIS_Bath @UniofBath, great to have so many enthusiastic visitors! #studyML #BelongAtBath