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Machine learning theory made easy.So, you want to master machine learning. Even though you have experience in the field, sometimes you still feel that something is missing. A look behind the curtain.Have you ever felt the learning curve to be so sharp that it was too difficult even to start? The theory was so dry and seemingly irrelevant that you were unable to go beyond the basics?If so, I am building something for you. I am working to create the best resource to study the mathematics of machine learning out there.Join the early access and be a part of the journey! Math explained, as simple as possible.Every concept is explained step by step, from elementary to advanced. No fancy tricks and mathematical magic. Intuition and motivation first, technical explanations second. Open up the black boxes.Machine learning is full of mysterious black boxes. Looking inside them allows you to be a master of your field and never be in the dark when things go wrong. Be a part of the process.This book is being written in public. With early access, youâll get each chapter as I finish, with a personal hotline to me. Is something not appropriately explained? Is a concept not motivated with applications? Let me know, and Iâll get right on it!Preview chapter!If you would like to see a sample chapter, I have prepared one for you! This one is about the Principal Component Analysis:https://mybinder.org/v2/gh/cosmic-cortex/mathematics-of-machine-learning-preview/main?filepath=PCA.ipynbWhat you’ll get?â¢ The latest version of the book, in an interactive Jupyter Book format + pdf.â¢ Exclusive access to a new sub-chapter each week, as I finish them. (See my planned roadmap below.)â¢ A personal hotline to me, where you can share your feedback to me, so I can build the best learning resource for you.What I’ll get?Writing a book is a long and difficult project. I want to do this the right way, so I decided to dedicate 100% of my time and energy to this. However, I can’t do this without your support. I created the Early Access Program for those wishing to join me in this journey. With you signing up for the Early Access Program, I’ll getâ¢ your financial support so I can work on this project full time,â¢ and your continual feedback, which is essential for me to write the best book on the subject for you.Refund policyIf you find that the Early Access Program is not for you, no worries! Let me know within 30 days of your purchase, and I’ll refund you immediately – no questions asked.Preliminary table of contentsPart 1. Linear algebraâ¢ Vector spaces (September 1st)â¢ Normed spaces (September 8th)â¢ Inner product spaces (September 15th)â¢ Linear transformations (September 22th)â¢ How linear transformations affect volume and magnitude (September 29th)â¢ Linear equations (October 6th)â¢ Eigenvalues and eigenvectors (October 13th)â¢ Special transformations (October 20th)Part 2. Functionsâ¢ Sequences (October 27th)â¢ Limits and continuity (November 3rd)â¢ Differentiation (November 10th)â¢ Minima and maxima (November 17th)â¢ The basics of gradient descent (November 24th)â¢ Integration in theory (December 1st)â¢ Integration in practice (December 8th)Part 3. Multivariable calculusâ¢ Partial derivatives and gradientsâ¢ Minima and maxima in multiple dimensionsâ¢ Gradient descent in its full formâ¢ Constrained optimizationâ¢ Integration in multiple dimensionsPart 4. Probability theoryâ¢ The mathematical concept of probabilityâ¢ Distributions and densitiesâ¢ Random variablesâ¢ Conditional probabilityâ¢ Expected valueâ¢ Information theory and entropyâ¢ Multidimensional distributionsPart 5. Statisticsâ¢ Fundamentals of parameter estimationâ¢ Maximum likelihood estimationâ¢ The Bayesian viewpoint of statisticsâ¢ Bias and varianceâ¢ Measuring predictive performance of statistical modelsâ¢ Multivariate methodsPart 6. Machine learningâ¢ The taxonomy of machine learning tasksâ¢ Linear and logistic regressionâ¢ Fundamentals of clusteringâ¢ Principal Component Analysisâ¢ Most common loss functions and whatâs behind themâ¢ Regularization of machine learning modelsâ¢ t-distributed stochastic neighbor embeddingPart 7. Neural networksâ¢ Logistic regression, revisitedâ¢ Activation functionsâ¢ Computational graphsâ¢ Backpropagationâ¢ Loss functions, from a neural network perspectiveâ¢ Weight initializationPart 8. Advanced optimizationâ¢ Stochastic gradient descentâ¢ Adaptive methodsâ¢ Accelerated schemesâ¢ The Lookahead optimizerâ¢ RangerPart 9. Convolutional networksâ¢ The convolutional layer, in-depthâ¢ Dropout and BatchNormâ¢ Fundamental tasks of computer visionâ¢ Alexnet and Resnetâ¢ Autoencodersâ¢ Generative Adversarial NetworksPlanned roadmap2021 Q3 – 2022 Q1: Core math chaptersâ¢ Calculus and multivariate calculusâ¢ Linear algebraâ¢ Probability theoryâ¢ Mathematical statistics2022 Q1 – 2022 Q2: Machine learning chaptersâ¢ Classical machine learningâ¢ Neural networks and convolutional networksâ¢ Advanced topics2022 Q3 – 2022 Q4: Editing and finishing touchesâ¢ Finalizing and prettifying figuresâ¢ Editing the text and improving the style

Mathematics of Machine Learning early accessMachine learning theory made easy.So, you want to master machine learning. Even though you have experience in the field, sometimes you still feel that something is missing. A look behind the curtain.Have you ever felt the learning curve to be so sharp that it was too difficult even to start? The theory was so dry and seemingly irrelevant that you were unable to go beyond the basics?If so, I am building something for you. I am working to create the best resource to study the mathematics of machine learning out there.Join the early access and be a part of the journey! Math explained, as simple as possible.Every concept is explained step by step, from elementary to advanced. No fancy tricks and mathematical magic. Intuition and motivation first, technical explanations second. Open up the black boxes.Machine learning is full of mysterious black boxes. Looking inside them allows you to be a master of your field and never be in the dark when things go wrong. Be a part of the process.This book is being written in public. With early access, youâll get each chapter as I finish, with a personal hotline to me. Is something not appropriately explained? Is a concept not motivated with applications? Let me know, and Iâll get right on it!Preview chapter!If you would like to see a sample chapter, I have prepared one for you! This one is about the Principal Component Analysis:https://mybinder.org/v2/gh/cosmic-cortex/mathematics-of-machine-learning-preview/main?filepath=PCA.ipynbWhat you'll get?â¢ The latest version of the book, in an interactive Jupyter Book format + pdf.â¢ Exclusive access to a new sub-chapter each week, as I finish them. (See my planned roadmap below.)â¢ A personal hotline to me, where you can share your feedback to me, so I can build the best learning resource for you.What I'll get?Writing a book is a long and difficult project. I want to do this the right way, so I decided to dedicate 100% of my time and energy to this. However, I can't do this without your support. I created the Early Access Program for those wishing to join me in this journey. With you signing up for the Early Access Program, I'll getâ¢ your financial support so I can work on this project full time,â¢ and your continual feedback, which is essential for me to write the best book on the subject for you.Refund policyIf you find that the Early Access Program is not for you, no worries! Let me know within 30 days of your purchase, and I'll refund you immediately - no questions asked.Preliminary table of contentsPart 1. Linear algebraâ¢ Vector spaces (September 1st)â¢ Normed spaces (September 8th)â¢ Inner product spaces (September 15th)â¢ Linear transformations (September 22th)â¢ How linear transformations affect volume and magnitude (September 29th)â¢ Linear equations (October 6th)â¢ Eigenvalues and eigenvectors (October 13th)â¢ Special transformations (October 20th)Part 2. Functionsâ¢ Sequences (October 27th)â¢ Limits and continuity (November 3rd)â¢ Differentiation (November 10th)â¢ Minima and maxima (November 17th)â¢ The basics of gradient descent (November 24th)â¢ Integration in theory (December 1st)â¢ Integration in practice (December 8th)Part 3. Multivariable calculusâ¢ Partial derivatives and gradientsâ¢ Minima and maxima in multiple dimensionsâ¢ Gradient descent in its full formâ¢ Constrained optimizationâ¢ Integration in multiple dimensionsPart 4. Probability theoryâ¢ The mathematical concept of probabilityâ¢ Distributions and densitiesâ¢ Random variablesâ¢ Conditional probabilityâ¢ Expected valueâ¢ Information theory and entropyâ¢ Multidimensional distributionsPart 5. Statisticsâ¢ Fundamentals of parameter estimationâ¢ Maximum likelihood estimationâ¢ The Bayesian viewpoint of statisticsâ¢ Bias and varianceâ¢ Measuring predictive performance of statistical modelsâ¢ Multivariate methodsPart 6. Machine learningâ¢ The taxonomy of machine learning tasksâ¢ Linear and logistic regressionâ¢ Fundamentals of clusteringâ¢ Principal Component Analysisâ¢ Most common loss functions and whatâs behind themâ¢ Regularization of machine learning modelsâ¢ t-distributed stochastic neighbor embeddingPart 7. Neural networksâ¢ Logistic regression, revisitedâ¢ Activation functionsâ¢ Computational graphsâ¢ Backpropagationâ¢ Loss functions, from a neural network perspectiveâ¢ Weight initializationPart 8. Advanced optimizationâ¢ Stochastic gradient descentâ¢ Adaptive methodsâ¢ Accelerated schemesâ¢ The Lookahead optimizerâ¢ RangerPart 9. Convolutional networksâ¢ The convolutional layer, in-depthâ¢ Dropout and BatchNormâ¢ Fundamental tasks of computer visionâ¢ Alexnet and Resnetâ¢ Autoencodersâ¢ Generative Adversarial NetworksPlanned roadmap2021 Q3 - 2022 Q1: Core math chaptersâ¢ Calculus and multivariate calculusâ¢ Linear algebraâ¢ Probability theoryâ¢ Mathematical statistics2022 Q1 - 2022 Q2: Machine learning chaptersâ¢ Classical machine learningâ¢ Neural networks and convolutional networksâ¢ Advanced topics2022 Q3 - 2022 Q4: Editing and finishing touchesâ¢ Finalizing and prettifying figuresâ¢ Editing the text and improving the style tivadar.gumroad.com

Machine learning theory made easy.So, you want to master machine learning. Even though you have experience in the field, sometimes you still feel that something is missing. A look behind the curtain.Have you ever felt the learning curve to be so sharp that it was too difficult even to start? The theory was so dry and seemingly irrelevant that you were unable to go beyond the basics?If so, I am building something for you. I am working to create the best resource to study the mathematics of machine learning out there.Join the early access and be a part of the journey! Math explained, as simple as possible.Every concept is explained step by step, from elementary to advanced. No fancy tricks and mathematical magic. Intuition and motivation first, technical explanations second. Open up the black boxes.Machine learning is full of mysterious black boxes. Looking inside them allows you to be a master of your field and never be in the dark when things go wrong. Be a part of the process.This book is being written in public. With early access, youâll get each chapter as I finish, with a personal hotline to me. Is something not appropriately explained? Is a concept not motivated with applications? Let me know, and Iâll get right on it!Preview chapter!If you would like to see a sample chapter, I have prepared one for you! This one is about the Principal Component Analysis:https://mybinder.org/v2/gh/cosmic-cortex/mathematics-of-machine-learning-preview/main?filepath=PCA.ipynbWhat you’ll get?â¢ The latest version of the book, in an interactive Jupyter Book format + pdf.â¢ Exclusive access to a new sub-chapter each week, as I finish them. (See my planned roadmap below.)â¢ A personal hotline to me, where you can share your feedback to me, so I can build the best learning resource for you.What I’ll get?Writing a book is a long and difficult project. I want to do this the right way, so I decided to dedicate 100% of my time and energy to this. However, I can’t do this without your support. I created the Early Access Program for those wishing to join me in this journey. With you signing up for the Early Access Program, I’ll getâ¢ your financial support so I can work on this project full time,â¢ and your continual feedback, which is essential for me to write the best book on the subject for you.Refund policyIf you find that the Early Access Program is not for you, no worries! Let me know within 30 days of your purchase, and I’ll refund you immediately – no questions asked.Preliminary table of contentsPart 1. Linear algebraâ¢ Vector spaces (September 1st)â¢ Normed spaces (September 8th)â¢ Inner product spaces (September 15th)â¢ Linear transformations (September 22th)â¢ How linear transformations affect volume and magnitude (September 29th)â¢ Linear equations (October 6th)â¢ Eigenvalues and eigenvectors (October 13th)â¢ Special transformations (October 20th)Part 2. Functionsâ¢ Sequences (October 27th)â¢ Limits and continuity (November 3rd)â¢ Differentiation (November 10th)â¢ Minima and maxima (November 17th)â¢ The basics of gradient descent (November 24th)â¢ Integration in theory (December 1st)â¢ Integration in practice (December 8th)Part 3. Multivariable calculusâ¢ Partial derivatives and gradientsâ¢ Minima and maxima in multiple dimensionsâ¢ Gradient descent in its full formâ¢ Constrained optimizationâ¢ Integration in multiple dimensionsPart 4. Probability theoryâ¢ The mathematical concept of probabilityâ¢ Distributions and densitiesâ¢ Random variablesâ¢ Conditional probabilityâ¢ Expected valueâ¢ Information theory and entropyâ¢ Multidimensional distributionsPart 5. Statisticsâ¢ Fundamentals of parameter estimationâ¢ Maximum likelihood estimationâ¢ The Bayesian viewpoint of statisticsâ¢ Bias and varianceâ¢ Measuring predictive performance of statistical modelsâ¢ Multivariate methodsPart 6. Machine learningâ¢ The taxonomy of machine learning tasksâ¢ Linear and logistic regressionâ¢ Fundamentals of clusteringâ¢ Principal Component Analysisâ¢ Most common loss functions and whatâs behind themâ¢ Regularization of machine learning modelsâ¢ t-distributed stochastic neighbor embeddingPart 7. Neural networksâ¢ Logistic regression, revisitedâ¢ Activation functionsâ¢ Computational graphsâ¢ Backpropagationâ¢ Loss functions, from a neural network perspectiveâ¢ Weight initializationPart 8. Advanced optimizationâ¢ Stochastic gradient descentâ¢ Adaptive methodsâ¢ Accelerated schemesâ¢ The Lookahead optimizerâ¢ RangerPart 9. Convolutional networksâ¢ The convolutional layer, in-depthâ¢ Dropout and BatchNormâ¢ Fundamental tasks of computer visionâ¢ Alexnet and Resnetâ¢ Autoencodersâ¢ Generative Adversarial NetworksPlanned roadmap2021 Q3 – 2022 Q1: Core math chaptersâ¢ Calculus and multivariate calculusâ¢ Linear algebraâ¢ Probability theoryâ¢ Mathematical statistics2022 Q1 – 2022 Q2: Machine learning chaptersâ¢ Classical machine learningâ¢ Neural networks and convolutional networksâ¢ Advanced topics2022 Q3 – 2022 Q4: Editing and finishing touchesâ¢ Finalizing and prettifying figuresâ¢ Editing the text and improving the style

BUY↗askafiftivadarMachine learning theory made easy.So, you want to master machine learning. Even though you have experience in the field, sometimes you still feel that something is missing. A look behind the curtain.Have you ever felt the learning curve to be so sharp that it was too difficult even to start? The theory was so dry and seemingly irrelevant that you were unable to go beyond the basics?If so, I am building something for you. I am working to create the best resource to study the mathematics of machine learning out there.Join the early access and be a part of the journey! Math explained, as simple as possible.Every concept is explained step by step, from elementary to advanced. No fancy tricks and mathematical magic. Intuition and motivation first, technical explanations second. Open up the black boxes.Machine learning is full of mysterious black boxes. Looking inside them allows you to be a master of your field and never be in the dark when things go wrong. Be a part of the process.This book is being written in public. With early access, youâll get each chapter as I finish, with a personal hotline to me. Is something not appropriately explained? Is a concept not motivated with applications? Let me know, and Iâll get right on it!Preview chapter!If you would like to see a sample chapter, I have prepared one for you! This one is about the Principal Component Analysis:https://mybinder.org/v2/gh/cosmic-cortex/mathematics-of-machine-learning-preview/main?filepath=PCA.ipynbWhat you'll get?â¢ The latest version of the book, in an interactive Jupyter Book format + pdf.â¢ Exclusive access to a new sub-chapter each week, as I finish them. (See my planned roadmap below.)â¢ A personal hotline to me, where you can share your feedback to me, so I can build the best learning resource for you.What I'll get?Writing a book is a long and difficult project. I want to do this the right way, so I decided to dedicate 100% of my time and energy to this. However, I can't do this without your support. I created the Early Access Program for those wishing to join me in this journey. With you signing up for the Early Access Program, I'll getâ¢ your financial support so I can work on this project full time,â¢ and your continual feedback, which is essential for me to write the best book on the subject for you.Refund policyIf you find that the Early Access Program is not for you, no worries! Let me know within 30 days of your purchase, and I'll refund you immediately - no questions asked.Preliminary table of contentsPart 1. Linear algebraâ¢ Vector spaces (September 1st)â¢ Normed spaces (September 8th)â¢ Inner product spaces (September 15th)â¢ Linear transformations (September 22th)â¢ How linear transformations affect volume and magnitude (September 29th)â¢ Linear equations (October 6th)â¢ Eigenvalues and eigenvectors (October 13th)â¢ Special transformations (October 20th)Part 2. Functionsâ¢ Sequences (October 27th)â¢ Limits and continuity (November 3rd)â¢ Differentiation (November 10th)â¢ Minima and maxima (November 17th)â¢ The basics of gradient descent (November 24th)â¢ Integration in theory (December 1st)â¢ Integration in practice (December 8th)Part 3. Multivariable calculusâ¢ Partial derivatives and gradientsâ¢ Minima and maxima in multiple dimensionsâ¢ Gradient descent in its full formâ¢ Constrained optimizationâ¢ Integration in multiple dimensionsPart 4. Probability theoryâ¢ The mathematical concept of probabilityâ¢ Distributions and densitiesâ¢ Random variablesâ¢ Conditional probabilityâ¢ Expected valueâ¢ Information theory and entropyâ¢ Multidimensional distributionsPart 5. Statisticsâ¢ Fundamentals of parameter estimationâ¢ Maximum likelihood estimationâ¢ The Bayesian viewpoint of statisticsâ¢ Bias and varianceâ¢ Measuring predictive performance of statistical modelsâ¢ Multivariate methodsPart 6. Machine learningâ¢ The taxonomy of machine learning tasksâ¢ Linear and logistic regressionâ¢ Fundamentals of clusteringâ¢ Principal Component Analysisâ¢ Most common loss functions and whatâs behind themâ¢ Regularization of machine learning modelsâ¢ t-distributed stochastic neighbor embeddingPart 7. Neural networksâ¢ Logistic regression, revisitedâ¢ Activation functionsâ¢ Computational graphsâ¢ Backpropagationâ¢ Loss functions, from a neural network perspectiveâ¢ Weight initializationPart 8. Advanced optimizationâ¢ Stochastic gradient descentâ¢ Adaptive methodsâ¢ Accelerated schemesâ¢ The Lookahead optimizerâ¢ RangerPart 9. Convolutional networksâ¢ The convolutional layer, in-depthâ¢ Dropout and BatchNormâ¢ Fundamental tasks of computer visionâ¢ Alexnet and Resnetâ¢ Autoencodersâ¢ Generative Adversarial NetworksPlanned roadmap2021 Q3 - 2022 Q1: Core math chaptersâ¢ Calculus and multivariate calculusâ¢ Linear algebraâ¢ Probability theoryâ¢ Mathematical statistics2022 Q1 - 2022 Q2: Machine learning chaptersâ¢ Classical machine learningâ¢ Neural networks and convolutional networksâ¢ Advanced topics2022 Q3 - 2022 Q4: Editing and finishing touchesâ¢ Finalizing and prettifying figuresâ¢ Editing the text and improving the style

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