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BRTC Makeover!

bumpyroadtocode Blog January 21, 2019January 21, 2019 0 Minutes

bumpy road to code [BRTC] gets a makeover, new color scheme and logo, yay!
Also, yes I have neglected my code-blog for a while, its true, I have been massively busy, but I believe I can do better in the future, I promise 🙂

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Published January 21, 2019January 21, 2019

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Check out my Machine Learning and Artificial Intelligence Instagram Account: @artificialintelligence.digest

How Artificial Intelligence, Machine Learning and Deep Learning integrate. . :: "Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data." - SAS . :: This is the second post to untangle the differences between AI, ML and DL. As the diagram shows, Artificial Intelligence is the paradigm or a collection of techniques for machines to learn and use the knowledge to solve problems autonomously. Machine Learning is one of those techniques and subsequently, Deep Learning is a Machine Learning Method, next to Unsupervised and Supervised Learning. . #artificialintelligence #machinelearning #deeplearning #ai #ml #dl #data #dataprocessing #datascience #datascientist #datavisualization #statistics #analytics #dataanalytics #supervisedlearning #unsupervisedlearning #naturallanguageprocessing #nlp #expertsystems #vision #speech #planning #robotics #contentextraction #classification #machinetranslation #questionanswering #textgeneration #imagerecognition #machinevision

Machine Learning Model Random Forest Regression Regression . :: Random Forest Regression is one of the most powerful regression models out there. You can use either multiple algorithms or the same algorithm multiple times to make a more powerful model then your original. . :: In the example above, for simplicity, I chose to use multiple decision tree algorithms. The dataset is divided into subsets and each subset is training a tree. For the prediction step, the same data is passed through all trees and the average of the single tree y is taken as the resulting y. . :: At least take 500 trees for your forest, which means you will end up with 500 predictions for the one data point in question (x1), this is very powerful! . #artificialintelligence #machinelearning #ai #ml #data #randomforestregression #decisiontreeregression #regression #dataprocessing #datascience #datascientist #datavisualization #statistics #analytics #dataanalytics

Machine Learning VS Deep Learning . :: @nvidia Michael Copeland „Artificial intelligence is the future. Artificial intelligence is science fiction. Artificial intelligence is already part of our everyday lives. All those statements are true, it just depends on what flavor of AI you are referring to. . For example, when Google DeepMind’s AlphaGo program defeated South Korean Master Lee Se-dol in the board game Go earlier this year, the terms AI, machine learning, and deep learning were used in the media to describe how DeepMind won. And all three are part of the reason why AlphaGo trounced Lee Se-Dol. But they are not the same things. . The easiest way to think of their relationship is to visualize them as concentric circles with AI — the idea that came first — the largest, then machine learning — which blossomed later, and finally deep learning — which is driving today’s AI explosion — fitting inside both.“ . #machinelearning #deeplearning #neuralnetworks #perceptron #learning #arificialintelligence #data #featureextraction #classification #datascience #dataprocessing #patternrecognition #statistics #statisticalmodelling #kdd #databases #ai #datamining #neurocomputing #patternrecognition

Machine Learning Model Multiple Linear Regression . :: Multiple Linear Regression is quite an exciting machine learning model. Instead of looking into one independent variable and one dependent variable, we can look into multiple independent variables, like in the example above experience x1 and age x2 to predict the dependent variable salary y. I chose to only use two independent variables because this allows me to represent the model in a three-dimensional space. Of course, Multiple Linear Regression gets more exciting with many more independent variables, especially looking into variable coefficients, which determines a weight for the variable, indicating how important one independent variable is or furthermore exploring ways of dimensionality reduction, once we have too many independent variables and we need to figure out which are the most impactful independent variables. . #artificialintelligence #machinelearning #ai #ml #data #multiplelinearregression #regression #dataprocessing #datascience #datascientist #datavisualization #statistics #analytics #dataanalytics

How Artificial Intelligence, Machine Learning and Deep Learning integrate. . :: A lot of people ask me, what is this good for and how is Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) related to one another. I will start a new series of images explaining this, in baby steps, starting from a big picture and slowly going into details. This is the first post of that series of exploring AI, ML and DL in more detail. Yes, this is already a lot of detail as a start, but somewhere one needs to start I figured. . :: So, this first post could make a point about how AI, ML and DL are not to be looked at as different things, in fact, they rely on each other. AI is using methods of ML and DL to mimic human behaviour, like speaking (cortana by @Microsoft), or seeing (self-driving cars by @Tesla and many more). ML is a method using statistics, probably the earliest form of ML were expert systems. Finally, DL is one specific technique of ML, to be precise, neural networks and even further, multi-layered neural networks. . #artificialintelligence #machinelearning #deeplearning #ai #ml #dl #data #dataprocessing #datascience #datascientist #datavisualization #statistics #analytics #dataanalytics

Machine Learning Model Dicision Tree Regression . :: There are two types of Decision Trees under the term CART, Classification Trees and Regression Trees. In the image above is a Regression Tree Model explained. Your data might be of two, three or any other dimension, actually the Decision Tree Regression is very powerful in higher dimension regression problems. The example shown has three dimensions, the experience variable x1 in years, the salary variable x2 in kilo amount and the according z variable for each data point {x1, x2}. How this works is, Step 1: to split the initial two dimensional data into so called leafs. Step 2: each leaf has now a collection of data points, the z value of those points is averaged for each leaf. Step 3: Introducing a new data point {x1, x2} and through Step 4, the decision tree we can now find out its corresponding z value from the leaf it lies in. . #artificialintelligence #machinelearning #ai #ml #data #decisiontreeregression #regression #dataprocessing #datascience #datascientist #decisiontrees #naivebayes #markov #neuralnetworks #geneticprograms #supportvectors #datavisualization #statistics #analytics #dataanalytics

The Five Tribes in Machine Learning Evolution . :: According to Pedro Domingos, there are five tribes in Machine Learning, looking into different understanding of logic and reasoning and methodology: Symbolists, Bayesians, Connectionists, Evolutionaries and Analogizers. Each of them have their strength and weaknesses. Do we need to combine them to go the next step? . :: Source: Pedro Domingos, The Master Algorithm, 2015 . #artificialintelligence #machinelearning #ai #ml #data #dataprocessing #datascience #datascientist #fivetribes #symbolists #bayesians #connectionists #evolutionaries #analogizers #decisiontrees #naivebayes #markov #neuralnetworks #geneticprograms #supportvectors #datavisualization #statistics #analytics #dataanalytics

Machine Learning Model Support Vector Regression (SVR) . :: Support Vector Regression was one of the ML models that took my a while to fully understand. Not to confuse with Support Vector Machines (SVM), which is used for classification, while SVR and SVM have the same underlying algorithm, they are indeed to be differentiated. . :: In a nutshell: SVR is just like Linear Regression (SVR can also be used for non-linear models), but with a loss-function, basically penalising data points which lie outside of a certain threshold, or the so called soft margin, represented by the area in light red. The optimisations objective is to maximise the y distance of every data point to the regression function, represented as the red line. . :: Now, what is it good for? Well, to explain a specific use-case would probably be a bit too much, but think of SVR as a Linear Rregression Model, but with tolerance. (Please let me know if anything is unclear, there are a bunch of papers and research articles about the topic, but on a very high level, not very friendly to people new to the field) . #artificialintelligence #machinelearning #ai #ml #data #dataprocessing #datascience #datascientist #supportvectorregression #linear #linearregression #regression #datavisualization #statistics #analytics #dataanalytics

Machine Learning (ML) divided into: . :: Supervised Learning: :: Classification + Regression :: and Clustering models :: and their corresponding algorithms and methods . #data #datanalytics #datascience #datascientist #ai #ml #artificialintelligence #machinelearning #deeplearning #neuralnetworks #supervisedlearning #unsupervisedlearning #clustering #classification #regression

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