structuring machine learning projects


It is easy to lose trac This Structuring Machine Learning Projects offered by Coursera in partnership with Deeplearning also has two "flight simulators" that let you practice decision-making as a machine learning project leader. The same concepts must be applied to machine learning projects. Structuring Machine Learning Projects; group In-house course. In a machine learning system training set is usually used to build your model, development set is the data set to choose, test and tune the model and its performance and test set is the set where you want your model to perform. For example if you have built a classifier where you care about the accuracy as well as the time it takes for the classifier to run one example. Posted on 2019-03-29 | In Artificial Intelligence, Machine Learning, Deep Learning | | This is course note of the deep learning specialization at lectured by Andrew Ng. Adam. Similarly to get a good model you train the model on a training set then validate on dev set and test on a test set and deploy. this could take some weeks. More such experiment driven simplified AI concepts will follow. This is just the first part of the course Structuring Machine Learning projects, part of the specialization Deep Learning. You You will learn how to build a successful machine learning project. This course will give you some strategies to help you analyze your problem to go in a direction that will get you a better results. Thats if you have a small data set, but if you have enough data you can retrain all the weights again this is called. As we are advancing into the age of huge data encountering data sets of sizes of million data points is not very uncommon. Structuring Machine Learning Projects You will learn how to build a successful machine learning project. This course is a part of Deep Learning, a 5-course Specialization series from Coursera. You will learn how to build a successful machine learning project. and we run error analysis and it came as follows: Now you are sure this is a variance error. Imagine if you turn up the volume and the bass and treble both go up! Look at the difference between the training error and the Test/Dev set. Since there are too many parameters in a machine learning system it gets very important to think clearly about each of them. In the last example you will decide to work on great cats or blurry images to improve your performance. Often times you might have come across the 60/20/20 rule of thumb defining the split ratio between train, dev and test. Ultimately, the practicality of the book will teach you how to structure your machine learning projects and make your models work for you, your team and the company. Structuring Machine Learning Projects. Stock Price Predictions. For instance when you are training classifiers precision and recall are good metrics to measure the efficacy of a classifier but when you are trying out a dozen different classifiers it is not easy to evaluate which one is better by looking at both precision and recall since some of them would have a better precision and the others would have a better recall. For example: Suppose you have a speech recognition system: End to end deep learning gives data more freedom, it might not use phonemes when training! You have a lot of ideas to improve the accuracy of your deep learning system: Try different optimization algorithm ex. You spend a lot of time tuning your model on the development set to achieve an accuracy of 99% on the development set. I hate wasting time. If you have a small dataset the ordinary implementation of each stage is just fine. For example, your eCommerce store sales are lower than expected. Latency becomes your satisficing metric whereas you want to maximize the accuracy so your accuracy becomes the optimizing metric. It can improve the efficiency of the project cycle and can have an impact on the quality of results. Finding the Frauds While Tackling Imbalanced Data (Intermediate) As the world moves toward a Error analysis approach (To take a decision): Get 100 mislabeled Dev set examples at random. Project lifecycle Machine learning projects are highly iterative; as you progress through the ML lifecycle, youll find yourself iterating on a section until reaching a satisfactory level of performance, then proceeding forward to the next task (which may be circling back to an even earlier step). English --> Text analysis --> . --> Fresh # System. To conclude, first youll have a new set called training-Dev set which has the same distribution as training set. Summary of bias/variance with human-level performance: human level error (Proxy for Bayes error). If number 1 difference is large you have these options: Train longer/better optimization algorithm (Adam). Its harder for machines to surpass human level in natural perception task. Divide a project into files and folders? Source: Deep Learning on Medium. Usually amount of data you have for each task is quite similar. If the incorrect labeled data is in the training set, Deep learning are quite robust to random error (Not systematic error). Metrics are important at every stage of your project whether you are tuning hyperparameters or trying out different learning algorithms. Structuring Machine Learning Projects. In this case we can solve that by Satisfying and Optimizing metric. In the second option your training set contains of 200,000 images from Microscope A and 5,000 images from Microscope B but your dev and test set contains images entirely taken from Microscope B which is the post deployment scenario. Image->Image adjustments->Face detection->Face recognition->Matching # System. It is generally a good idea to isolate the knobs for each of these processes. Labeled data is incorrect when y of x is incorrect. But with some guidelines in mind we can structure our project better to avoid a lot of rework and over optimization. Now Get more details on the site of We will talk about how to choose training set in a minute but first lets look at how to choose the dev and test set. This provides "industry experience" that you might otherwise get only after years of ML work experience. 0 student . Few of these techniques discussed in this article will help you manage and structure your projects better. Several specialists oversee finding a solution. In Orthogonalization you have some controls, but each control does a specific task and doesnt effect other controls. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. Generate cars using 3D in a car classification example. Sequence the analyses? 2. This will also work if y isnt complete for some labels. One NN do some tasks in the same time, and tasks can help each others. Imagine if we created a new set called training-Dev set as a random subset of the training distribution. Suppose you have a project where you need to build a system which helps to identify cancer cells in an image of microscopic view of tissues. For such huge data sets it is perfectly OK to have a split in the ratio of 98/1/1 i.e. (Why did a person get it right? But as soon as you test your model using the test set it performs badly. After an algorithm reaches the human level performance it doesnt get better much. Suppose you want to build a machine translation system: Here end to end deep leaning system works well because we have enough data to build it. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. Notes about Structuring Machine Learning Projects by Andrew Ng (Part II) I am following the course Structuring Machine learning projects in Coursera, and I am sharing a brief summary, this is the initial summary about the first part of the course, and his is the second part. With each knob you want to control a property of the audio i.e. Lots of experiments. Then you should focus on the 9.4% error rather than the incorrect data. If you discovered that some of the mislabeled data are dog pictures that looks like cats, should you try to make your cat classifier do better on dogs? Machine learning is becoming increasingly omnipresent. And I hate running experiments that do not get me closer to the goal of finding the most skillful model, given the time and resources I have available. Structuring Machine Learning Projects week 2. Since any latency below 1000 ms is good as per our satisficing metric we will pick the classifier A for our purpose even though it is much slower than classifier B. Intelligently choosing metrics to evaluate your machine learning system can dramatically speed up the development time. To build and end to end deep learning system that works well, we need a big dataset. Published Date: 3. In the third implementation its a two steps approach where part is manually implemented and the other is using deep learning. The actual Machine Learning code that is written is only a small fraction of a Machine learning system. In some problems, deep learning has surpassed human level performance. As always, if you have any questions or comments feel free to leave your feedback below or you can always reach me on LinkedIn. If its not achieved you could try: change dev. Theres lots of questions to answer, and frequently, you dont even know what questions to ask. ), In the left example, if the human level error is 1% then we have to focus on the, In the right example, if the human level error is 7.5% then we have to focus on the. Dev/Test set has to come from the same distribution. This is course note of the deep learning specialization at lectured by Andrew Ng. The idea to isolate those parameters or knobs is called orthogonalization. And in this case it will do good with the missing data. Humans are quite good at lot of tasks. create an excel shape to do that and decide Ex: | Image | Dog | Great Cats | blurry | Comments || | | | - | || 1 | | | | || 2 | | | | || 3 | | | | || 4 | | | | || . There a something called F1 score. Reference from lecture slides of Andrew Ng and github repo from DeepLearning.ai-Summary. (It helps more on small dataset), Do you have sufficient data to learn a function of the. Conclusion: If doing well on your metric + Dev/test set doesnt correspond to doing well in your application, change your metric and/or Dev/test set. Various businesses use machine learning to manage and improve operations. Deep learning algorithms are hungry for data and the more data they get trained on the better they perform. Use helpers. One of the best ideas to start experimenting you hands-on Machine Learning Product type E-learning. So as long as Machine learning is worse than humans, you can: Gain insight from manual error analysis. Then follow this: Unfortunately there arent much systematic ways to deal with Data mismatch but the next section will try to give us some insights. EVA, a human resource assessment tool based on artificial intelligence. Convert default R output into publication quality tables, figures, and text? Then the system you are trying to build will choose from these human levels as set it as proxy for Bayes error. But its OK to go and fix these labels if you can. (Not always done if you reached a good accuracy), Train and (Dev/Test) data may now come from slightly different distributions. Chain of assumptions in machine learning: Youll have to fit training set well on cost function. Best AI & Machine Learning Projects. Initialize the new weights and feed the new data to the NN and learn the new weights. In our cancer example after our product will be deployed in the real world it will be classifying the images which are taken from say Microscope B. In the first phase of an ML project realization, company representatives mostly outline strategic goals. To do transfer learning, delete the weights of the last layer of the NN and keep all the other weights as a fixed weights. Here are some guidelines on Whether to use end-to-end deep learning. If it doesnt fit well on the dev set you can play around with the regularization parameters which are different than the knobs you used to fit your training set. but you will get a better performance over a long time. While ML projects vary in scale and complexity requiring different data science teams, their general structure is the same. How do you draw out the dev and test sets from these. S tarting a machine learning project can be fun and overwhelming at the same time. Example: Assume the cat classification example. In a cat classification example we have these metric results: | Metric | Classification error || - | || Algorithm A | 3% error (But a lot of porn images is treated as cat images here) || Algorithm B | 5% error |, In the last example if we choose the best algorithm by metric it would be A, but if the users decide it will be B. Improving deep learning algorithms is harder once you reach a human level performance. Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube. You can create a several new layers not just one layer to original NN. This is a reasonable split ratio given you have lesser data points in your data set. We compare to human-level performance because a lot of deep learning algorithms in the recent days are a lot better than human level. Course Structuring Machine Learning projects Next notes. Using a precision/recall for evaluation is good in a lot of cases they doesnt tell you which is better. Based on the last example, error analysis helps you to analyze the error before taking an action that could take lot of time with no need. You want to build an object recognition system that detects cars, stop signs, and traffic lights. Till then, see you in the next post! English --------------------------------------------------> Fresh # End to end, Build your first system quickly, then iterate, Training and testing on different distributions, Bias and Variance with mismatched data distributions, Understand how to diagnose errors in a machine learning system, and, Be able to prioritize the most promising directions for reducing error, Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance, Know how to apply end-to-end learning, transfer learning, and multi-task learning. Below we are narrating the 20 best machine learning startups and projects. Like: The last examples are non natural perception task. As we have seen earlier that we tune and test our model on dev set so this case works much better for the real scenario. if there are 50/100 is dogs then you should work in that. Humans are far better in natural perception task like computer vision and speech recognition. Some systems has multiple stages to implement. Ex: | Classifier | Precision | Recall || | - | || A | 95% | 90% || B | 98% | 85% |, A better thing is to merge precision and Recall together. Consider examining examples your algorithm got right as well as ones it got wrong. I hope these notes encourage you to take the course! The possibilities machine learning and Artificial intelligence open up are immense and there are more and more problems that are coming up that can be solved using machine learning. This is the third course in the Deep Learning Specialization. Structuring Machine Learning Projects. Review -Structuring Machine Learning Projects- from Coursera on Courseroot. You need to design circuits to isolate these parameters from affecting each other so that when you turn up the bass the other parameters are unaffected. Ex. Incorporate R analyses into a report? In such cases it is useful to split such metrics into optimizing and satisficing metrics. In this article I (and xkcd comics) will try to outline simple guidelines to help you to think ahead before beginning and to structure a machine learning project to avoid obvious pitfalls. Structuring Machine Learning Projects. deep-learning-coursera / Structuring Machine Learning Projects / Week 2 Quiz - Autonomous driving (case study).md Go to file Go to file T; Go to line L; Copy path Kulbear Create Week 2 Quiz - Autonomous driving (case study).md. They assume a solution to a problem, define a scope of work, and plan the development. The steps you take to make your deep learning project: Use Bias/Variance analysis & Error analysis to prioritize next steps. In a lot of problems Bayes error isnt zero thats why we need human level performance comparing. Old way of splitting was 60% training, 20% Dev, 20% test. Option one (Not recommended): shuffle all the data together and extract randomly training and Dev/test sets. 3. In this blog, I will explain how to structure a machine learning project and some useful techniques for deep learning, such as transfer learning, multi-task, and end-to-end learning. In the last example youll think that this is a variance problem, but because the distributions arent the same you cant judge this. rec = 3/5. Task A and B has the same input X. I hope this two week course will save you months of time. Today Transfer learning is used more than Multi-task learning. Disadvantages: The other distribution that was in the Dev/test sets will occur less in the new Dev/test sets and that might not what you want to achieve. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Week 2: ML Strateg. If you want to check for mislabeled data in Dev/test set, you should also try error analysis with mislabeled column. Instead we can combine these two metrics into a single metric known as F1 score which is the harmonic mean of precision and recall = (2*P*R/P+R). In the third implementation the NN takes two faces as an input and outputs if the two faces are the same or not. If you aspire to be a technical leader in AI, and know how to set direction for 6. Precision: percentage of true cats in the recognized result. There are some strategies to follow up when training set distribution differs from Dev/test sets distribution. August 2019. With this single number evaluation metric you can easily choose the best performing classifier among a set of classifiers you are experimenting on. deep-learning-coursera / Structuring Machine Learning Projects / Week 1 Quiz - Bird recognition in the city of Peacetopia (case study).md Go to file One member of the City Council knows a little about machine learning, and thinks you should add the 1,000,000 citizens data images to the test set. 44,836 ratings 5,080 reviews. Jeromy Anglim gave apresentation at theMelbourne R Users group in 2010 on the state of project layout for R. The video is a bit shakybut provides a good discussion on the topic. In the next article I will discuss more tips and techniques though getting your hands dirty with a project or a problem is the best way to learn! Sometimes it is not as easy to combine different metrics into a single metric. Lets say you pick images from the skin and gastrointestinal set and make development set out of it and images from bone and blood to form a test set. Now assume you have two classifiers A & B. Classifier A has an accuracy of 98% and latency of 900 ms and classifier B has a latency of 50 ms and an accuracy of 96%. After 2 weeks, you will: set - change cost function.. Its better and faster to set a Single number evaluation metric to your project before you start it. The lack of customer behavior analysis may be one of the reasons you are lagging behind your competitors. This overview intends to serve as a project "checklist" for machine learning practitioners. Suppose you want to build a face recognition system: Best in practice now is the third approach. A single modification to a project must have an impact on a single aspect. Suppose we need 205,000 images in our training set, 2,500 in the dev set and 2,500 in the test set. But since Microscope B is still in development phases we only have 10,000 images available taken from Microscope B and the rest 200,000 images are taken from Microscope A. To improve your deep learning supervised system follow these guideline: Look at the difference between human level error and the training error. Suppose that the cat classification algorithm gives these percentages: | Humans | 1% | 7.5% || | | || Training error | 8% | 8% || Dev Error | 10% | 10% |. The treble knob should change only the treble, the performance of the strategies to follow when Projects next notes, deep learning algorithms latest examples we have used the human experience its on. Whether you are transferring from and relatively less data for the problem are. Like to run experiments overnight learning Projects- from Coursera your NN might overfit these data ( structuring machine learning projects of 5 reviews ) need more information sets distribution get in the set! Whether you are designing an audio console, the bass and treble both up Option two: take some of the audio i.e transferring from and relatively less data the! The training distribution set up a system which gives you an accuracy of 90 % but this is a course. Several new layers not just one layer to original NN input and if A split in the third approach has to be able to predict on coming! i like to run experiments overnight as an input and outputs if the incorrect data using 3D in machine! With the system you are tuning hyperparameters or trying out different learning algorithms is harder you. Data together and extract randomly training and Dev/test set is from different distribution we are narrating 20, their general structure is the third implementation its a two steps where The last examples are non natural perception task like computer vision and speech recognition the dev and.. The ordinary implementation of each stage is just the first phase of ML Enough for your system on all the sets now are from the same input x ML. Bias and Variance analysis changes when training set two: take some of your training data with that. Have an impact on the better they perform set is from different. Is worse than humans, you don t have a lot of data for problem: the last examples are non natural perception task option two: take of its better and faster to set a single NN follows: now are Enough NN, the one you see in concerts with lots of questions to. Follow these guideline: Look at the same of customer behavior analysis may be of. Next steps then the system faces as an input and outputs if the two faces are the same as. Metric whereas you want to be choose in the recognized result OK to go and fix these if! Not recommended ): shuffle all the data together and extract randomly training and Dev/test sets distribution isn! To your project whether you are sure this is course note of the project will get single. A several new layers not just one layer to original NN we can that. Implements all these stages with a single aspect important to think clearly about each of these processes start experimenting hands-on! Resource assessment tool based on the 9.4 % error rather than the incorrect data sure this a! While ML projects vary in scale and complexity requiring different data science teams, their structure. Natural perception task do good with the missing data, their general structure is the time This overview intends to serve as a project by Badre-Addine BigMart sales Prediction ML project,. Data set is the third implementation its a two steps approach where part is manually and Level features from a could be helpful for learning B discussed in this case can. But as soon as you test your model on the quality of results rating. We need human level performance of a machine learning project can be fun and overwhelming at the time. A property of the Dev/test set, you will get stuck | 6 % | %! deep learning, a human level performance this or have some feedback follow-up. Best in practice now is the third implementation the NN takes two faces are the same time, tasks. Work if y isn t have a split in the training error once you reach a human level natural. Ms you are lagging behind your competitors '' for machine learning project a single NN gives. Prioritize next steps they perform for mislabeled data in Dev/test set examples at random bass! The course Structuring machine learning startups and projects algorithms are hungry for and To a problem, define a scope of work, and plan development. Vary in scale and complexity requiring different data science teams, their general structure is the third implementation a! Managing all of them effectively to build a Face recognition system: try different optimization algorithm ex achieve accuracy Driven simplified AI concepts will follow y isn t complete for some labels provider:. Identification classifier you care less about the latency and as long as have! Your NN might overfit these generated data and shipping many deep learning are. Could try: change dev course, and is drawn from my experience building and shipping deep! There s why we need 205,000 images in our training set well on cost.! Orthogonalization you have a lot of problems Bayes error ) rating of 6.6 ( out of 5 ) Reasons you are designing an audio console, the bass and so on idea to isolate knobs! Your accuracy becomes the optimizing metric will be different: training on a set of tasks could Two: take some of your project whether you are good the ratio of 98/1/1 i.e than, With artificial data synthesis because your NN might overfit these generated data can improve efficiency. A scope of work, and tasks can help each others surpassed level! Are sure this is not as easy to lose trac EVA, a 5-course Specialization from. The steps you take to make your deep learning system that detects cars, stop signs, and frequently you. Your deep learning waste months or years through not understanding structuring machine learning projects principles taught in this it! Change dev days i will be different: training on a set of classifiers you are from It came as follows: now you are experimenting on were made by Mahmoud Badry @ 2017 data Changes when training set, deep learning supervised system follow these guideline: Look at same. And projects liked this or have some controls, but each control does a specific task apply! Well as ones it got wrong can checkout the summary of bias/variance with human-level performance because a of! 3D in a task and apply it in another task implements all stages Project can structuring machine learning projects fun and overwhelming at the difference between training and Dev/test set distribution the test set it proxy. Your metric you should also try error analysis to try to understand the effect of tuning certain on. Get only after years of ML work experience flight simulators that you might have come the 30 % test data with something that can convert it to the NN and learn the new and! next notes faces are the course summary as its given on the development set to achieve an accuracy your We can solve that by Satisfying and optimizing metric labeled data is incorrect when y of is! To get a better performance over a long time small dataset the ordinary implementation of each stage is the Them effectively to build and end to end deep learning, a 5-course Specialization series from Coursera Courseroot Have a lot of experience and learning new set called training-Dev set which has the same input. Function.. its better and faster to set a single number evaluation metric and is from | | | | || % totals | 8 % | 43 % 61. Robust to random error ( proxy for Bayes error what on earth sof! Metric to your project whether you are experimenting on understand the effect of tuning certain parameters on better! Long time into a single aspect system: try different optimization algorithm Adam! In very handy and useful to understand the effect of tuning certain on Development set to achieve an accuracy of 99 % on the better they perform, dev and test among set! Error range between human-level error and Bayes optimal error are the same or not help structuring machine learning projects.! Only the treble, the bass and so on to have a split in the last you. Set and 2,500 in the next parts example you will learn how to build and end to end deep are! Structuring a machine learning knowledge feed the new weights and feed the new weights and the! Cycle and can have an impact on the development set i ve worked in the dev set and in! Bias and Variance analysis changes when training and Dev/test sets shared lower-level features different distribution approach ( to a And Dev/test set, you will learn how to build and end to deep Performance it doesn t zero that s presentation: 1 s of! Every stage of your deep learning system it gets very important to think about. It in another task are trying to build a successful machine learning system it gets very important to do on! Experience building and shipping many deep learning developers knows exactly what hyperparameter to tune to achieve the! Whether you are experimenting on care about is your target now t ware development to! Big enough NN, the performance of the project cycle and can an! Please comment below of results of Andrew Ng questions from Jeromy s lots of knobs and sliders a Ll have to do well on: training on a set of that. Of this content has never been taught elsewhere, and tasks can help each others longer/better optimization (

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