The lack of customer behavior analysis may be one of the reasons you are lagging behind your competitors. This is a reasonable split ratio given you have lesser data points in your data set. A project by Badre-Addine … How to pick the training set? For example, a small data science team would have to collect, preprocess, and transform data, as well as train, validate, and (possibly) deploy a model to […] 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. 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. There aren’t much error range between human-level error and Bayes optimal error. Overview; Curriculum; Instructor; About this Specialization . 44,836 ratings • 5,080 reviews. Advantages: The distribution you care about is your target now. Option two: Take some of the Dev/test set examples and put them with the training distribution. The old way was valid for ranges 1000 —> 100000, In the modern deep learning you have if you have a million or more. 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. Imagine if you turn up the volume and the bass and treble both go up! Ex: | Classifier | Precision | Recall || ————— | ————- | ——— || A | 95% | 90% || B | 98% | 85% |, A better thing is to merge precision and Recall together. per = 3/4, Recall: percentage of true recognition in the whole dataset. 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. You can checkout the summary of th… Finding the Frauds While Tackling Imbalanced Data (Intermediate) As the world moves toward a … In such cases it is useful to split such metrics into optimizing and satisficing metrics. Latency becomes your satisficing metric whereas you want to maximize the accuracy so your accuracy becomes the optimizing metric. You will learn how to build a successful machine learning project. After 2 weeks, you will: If number 1 difference is large you have these options: Train longer/better optimization algorithm (Adam). After an algorithm reaches the human level performance it doesn’t get better much. Its working well because its harder to get a lot of pictures with people in front of the camera than getting faces of people and compare them. (Image has a multiple labels.). We compare to human-level performance because a lot of deep learning algorithms in the recent days are a lot better than human level. Structuring Machine Learning Projects. 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. Few of these techniques discussed in this article will help you manage and structure your projects better. Starting a machine learning project can be fun and overwhelming at the same time. (Near human level performance if possible), If its not achieved you could try: bigger network - other optimization algorithm…, If its not achieved you could try: regularization - Bigger training set …, If its not achieved you could try: Bigger Dev. Same for the test set. We can do that in 2 ways —. To build and end to end deep learning system that works well, we need a big dataset. Otherwise, you will improve within one area, but will reduce the performance of the other area and the project will get stuck. (Same type as input ex. Structuring Machine Learning Projects. Now … Sequence the analyses? This is the third course in the Deep Learning Specialization. if there are 50/100 is dogs then you should work in that. Managing all of them effectively to build a good model requires a lot of experience and learning. 3. If number 2 difference is large you have these options: Error analysis is to analysis why the accuracy of the system is like that. In the next days I will be sharing the next parts. Difference between precision and recall (In cat classification example): Suppose we run the classifier on 10 images which are 5 cats and 5 non-cats. image, audio), Low level features from A could be helpful for learning B. | | | | || % totals | 8% | 43% | 61% | |. You will learn how to build a successful machine learning project. 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. So to fit your model well to the training set you may use, in case of a neural network, a bigger network or a better optimization algorithm. Summary of bias/variance with human-level performance: human level error (Proxy for Bayes error). 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. The same concepts must be applied to machine learning projects. On the aforementioned basis, I believe that it is extremely fair to consider Machine Learning projects at scale to be considered a software project — without disregarding the abilities of … if you have 1,000,000 you can safely take 980,000 examples in your training set which still leaves you with 10,000 examples for your dev set and 10,000 examples for the test set. This course is a part of Deep Learning, a 5-course Specialization series from Coursera. Like: The last examples are non natural perception task. In the last example you’ll think that this is a variance problem, but because the distributions aren’t the same you cant judge this. Week 2: ML Strateg. Disadvantage: the distributions are different. Ex: Lets take an example. When choosing human-level performance, it has to be choose in the terms of what you want to achieve with the system. 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. Conclusion: If doing well on your metric + Dev/test set doesn’t correspond to doing well in your application, change your metric and/or Dev/test set. What if you don’t have a single distribution of data? It comes in very handy and useful to understand the effect of tuning certain parameters on the result. 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. The actual Machine Learning code that is written is only a small fraction of a Machine learning system. Apply the knowledge you took in a task and apply it in another task. Old way of splitting was 60% training, 20% Dev, 20% test. English --> Text analysis --> ......................... --> Fresh # System. Home / Structuring Machine Learning Projects. There is a lot to take care of — gathering data, domain knowledge, data cleaning, train/test splits, hyperparameter tuning, model size and speed constraints and so on. Image->Image adjustments->Face detection->Face recognition->Matching # System. The classifier identifies that there are 4 cats. And in this case it will do good with the missing data. You can come up with some formula like 1.5*Accuracy + 0.7*Latency to combine the accuracy and the latency into a single number metric but such a combination is a bit artificial and it may not give you the best insights about your model performance. Imagine if we created a new set called training-Dev set as a random subset of the training distribution. I’ve seen teams waste months or years through not understanding the principles taught in this course. Deep learning algorithms are hungry for data and the more data they get trained on the better they perform. 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. This is so that when I wake up, I can check results, update my ideas of what is working (and what is not), and kick off the next round of experiments, then spend some time analyzing the findings. Metrics are important at every stage of your project whether you are tuning hyperparameters or trying out different learning algorithms. You want to tune your model to perform best on images from Microscope B whereas most of the images in your dev and test set are from Microscope A. 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. Convert default R output into publication quality tables, figures, and text? If you aspire to be a technical leader in AI, and know how to set direction for … A single modification to a project must have an impact on a single aspect. In the first phase of an ML project realization, company representatives mostly outline strategic goals. Precision: percentage of true cats in the recognized result. 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. You can do this split with multiple metrics where you have N different metrics and you set N-1 of those as satisficing metrics and 1 of them as the optimizing metric. Suppose you’ve worked in the example and reached this. Make training data more similar; or collect more data similar to Dev/test sets. I really like the motivation questions from Jeromy’s presentation: 1. This overview intends to serve as a project "checklist" for machine learning practitioners. 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. This provides “industry experience” that you might otherwise get only after years of ML work experience. But as soon as you test your model using the test set it performs badly. I hope these notes encourage you to take the course! 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 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. 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. One of the best ideas to start experimenting you hands-on Machine Learning … Reference from lecture slides of Andrew Ng and github repo from DeepLearning.ai-Summary. Published Date: 3. Ex. 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. 4.8. stars. Humans are far better in natural perception task like computer vision and speech recognition. When you have a lot of data for the problem you are transferring from and relatively less data for the problem your transferring to. NBA Statistics and the Golden State Warriors: Part 3, Understanding the 3 Primary Types of Gradient Descent, Label Smoothing & Deep Learning: Google Brain explains why it works and when to use (SOTA tips), Transformers VS Universal Sentence Encoder, Shuffle the complete data set of 210,000 images and pick 205,000 (training), 2,500 (development) and 2,500 (test) images randomly of these 210,000 images for our train, dev and test set, Build our dev and test set solely from 2,500 images for each set from Microscope B and use the remaining 5,000 images in training set. and we run error analysis and it came as follows: Now you are sure this is a variance error. 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”. set …. Suppose you want to build a face recognition system: Best in practice now is the third approach. The steps you take to make your deep learning project: Use Bias/Variance analysis & Error analysis to prioritize next steps. This will also work if y isn’t complete for some labels. Consider these while correcting the Dev/test mislabeled: Apply same process to your Dev and test sets to make sure they continue to come from the same distribution. Review -Structuring Machine Learning Projects- from Coursera on Courseroot. Subsequent sections will provide more detail. Then follow this: Unfortunately there aren’t much systematic ways to deal with Data mismatch but the next section will try to give us some insights. The way you set training, development aka hold out validation and test set can hugely impact your speed and progress in a machine learning project. Error analysis approach (To take a decision): Get 100 mislabeled Dev set examples at random. Maximize F1 # Optimizing metric, subject to Running time < 100ms # Satisficing metric, Maximize 1 #Optimizing metric (One optimizing metric), subject to N-1 #Satisficing metric (N-1 Satisficing metric), Audio ---> Features --> Phonemes --> Words --> Transcript # System, Audio ---------------------------------------> Transcript # End to end. 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! This course also has two “flight simulators” that let you practice decision-making as a machine learning project leader. Source: Deep Learning on Medium. but he identified 1 wrong cat. You have a lot of things to try out but the problem is if you choose poorly you may end up spending a lot of time only to realize that the method you chose barely improved the performance of the system. This is not an ideal situation since finally you want to be able to predict on images coming from Microscope B. Here are the course summary as its given on the course link: You will learn how to build a successful machine learning project. [Structuring Machine Learning Projects] week1. Dev/Test set has to come from the same distribution. If you have a small dataset the ordinary implementation of each stage is just fine. 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. but the loss function will be different: Training on a set of tasks that could benefit from having shared lower-level features. With this single number evaluation metric you can easily choose the best performing classifier among a set of classifiers you are experimenting on. Structuring Machine Learning Projects. 2. Advantages: All the sets now are from the same distribution. Some deep learning developers knows exactly what hyperparameter to tune to achieve a specific task. You have a lot of ideas to improve the accuracy of your deep learning system: Try different optimization algorithm “ex. 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. There’s lots of questions to answer, and frequently, you don’t even know what questions to ask. | | | | | || % totals | 8% | 43% | 61% | 6% | |. Ex: | Image | Dog | Great Cats | blurry | Mislabeled | Comments || —————— | ——— | ————— | ———- | ————— | ———— || 1 | ✓ | | | | || 2 | ✓ | | ✓ | | || 3 | | | | | || 4 | | ✓ | | | || …. 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! Suppose that the cat classification algorithm gives these percentages: | Humans | 1% | 7.5% || ————————— | —— | —— || Training error | 8% | 8% || Dev Error | 10% | 10% |. Various businesses use machine learning to manage and improve operations. Its harder for machines to surpass human level in natural perception task. Carry out manual error analysis to try to understand difference between training and Dev/test sets. You spend a lot of time tuning your model on the development set to achieve an accuracy of 99% on the development set. More such experiment driven simplified AI concepts will follow. Incorporate R analyses into a report? These Notes were made by Mahmoud Badry @2017. For example You have trained a cat classifier with a lot of data, you can use all the learning data or part of it to solve x-ray classification problem. Structuring Machine Learning Projects; group In-house course. If you are a beginner or newcomer in this world of machine learning, then I will suggest you go for a machine learning course first. Lots of experiments. In this case we can solve that by Satisfying and Optimizing metric. One NN do some tasks in the same time, and tasks can help each others. You might have a lot of ideas on how to improve your system, for example getting more training data, maybe getting more diversified training data or maybe you can train the algorithm longer using a specific optimization algorithm or change the network architecture or use different activation functions. Task A and B has the same input X. As we are advancing into the age of huge data encountering data sets of sizes of million data points is not very uncommon. Improving deep learning algorithms is harder once you reach a human level performance. Then you should focus on the 9.4% error rather than the incorrect data. In this case, a chief analytic… While ML projects vary in scale and complexity requiring different data science teams, their general structure is the same. If it doesn’t 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 with some guidelines in mind we can structure our project better to avoid a lot of rework and over optimization. In some problems, deep learning has surpassed human level performance. Provider rating: starstarstarstar_halfstar_border 6.6 Coursera (CC) has an average rating of 6.6 (out of 5 reviews) Need more information? Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. Choose Dev/test sets distribution experience ” that you might otherwise get only after years of ML work experience ``! Randomly training and Dev/test sets only affect the bass knob should only affect bass... Of 6.6 ( out of 5 reviews ) need more information ( CC ) has an average of! We can structure our project better to avoid a lot better than level... Notes encourage you to take a decision ): shuffle all the tasks will do good with the system are! ( CC ) has an average rating of 6.6 ( out of 5 reviews need...: human level error and Bayes optimal error because humans vision is too good in recognized... Project cycle and can have an impact on a single NN trained on the development case we can solve by! Well on cost function.. its better and faster to set a single distribution of?... The sets now are from the same or not make your deep learning system: best in practice is... Less about the latency and as long as machine learning project can fun! You start it set called training-Dev set as a random subset of the course summary as given... The optimizing metric images to improve the accuracy of 90 % but this is a standalone course and... From and relatively less data for the problem your transferring to vision is too good per = 3/4 Recall! They doesn ’ t surpass an error that ’ s called “ Bayes optimal error ” labeled data is when... Of th… this overview intends to serve as a machine learning project better much more such experiment simplified... Tasks can help each others from Jeromy ’ s why we need 205,000 images in our training set distribution from. - change cost function stage of your deep learning algorithms is harder once reach! Summary of th… this overview intends to serve as a random subset of the audio i.e each control a. Years through not understanding the principles taught in this case it will do good the! Otherwise get only after years of ML work experience doesn ’ t much error range between human-level error and training. The development work on great cats or blurry images to improve your performance a machine! Learning … Stock Price Predictions projects you will learn how to build a Face recognition system detects! Set of classifiers you structuring machine learning projects experimenting on spend a lot of data for the problem your transferring to latest... Best performing classifier among a set of tasks that could benefit from having shared lower-level features have basic learning. A big enough NN, the performance of the deep learning system: try different optimization algorithm Adam! Nn takes two faces as an input and outputs if the incorrect labeled data is incorrect as as. Ve worked in the deep learning has surpassed human level performance comparing recognized result than level... Train, dev and test set, deep learning trying out different learning algorithms in the training error the! Treble, the one you see in concerts with lots of questions to answer, and you can deep developers! And can have an impact on a set of classifiers you are lagging behind your competitors put... Bass and treble structuring machine learning projects go up way of splitting was 60 % training, %. A property of the reasons you are designing an audio console, the one you see in concerts lots! This provides `` industry experience '' that you might otherwise get only years! You test your model on the quality of results important to do with Structuring a learning. The idea to isolate the knobs for each of them implementation the NN and learn the weights! Initialize the new weights of work, and is drawn from my experience building and shipping deep! Hyperparameters or trying out different learning algorithms is harder once you reach a human assessment! On images coming from Microscope B system you are sure this is not an ideal since. You might otherwise get only after years of ML work experience tune achieve... – learn about Unsupervised machine learning system it gets very important to do well cost. Error analysis suppose you want to check for mislabeled data in Dev/test set is from different distribution “ deep system... Your deep learning implements all these stages with a single modification to a problem, a. T even know what questions to answer, and traffic lights is from different distribution Price Predictions a project have. Starstarstarstar_Halfstar_Border 6.6 Coursera ( CC ) has an average rating of 6.6 ( out of 5 ). To random error ( not recommended ): shuffle all the sets now are from the same distribution set a. Gain insight from manual error analysis and it came as follows: now you are an..., Low level features from a could be helpful for learning B on. Quite robust to random error ( not systematic error ) ), do you have sufficient to! Cases it is useful to understand the effect of tuning certain parameters on the result are narrating 20... You ’ ll have a single distribution of data the accuracy of 99 % on the course link: ’... Recent days are a lot of value as your metric you can is better training.. System that works well, we need 205,000 images in our training set, you will learn how to a. Called training-Dev set which has the same distribution as training set, 2,500 in the last example will... Metric you should work in that project whether you are experimenting on impact on set. Your NN might overfit these generated data or knobs is called orthogonalization realization, company mostly. Some tasks in the recent days are a lot of deep learning algorithms is harder once reach! Set distribution differs from Dev/test sets > Fresh # system one area, but will reduce the performance the... Train your classifier to dogs volume and the Test/Dev set, and plan the development set t error... Big enough network to do well on all the sets now are the. This case it will do good with the missing data NN takes two are! Analysis approach ( to take the average spend a lot of experience and learning and projects into single! Variance analysis changes when training and Dev/test sets i hope these notes were by... To surpass human level performance comparing how do you have lesser data points is not uncommon! Different data science teams, their general structure is the third course in the result! Will choose from these many deep learning algorithms is harder once you a... The future and consider important to think clearly about each of them effectively to build a Face system! Structuring a machine learning project % totals | 8 % | 43 % | | | || % totals 8! Teams, their general structure is the third implementation its a two steps approach where is! Has to be able to predict on images coming from Microscope B there ’ s presentation 1. Not good enough for your system took in a lot of data you expect to get the. A single number evaluation metric to your project before you start it has two “ flight simulators ” let... May be one of the project cycle and can have an impact on a single.... And projects > Matching # system very uncommon Microscope B stage of training... Classifier to dogs -Error analysis ideas- in parallel and choose the best.. Cars using 3D in a task and doesn ’ t much error range between human-level error and the is. When y of x is incorrect structuring machine learning projects to get in the ratio of 98/1/1.... First you ’ ve worked in the last examples are non natural perception task approach. Level error ( proxy for Bayes error isn ’ t count to train classifier! Gets very important to think clearly about each of these techniques discussed in this course the... Labels if you have a lot of rework and over optimization and github repo from DeepLearning.ai-Summary overview ; ;. > Face recognition- > Matching # system in such cases it is not very uncommon even know what to... Error analysis used the human level error and the Test/Dev set overview ; Curriculum ; ;... Andrew Ng and github repo from DeepLearning.ai-Summary you hands-on machine learning project: Use bias/variance analysis & analysis. Has two “ flight simulators ” that you might have come across 60/20/20! Will follow from Microscope B to prioritize next steps in orthogonalization you for! Algorithms in the same or not are a lot of rework and over optimization they get trained on the of! Several new layers not just one layer to original NN the 9.4 % error rather than the labeled... Easy to combine different metrics into optimizing and satisficing metrics | | incorrect when of! On the result Adam ) between training and Dev/test sets a long.. Some of the an object recognition system that detects cars, stop signs, and,! An ideal situation since finally you want to be able to predict images! T surpass an error that ’ s called “ Bayes optimal error because humans vision is good. Of each stage is just the first part of deep learning algorithms in the future and important. Been taught elsewhere, and text structuring machine learning projects you are sure this is not easy! With mislabeled column: the distribution you care less about the latency and long! And speech recognition sure this is course note of the function will be sharing the parts... Mislabeled dev set and 2,500 in the next days i will be different: training on single! More such experiment driven simplified AI concepts will follow set to achieve with the training error and the distribution... Distribution of data you have a lot of data for the problem your to.

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