Thomas Henson

Tips & Tricks for Studying Machine Learning Projects

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How to Study Machine Learning Through Projects

Studying Machine Learning can seem overwhelming! Over our careers as developer or technologist we are constantly having to learning new skills. Whether you are  Database Administrator who needs to learn about Hadoop or Web Developer looking to learn JavaScript. Change is enviable and the way to change is through learning.  In fact many developers in the community advocate for making learning a daily or weekly habit of 1 – 2 hours every week. In today’s episode of Big Data Big Questions we explore my tips and tricks for learning Machine Learning (ML) or any other new technology.

Studying Machine Learning

Tips and Ticks for Studying Machine Learning

Make sure to watch the full video where I break down my tips and tricks for learning Machine Learning.

 

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5 Things Every Data Team Should Know About Transfer Learning

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Did you know there is a technique in Deep Learning (DL) that doesn’t require large data sets and extremely long training times? It’s called Transfer Learning and the fact if you have done any “Hello World” image detection examples or followed my Tensorflow TFLeanring Course you have already used. Data Teams with Data Engineers and Data Scientist should know Transfer Learning. Let us jump into understanding Transfer Learning.

What is Transfer Learning

Transfer Learning is a Machine Learning (ML) technique that focusing on storing knowledge gained from one problem and applying to another related problem. Data Scientist start by building one model then use that same model as the starting point for a new model. Typically the secondary model is a related problem but not always. For example, let us take a model that was built by our friend Dwight to detect images of bears. Now Dwight has used that model to try and figure out how to identify the best bear. Part of that model does image detection it identifies a bear.

Dwight can now share his model with his best friend Jim who wants to build a model to detect dogs. Since the model that Dwight has already been pre-trained Jim can reduce his time in training.

Transfer Learning Bears

Transfer Learning speeds up time to results (does not guarantee results😊)

The second thing you need to know about Transfer Learning speeds up time to results. Think of Transfer Learning as a framework in programing languages. When I was a Web Developer in .NET community, I could build features within my Web Application quicker using .NET functions already built in. For example, connecting to a SQL data could be done using a built-in function called ConnectionString. The complicated details of building that connection to SQL server was abstracted away from me.

Using Transfer learning Data Teams are not starting from scratch which allows models to be built and trained faster. Just as frameworks allow to abstract away complexity, Transfer Learning is similar in that developers can focus on solving higher level problems. In our Bear detector example our friend Dwight has already done the hard work for building an image detector. Now Jim can change a few lines of code and build a new model.

Transfer Learning for Data Reduction

When we think of Deep Learning large data sets are what comes to mind. Transfer Learning allows Data Scientist to use smaller data sets to train models. By utilizing models already built for one task the model can then be retooled to solve a different problem. In our previous example of an image detector for Bears. How much data would need to be applied to create a new model to identify dogs? How about the Jetson Nano thumbs up or down project?

One area being impacted by Transfer Learning is Healthcare. Pretrained models are huge in helping with Healthcare models. For example, let us say there is a specific lung image detection model that is trained 80% of the way this is called a pretrained model. Data Teams can use this model to apply to their problem to take it the remaining 20% of way to train. Imagine one model trained to detect scar tissue can be used to detect other complex lung issues like Pneumothorax, Cancer, COPD, and more.

Transfer Learning

Most Computer Vision Already Incorporates Transfer Learning

For many reasons we have already discussed Object detection incorporated Transfer Learning. Edge detections is already designed.  An Edge is the sharp contrast in a image. For example, the below is a photo of a Jim from the Office, notice where his brown tie meets his yellow shirt? This would be an edge. Tensorflow and other Deep Learning Frameworks come with functions ready to do object detection. Those function already incorporate models that can detect edges in images.

Transfer Learning

One example is in the Jetson Nano Getting Started Project where you can build a model to detect Thumbs Up or Thumbs Down. Out of the box we just use the pretrained model and add our data. For this model we are adding our own images of thumbs up and thumbs down. Using Transfer Learning allows for Jetson Nano users to quick build an image detection with minimal coding and data.

NVIDIA has a Transfer Learning Toolkit in it is 2nd Generation

We all know here at Big Data Big Questions we love the NVIDIA team. Well at NVIDIA’s GPU Cloud or NGC they have catalog of Deep Learning frameworks like we have just talked about. Whether you are looking to train a model for healthcare with their Clara Framework or Natural Language Processing (NLP) with BERT. Many of these models come pretrained to apply your data to solve your problem. Here is the NVIDIA official statement on the NVIDIA Transfer Learning Toolkit:

To enable faster and accurate AI training, NVIDIA just released highly accurate, purpose-built, pretrained models with the NVIDIA Transfer Learning Toolkit (TLT) 2.0. You can use these custom models as the starting point to train with a smaller dataset and reduce training time significantly. These purpose-built AI models can either be used as-is, if the classes of objects match your requirements and the accuracy on your dataset is adequate, or easily adapted to similar domains or use cases.

By using NVIDIA’s TLT 2.0 data teams can reduce development by up to 10X. Even cutting development times in half is a huge game changer for A.I. development.

Wrapping Up Transfer Learning

Transfer Learning is a powerful technique within Deep Learning for helping put models into production faster and with smaller data sets. The key application of Transfer Learning is building off previous training just like we do as humans. The first time I learned to program with Java was hard! Object-Oriented programing was new to me. However, over time I got better, then when I switched to C# for it was a lot easier to take in the concepts and learn. See I was building off my previous training in Java to learn C#.

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Deep Learning Python vs. Java

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What About Java in Deep Learning?

Years ago when I left Java in the rear view of my career, I never imagined someone would ask me if they could use Java over Python. Just kidding Java you know it’s only a joke and you will always have a special place in my heart. A place in my heart that probably won’t run because I have the wrong version of the JDK installed. 

Python is king of the Machine Learning (ML) and Deep Learning (DL) workflow. Most of the popular ML libraries are in Python but are there Java offerings? How about in Deep Learning can you use Java? The answer is yes you can! Find the differences between Machine Learning and Deep Learning libraries in Java and Python in the video.

Transcript

Hi, folks. Thomas Henson here, with thomashenson.com, and today is another episode of Big Data Big Questions. Today’s question comes in around deep learning frameworks in Java, not Python. So, find out about how you can use Java instead of Python for deep learning frameworks. We’ve talked about it here on this channel, around using neural networks and being able to train models, but let’s find out what we can do with Java in deep learning.

Today’s episode comes in and we’re talking about deep learning frameworks that use Java, not Python. So, today the question is, “Are there specific deep learning frameworks that use Java, not Python?” First off, let’s talk a little bit about deep learning, do a recap. Deep learning, if you remember, is the use of neural networks whenever we’re trying to solve a problem. We see it a lot in multimedia, right, like, we see image detection. Does this image contain a cat or not contain a cat?

The deep learning approach is to take those images [Inaudible 00:01:10] you know, if we’re talking about supervised, so take those labeled images, so of a cat, not of a cat, feed those into your neural network, and let it decide what those features are. At the end you get a model that’s going to tell you, is this a cat or is this not a cat? Within some confidence. Hopefully not 50%, maybe closer to 99 or 97. But, that’s the deep learning approach versus the machine learning approach that we’ve seen a good bit.

We talk about Hadoop and traditional analytics from that perspective is in machine learning we’re probably going to use some kind of algorithm like singular value decomposition, or PCI, and we’re going to take these images and we’re going to look at each one and we’re going to define each feature, from the cat’s ears to the cat’s nose, and we’re going to feed that through the model and it’s going to give us some kind of confidence. While the deep learning approach we get to use a neural network, it defines some of those features, helps us out a lot. It’s not magic, but it is a little bit, so really, really innovative approach.

So, the popular languages, and what we’ve talked most about on this channel and probably other channels and most of the examples you’ve seen are all around Python, right? I did do a video before where I was wrong on C++. There was more C++ in deep learning than I really originally thought. You can check that video out, where we kind of go through and talk about that and I come in and say, “Hey, sorry. I missed the boat on that one.” But, the most popular language, one… I mean, I did a Pluralsight video on it, Take CTRL of Your Career, around TensorFlow and using TFLearn. TensorFlow is probably far and away the most popular one. You’ve seen it with stats that are out there. Also PyTorch, Caffe2, MXNet, and then some other, higher-level languages where Keras is able to use some of TensorFlow and be a higher-level abstraction, but most of those are going to use Python and then some of them have C++. Most examples that you’re going to see out there, just from my experience and just working in the community, is Python. Most people are looking for those Python examples.

But, on this channel, we’ve talked a lot about options and Hadoop for non-Java developers, but this is an opportunity where all you Java developers out there, you’re looking for, “Hey, we want to get into the deep learning framework. We don’t want to have to code everything ourselves. Are there some things that we can attach onto?” And the answer is yes, there are. It’s not as popular as Python right now, or R and C++ in the deep learning frameworks, but there is a framework called Deeplearning4j that is a Java-based framework. The Java-based framework is going to allow for you to use Java. You could still use Python, though. Even with the framework, you can abstract away and do Python, but if you’re specifically a Java developer and looking to… I mean, maybe you want to get in and contribute to the Deeplearning4j community and be able to take it from that perspective, or you’re just wanting to be able to implement it in some projects. Maybe you’re like, “Hey, you know what? I’m a Java developer. I want to continue doing Java.” Java’s been around since ’95, right? So, you want to jump into that? Then Deeplearning4j is the one for you.

So, really, maybe think about why would you want to use a Java-based deep learning framework, for people that maybe aren’t familiar with Java or don’t have it. One of the things is it claims to be a little bit more efficient, so it’s going to be more efficient than using an abstraction layer from that perspective in Python. But also, there’s a ton of Java developers out there, you know, there’s a community. Talked about how it’s been around since ’95, so there’s an opportunity out there to tap into a lot of developers that have the skills to be able to use it and so, there’s a growing need, right? There’s communities all around the globe and different little subsets and little subareas. Java’s one of those.

I mean, if you look at what we did from a Hadoop perspective, so many people that were Java developers moved to that community, also a lot of people that didn’t really do Java. It’s a lot like, like I said, at the point I was at in my career, I was more of a .NET C# developer. Fast forward to getting into the Hadoop community, went back to my roots as a Java, so I’d done some Java in the past, and went through that phase. And so, for somebody like me, maybe I would want to go back out. I don’t know. I’ve kind of gone through more Python, but a lot of different options out there. Just being able to give Java developers a platform to be able to get involved in deep learning, like, deep learning is very popular.

So, those are some of the reasons that you might want to go, but the question is, when you think about it, so if I’m not a Java developer, or what would you recommend? Would you recommend maybe not learn TensorFlow and go into Deeplearning4j? You know, I think that one’s going to depend… I mean, we say it a lot in here. It’s going to depend on what you’re using in your organization and what your skill set is. If you’re mostly a Python person, my recommendation would be continue on or jump into the TensorFlow area. But if you’re working on a project that is using Deeplearning4j then by all means go down that path and learn more about it. If you’re a Java developer and you want to get into it, you don’t want to transition skills or you’re just looking to be able to test something out and play with it, and you don’t want to have to write it in Python, you want to be able to do it in Java, yeah, use that.

These are all just tools. We’re not going to get transfixed on any tool. We’re not going to go all in and say, “You know what? I’m only going to be a Java developer,” or, “I’m only going to be this.” We’re going to be able to transition our skills and there’s always going to be options out there to do it. And in these frameworks too, right? Deeplearning4j is awesome, but maybe there’s another one that’s coming up that people would want to jump into, so like I said, don’t get so transfixed with certain frameworks. Like, Hadoop was awesome. We broke it apart. A lot of people navigated to Spark and still use HDFS as a base. There’s always kind of skills that you can go to, but if you go in and say, “Hey, I’m only going to ever do MapReduce and it’s always going to be in Java,” then you’re going to have some challenges throughout your career. That’s not just in data engineering, that’s throughout all IT. Heck, probably throughout all careers. Just be able to be flexible for it.

So, if you’re a Java developer, if you’re looking to test some things out, definitely jump into it. If you don’t have any Java skills and it’s not something that you’re particularly wanting to do, then I don’t recommend you running in and trying to learn Java just for this. If you’re doing Python, steady on with TensorFlow, or PyTorch, or Caffe, whatever you’re using.

So, until next time. See you again on Big Data Big Questions. Make sure you subscribe and ring that bell so you never miss an episode. If you have any questions, put them in the comment section here below. Thanks again.

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