Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models and TensorFlow is one of the newest and most comprehensive libraries for implementing deep learning. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. This course is your guide to exploring the possibilities with deep learning; it will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data.
With this video course, you will dig your teeth deeper into the hidden layers of abstraction using raw data. This course will offer you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. During the video course, you will come across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, high level interfaces, and more.
With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond.
About The Author
Dan Van Boxel is a Data Scientist and Machine Learning Engineer with over 10 years of experience. He is most well-known for "Dan Does Data," a YouTube livestream demonstrating the power and pitfalls of neural networks. He has developed and applied novel statistical models of machine learning to topics such as accounting for truck traffic on highways, travel time outlier detection, and other areas. Dan has also published research and presented findings at the Transportation Research Board and other academic journals.
TensorFlow is a new machine library that is probably not installed on our operating system by default. This video will guide you through installing TensorFlow locally or remotely.
Before we can use TensorFlow for deep learning, we need to understand how TensorFlow handles basic objects and operations. This video will walk you through a few computations.
Learning any library from documentation can be challenging, so we're going to build a practical machine learning classifier with TensorFlow. We'll start with a simple logistic regression classifier and build up from there.
Though we have a classifier, we need to compute weights so that our model is accurate. For this, we can use TensorFlow to specify and optimize a loss function. TensorFlow will then use this to find good weights.
Using single pixels as features limits us to model essentially linear phenomena. To model non-linear things such as font styles involving several pixels, we will use neural networks to transform our inputs into non-linear combinations for use in a logistic regression classifier.
Now that we understand neural networks, we can see how TensorFlow makes them easy to implement and train.
Now that we've trained a neural network, we should inspect it closely to understand the accuracy and weights.
A single hidden layer is good, but you may find the number of neurons growing prohibitive in order to model very complex features. To combine features more easily, we expand the network in depth rather than width. It's true deep learning with multiple hidden layers.
With our deep neural net trained, we should take time to check its accuracy and understand the features it's extracting.
Particularly in images, the features that we want to find can occur anywhere among the pixels. Convolutional neural nets allow us to train one set of weights to search small windows of an image for a feature.
Understanding the theory of convolutional layers is useless without learning the tools to actually use them. This video walks through a simple example with TensorFlow.
Convolutions can find a feature anywhere in an image, but with all the overlap, we need to make sure we don't find the same feature in the same place multiple times. A pooling layer reduces the size of our input, taking only relevant information.
Having learned how Max Pooling works in theory, it's time to put it into practice by adding it to our simple example in TensorFlow.
We've learned about convolutional layers and used them in an example, but now let's use them for real by adding a convolutional layer to the font classification model.
Convolutional layers often work well when chained together. Let's add another to our font classification model.
After our deep model has trained for a model, it's time to see how well it performs.
Some problems have time-based inputs. Features from the recent past might matter to the current prediction. To address these, researchers have developed recurrent neural networks. TensorFlow natively supports these.
TensorFlow models can be cumbersome to specify, yet follow a common pattern. skflow provides a simple interface for typical models.
RNNs can be hard to specify, but skflow will let us quickly build a model.
After learning so many methods and building all these models, it's helpful to look back and see how far we've come.
TensorFlow is changing very quickly and is being adopted by more researchers and professionals. But at its core are the contributions submitted by new users.
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