TensorFlow Intermediate: Recurrent Neural Networks

Name: TensorFlow Intermediate: Recurrent Neural Networks
Author: Josh Turner

Understand TensorFlow in deep learning with a focus on recurrent neural networks

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Course: On-Demand

Hello, my name is Josh Turner, in these lessons you will learn about reoccurant neural networks or RNNs. In this lesson I will show you a recap of neural networks, then we'll look at some TensorFlow basics. Neural networks are a type of machine learning algorithm inspired by the human brain. They consist of interconnected nodes called neurons organized in layers. Each neuron receives input, performs a computation, and passes the result to the next layer, until the final output is generated. Neural networks are capable of learning complex patterns and making predictions based on the input data. Here is a graphical representation of such a neural network. You can see that we have some nodes or neurons as our input layer. Those will be passed into other nodes which will make up the first hidden or connected layer. Calculations will be performed in each of these nodes and then passed through an activation function. Next will then be the input for the next layer. This process will be repeated until the final layer where an output is given and given a probability of which class you want to identify with. Okay, so that tells us a little bit of a recap of neural networks. Now let's get into some TensorFlow basics. Don't forget to open the Python environment of your choosing and pep install TensorFlow. In this lesson, we'll use the TensorFlow library. So the necessary libraries will just simply be to import TensorFlow as TF. Once you've done that, you can run this cell of code to make sure it works on your local machine. Okay, so far so good. TensorFlow is built from some basic objects called tensors. The basic idea of a tensor is to think of it as a multi-dimensional array or a matrix, depending on the use case for your problem. Tensors are the building blocks of all models and all algorithms in TensorFlow, so it's important that we understand how to create these. So let's create a couple of types of tensors. The first is the constant tensor. The values in the constant tensor will not change. Next is the variable tensor. The variable tensor may change in the process of going through the TensorFlow computational graph. That's the difference between the two tensors. Go down on the cell of code, make sure it works on your local machine. We know how to build tensors. The next thing we want to do is start building a computational graph. So the basic idea behind this is we want to build some kind of input that we can input tensors into and then connect layers one by one using the basic idea of the neural network so that we can create a neural network type computational graph. Let's see how this is done. So in order to do this, I want you to define a few variables. First we'll have an input size of whatever the size of your data is. I'm just using two here, and I'm also using that for an output size. Let's define an input shape, which will be some kind of tensor shape of your input size. In this case, I've left it blank, so it's just two by one in this case. Finally, we can use the TensorFlow cross library to define an input, and we'll define that to be our inputs. Run this cell of code, and you can start your computational graph. The next thing is we need to create our very first fully connected layer. We have our input, now let's create our first layer. In order to do that, create the following variable. Call your variable dense layer and set it equal to TF dot cross dot layers dot dense. We'll use units as our output size, and we'll use the relu activation function. The input for this layer will be our inputs. The next thing that we can do is create and connect it to another layer. So let's go ahead and do that now. Create a variable for your second layer that you'll call dense layer two, and that will be almost the exact same as dense layer one, but it will have as input dense layer one instead of the other inputs. Stay tuned for the next lesson where you'll learn about reoccurant neural networks. Thanks for watching.

Intermediate Provider : Josh Turner 5 Lessons · 27m in English

Course Description

In this intermediate-level TensorFlow course, we’ll cover deep learning and the applications of recurrent neural networks (RNNs). You’ll gain a comprehensive understanding of TensorFlow’s capabilities in handling sequential data. You’ll also preprocess time series and text data, build and train RNN models, and unlock the potential of deep learning for various domains.

The course begins by expanding your knowledge of data preprocessing techniques. You will explore specific preprocessing methods tailored to time series data and text data, learning how to handle different data formats and prepare them for modeling. Through hands-on exercises, you‘ll master the art of feature extraction, sequence padding, and encoding to ensure optimal data representation.

Next, you’ll learn the inner workings of RNNs, including concepts such as long short-term memory (LSTM) and gated recurrent units (GRUs). These architectures excel at capturing temporal dependencies in sequential data, and they have applications in various domains such as natural language processing, speech recognition, and time series forecasting.

In the final stages of the course, you will explore advanced topics in deep learning using TensorFlow. This includes techniques for model evaluation, including performance metrics specific to sequential data. We’ll discover strategies for model optimization and hyperparameter tuning to improve your model's accuracy and efficiency. You’ll gain insights into best practices for handling unbalanced or biased datasets, and learn how to address challenges specific to working with sequential data.

What You'll Learn

Apply preprocessing techniques for time series and text data in TensorFlow, including feature extraction, sequence padding, and encoding
Build and train recurrent neural network (RNN) models for sequential data
Explain the inner workings of RNN variants such as long short-term memory (LSTM) and gated recurrent units (GRUs)
Evaluate RNN models using performance metrics specific to sequential data
Optimize and fine-tune RNN models through hyperparameter tuning to improve accuracy and efficiency
Address challenges specific to sequential data, including handling long sequences and overfitting

Key Takeaways

Preprocessing is essential when handling sequential data, with methods tailored to time series and text data such as feature extraction, sequence padding, and encoding.
RNN architectures like LSTM and GRUs excel at capturing temporal dependencies in sequential data.
RNNs have applications across various domains, including natural language processing, speech recognition, and time series forecasting.
Model evaluation for sequential data relies on performance metrics specific to that data, alongside strategies for optimization and hyperparameter tuning.
Best practices include handling unbalanced or biased datasets and addressing challenges specific to working with sequential data.

Frequently Asked Questions

Who is this course for?

This is an intermediate-level TensorFlow course for learners who want to understand deep learning and the applications of recurrent neural networks (RNNs) for handling sequential data.

What topics does this course cover?

It covers data preprocessing for time series and text data, building and training RNN models, RNN variants including LSTM and GRUs, model evaluation and optimization, hyperparameter tuning, and handling long sequences and overfitting.

What skills will I gain from this course?

You will gain skills in Deep Learning, Deep Learning Methods, Keras (Neural Network Library), Recurrent Neural Network (RNN), TensorFlow, and Long Short-Term Memory (LSTM).

Does the course include hands-on practice?

Yes. Through hands-on exercises, you will master feature extraction, sequence padding, and encoding to ensure optimal data representation.

What lessons are included?

The course includes lessons on a recap of neural networks and TensorFlow basics, an introduction to recurrent neural networks, training and evaluating the RNN model, understanding RNN variants, and handling long sequences and overfitting.