Deep Learning Basics: Introduction and Overview with TensorFlow
TensorFlow is a highly capable deep learning framework that allows you to train various Deep Learning (DL) models for all kinds of computer tasks such as object recognition, detection and tracking, image classification and speech detection. This introduction will explain the basics of TensorFlow and why it is a great solution for the development of deep learning models.
What Is TensorFlow?
Developed by Google, Tensorflow is an open-source AI library for machine and deep learning designed to implement, train, test and deploy Artificial Neural Networks (ANNs). Even though TensorFlow was initially developed for internal use for engineers and researchers at Google, it was publicly released in 2015.
TensorFlow supports multiple coding languages including C, C++, Python, Java, JavaScript, and Swift. While TensorFlow is considered one of the most complex frameworks to learn, the TensorFlow site provides many tutorials to help beginners and enthusiasts start their journey on the framework.
The framework gets its name from its main features. TensorFlow uses tensors, which are multidimensional arrays as inputs. With TensorFlow, you can set a flowchart of operations or simply “Graphs”, you wish to perform on the input you fed to the system. The process begins when you feed the input, which passes through the system based on the instructions of the graphs and ends when the system generates an output.
How to Use TensorFlow for Deep Learning
The idea behind deep learning artificial neural networks, which extract features and attributes from raw data, is to mimic the connectivity and functionality of the neurons within the brain. Convolutional Neural Network, is a type of ANN commonly trained and used on TensorFlow to achieve various tasks that involve adding AI functionalities to computer hardware and software.
What makes convolutional neural networks ideal for these types of tasks is their architecture. A CNN is considered deep learning because it is comprised of multiple layers as opposed to simpler neural structures built with fewer layers. In fact, the depth of the network is determined by how many layers comprise its architecture, with some networks like ResNet having more than 150 layers.
With TensorFlow, developers can create a dataflow graph to describe the way in which the data is moving through a set of processing nodes or a graph. Each node within a graph is a representation of mathematical operation and each edge or connection of nodes represents a multidimensional data array known as a tensor.
TensorFlow allows developers to code with Python, which is both easy to learn and highly capable coding language. The tensors and nodes of TensorFlow are Python objects and TensorFlow applications are also Python applications.
However, since the transformation libraries used in TensorFlow are written in high-performance C++ binaries, the true mathematical operations are not done in Python. Instead, Python is used to direct traffic between various pieces and provide programming abstractions to combine them.
One of the reasons for the popularity of TensorFlow is the variety of platforms TensorFlow based applications can run on. Ranging from local machines to clusters, cloud and even smartphones operating systems like Android and iOs. Google gives TensorFlow developers that use its cloud service an important feature, the ability to use custom TensorFlow Processing Units (TPUs) for further optimization of the framework.
Optimizing TensorFlow for Deep Learning
Due to its nature, deep learning is far more complex than other subtypes of AI such as Machine Learning (ML). Consequently, even though it is more capable, deep learning tasks are much typically much more intensive, and harder to manage and track with many hyperparameters that require attention.
Thus, deep learning models have much higher requirements to run properly compared to machine learning algorithms. This has kept DL out of reach for many private users and organizations for many years.
However, as you can see from the graph above, the popularity of deep learning has soared in recent years. This surge in popularity has much to do with the launch of more powerful and cheaper computer processors.
The most important of these is the Graphics Processing Unit (GPU). GPUs were originally designed for 3D modeling and computer games, but their architecture makes them an excellent resource for training and running models on deep learning frameworks such as TensorFlow and PyTorch.
Even with the recent developments in the GPU industry and the advantages of TPU in TensorFlow, training and running deep learning models can still be expensive and time-consuming.
To make the most of your deep learning model with TensorFlow, you can use deep learning platforms like MissingLink. With MissingLink, you can automate and track your experiments across multiple machines within a simple web page so you can save precious time and reduce your expenses.
TensorFlow Benefits for Deep Learning
Abstraction is the most important benefit for deep learning that TensorFlow provides. TensorFlow manages most of the workflow and details behind the scenes, so you can focus on improving the application as a whole instead of having to deal with small details.
Another benefit of TensorFlow for developers is the ability to use eager execution mode which lets you evaluate and modify each of your graph operations so you can debug and gain important knowledge on your application with ease. For the same purpose, TensorFlow offers the TensorBoard, a visualization tool that lets you audit and chart how your graphs operate within a web-based interactive dashboard
Last but not least, since TensorFlow is developed by Google, you get access to many other nifty features also developed by Google which improves the overall experience of using TensorFlow. For example, besides the TPU, which accelerates performance, you can also use Google cloud offering the Google Cloud Platform (GCP) to share models you created with TensorFlow and interact with many other Google services for added efficiency.
Wrap Up
TensorFlow is a highly capable deep learning framework that eases the learning curve of new developers and enthusiasts with many tutorials. If you find that running and training deep learning models on the framework highly intensive, you should consider using a deep learning platform and maybe upgrade your great. Simply put, if you want to train your own deep learning model, TensorFlow is the best framework to choose from.
