If you are aware of machine learning, then you must also have heard about Deep Learning. To completely understand Deep Learning, you must also learn about what it entails and its libraries. In the long list, the one name that stands out is TensorFlow. While a Deep Learning with TensorFlow training will better be able to provide you with the right knowledge, here is a blog to help you get an idea about what it is. Let us understand these two and how you can benefit by getting certified in this field.
What is Deep Learning?
A subset of machine learning (ML), Deep Learning (DL) incorporates building and training of neural networks for solving complex problems. Deep Learning, today, is considered to be a powerful technique to accomplish multiple applications such as speech recognition, natural language processing, and computer vision.
There are various high-level libraries, such as TensorFlow and Keras, which have led to Python becoming a popular programming language for DL.
If we are to talk about TensorFlow with Deep Learning, it has a layered architecture that supports an end-to-end approach. It consists of three layers, namely the output layer, hidden layers, and the input layer. A huge amount of data is needed to ensure the result given by DL algorithms is precise. Let's see what artificial neural networks are to understand DL in a better light.
Artificial neural networks, or ANNs, function in a manner very similar to our biological neural networks. Herein, a node is the same as a neuron, links are the same as the axon, and the acceptor algorithms of a perceptron are the same as the dendrites. As mentioned above, it is divided into three layers-
The various neurons present in this layer consume data from eligible external environments. The input layer neither interacts with the data nor performs any computation.
This is where all the processing happens. Often also called the deep neural network, it extracts features, makes decisions, extracts valuable information from the data, and even predicts future actions.
Once the processing completes, all the data is transferred from the hidden layers to the output layer, where computing is done. The processed information is then provided to the outside environment.
Why is TensorFlow so popular? What is the reason behind this library being used so extensively?
Here are a few points that have led to TensorFlow being such a popular library.
Related Article- Deep Learning Tutorial for Beginners
For a library to be as famous as TensorFlow is, it has to have various features. Here is a list of some of the top TensorFlow features that you must know about-
TensorFlow makes the use of multi-layered neural networks for accurately building complex applications. It is also being used for decision making, detecting anomalies, real-time object detection, audio manipulation, image processing, and video analysis.
Furthermore, TensorFlow offers structure and algorithms for implementing ML via decision trees and ANN to further compute humongous numerical datasets accurately.
Since we have already talked about the features of TensorFlow, it becomes clear why professionals prefer Deep Learning with TensorFlow. Let us look at the process of setting up TensorFlow for Deep Learning, after which we will move to a comparison of Keras vs TensorFlow vs PyTorch.
Once you become a professional, you will need to ensure that the system can deliver essential computing power for performing DL algorithms on a dataset. This entails having the following –
The steps for installation are:
Read Also: Top Deep Learning Interview Questions
| Basis | Keras | TensorFlow | PyTorch |
| Language | Python | Python, C++ & CUDA | Lua |
| Architecture | Readable & simple | Easily understandable | Complex |
| Datasets | Comparatively smaller | Large datasets with high performance | Large datasets |
| Speed | Slow speed offering low performance | Fast speed offering high performance | Fast speed offering high performance |
The field of Deep Learning will continue to evolve. And this is where your opportunity to shine arises. Once you step into the field of deep learning, you will have the chance to continuously learn, grow, and advance your career prospects. This endless adventure is for those who are open to always learning and becoming better at what they do.
Ans. Google has developed TensorFlow primarily for Deep Learning applications. While TensorFlow also supports traditional ML, it is primarily used for deep learning.
Ans. TensorFlow is widely used for developing models for different tasks such as handwriting recognition, image recognition, natural language processing, and various computational-oriented simulations.
Ans. Both TensorFlow and PyTorch are deep learning libraries. However, these two are different in many ways. TensorFlow provides better visualization than PyTorch. TensorFlow also stands out better at deploying pre-trained models to production.
Ans. TensorFlow is said to be difficult to both learn as well as use, mostly because of the high amount of programming skills involved.
Course Schedule
| Course Name | Batch Type | Details |
| TensorFlow Training | Every Weekday | View Details |
| TensorFlow Training | Every Weekend | View Details |
