Best Deep Learning applications and software

Deep learning translates as deep learning and is a type of artificial intelligence (AI) that is encompassed within machine learning and has proven to be very effective in pattern recognition, data classification and decision making. 

It trains a system to perform tasks as any human being can perform them. Instead of organizing data to run through predefined rules, deep learning defines basic parameters from the data.

At first glance, it may seem that the Deep Learning and that the Machine Learning are the same, but they are not.

Differences between Deep Learning and Machine Learning

Deep learning and machine learning are subfields within the broader field of artificial intelligence (AI). They are both used to train computer systems to perform specific tasks, but there are some differences between them, as we can see in this article.

Autonomy is the major difference between these two types of AI. Machine learning uses algorithms to analyze data, learn from that data and make decisions based on what it learns, while deep learning - which is part of the former - uses artificial neural networks with several layers of neurons, which makes them more complex but also allows them to learn and make decisions on their own.

Machine learning is mainly used for tasks involving the analysis and classification of structured data, while deep learning is used for more complex tasks, such as natural language processing or computer vision.

Today, there are numerous deep learning applications and software that can help researchers, developers and companies take advantage of this technology.

Differences between DL and ML

Regarding the structure of the algorithms

• Machine Learning: ML algorithms seek to extract patterns and relationships from input data. ML models are created through often supervised training, with labeled data sets or those for which the correct output or solution is known, and are often used to make predictions or classifications from the newly known data.
• Deep Learning: DL algorithms are based on deep artificial neural networks. These networks are composed of multiple layers of interconnected nodes (neurons) that process information in cascade. Unlike traditional ML, DL can learn directly from data without the need to have manually selected features; although they do need to have labeled datasets, it should not be forgotten that their training is based on supervised learning (that of which the actual output or correct solution is known).

Regarding data representation

• Machine Learning: In ML, data is represented by a set of manually selected or automatically extracted features. These features are used as inputs to machine learning models, and their choice and quality affect model performance.
• Deep Learning: In DL, data is represented directly as raw information (e.g., pixel images, text streams, or audios). Deep neural networks have the ability to automatically learn high-level features and representations as they are trained on large amounts of data.

In terms of scalability

• Machine Learning: ML can perform well with data sets of moderate size and medium complexity. However, as problem complexity or dataset size increases, ML performance and scalability may be limited.
• Deep Learning: DL has proven to be highly scalable and effective on problems with large datasets and high complexity. Deep neural networks can automatically learn complex features and representations, making them particularly suitable for applications such as image recognition, natural language processing and autonomous driving.

Best Software for Deep Learning Implementation

Here are some of the best tools for creating software based on deep learning:

TensorFlow

TensorFlow is an open source library for deep learning developed by Google. It is very popular due to its ease of use, flexibility and scalability. It can run on a variety of platforms, including mobile devices and distributed systems.

PyTorch

PyTorch is another popular deep learning framework. It is known for its ease of use and its ability to rapidly prototype. It is also very flexible and can run on a variety of platforms.

Keras

Keras runs on TensorFlow. It is very easy to use and is designed to facilitate the creation and training of deep learning models. Keras also supports a variety of neural network architectures.

Caffe

Caffe developed by the Berkeley Vision and Learning Center research team. Known for its speed and efficiency, it is ideal for real-time applications and mobile devices. It has established itself as one of the best deep learning software, and thanks to its ease of use and seamless integration with popular programming languages, it has become the preferred choice for both researchers and developers in the field.

Theano

Theano is developed by the University of Montreal and is known for its efficiency and ability to optimize the computation of mathematical operations necessary for training deep learning models.

Torch

This software has been developed by Facebook and other partners. Torch is known for its ease of use and its ability to rapidly prototype. Torch is also very flexible and can run on a variety of platforms.

It is necessary to take into account the needs to be covered and the type of activity to be performed before choosing a software or libraries to help us implement algorithms based on deep learning, so it is necessary to contact qualified professionals with the necessary experience and training.

At Gamco, there will always be professionals ready to offer help and information as well as to provide our Artificial Intelligence solutions adapted to the needs to be covered at all times.