Most 6 Technologies are Needed For Machine Learning
Due to the rapid technological boom in the IT and development sector a few years back, Machine Learning and Artificial Intelligence have become popular careers. Today’s industry is facing a shortage in qualified and skilled professionals due to the number of businesses that are launching and looking for top talent. A multitude of tech professionals have rushed in to fill this gap by learning all the technologies related to machine learning and AI, and adding them to their skill set.
This is limited to the most important learning languages and doesn’t break any new ground. Most people working in these industries now realize the importance of looking beyond these learning languages. These will determine the future. There is no one right way to know which technology to look out for, as everything is constantly changing and new frameworks are being created.
It is now clear that AI is quickly changing every aspect of our lives (think Siri) so there are some key AI technologies that you need to be aware of in order to take your machine learning projects to the next level. Below is a list of six top technologies that you can use.
Also read: Evolving Trends of Machine learning in 2021
1. Keras: Keras is an open-source software library that simplifies the creation of deep learning models. It can be used with many other AI technologies, such as TensorFlow and Theano. It works well on both GPUs and CPUs. Additionally, it is known for being user-friendly as well as for its speedy prototyping.
2. Torch: This machine learning library, which was released in 2002, is one of the most enduring. It has many algorithms that can be used for deep learning. Open-source frameworks provide the fastest and most flexible platform, without any complexity.
3. Caffe: Caffe is one of the most recent options. The best thing about Caffe, aside from its expressive architecture and vibrant community, is the degree of innovation it encourages. This machine learning framework is primarily focused on speed, expressiveness, and modularity.
4. TensorFlow: This open-source machine learning framework has been used across many platforms since its initial release in 2015. It was initially created by Google, but it is now used extensively by all major tech companies like eBay, Dropbox, and Intel. Using flowgraphs, it is possible to create neural networks.
5. Theano: This Python library is an open-source Python library that can be used to create various machine learning models. It is considered an industry-standard because it is one of the most well-known libraries. It makes it easier to optimize, define and assess mathematical expressions.
6. Microsoft Cognitive Toolkit: This is an AI solution you can use to elevate your machine learning projects. Certain studies have shown that open-source frameworks can be used to train algorithms to behave like the human brain.
It is important to remember that while building a machine-learning application is one thing; selecting the right technology from all the options available is quite another. It’s not an easy task and it is important to evaluate many options before choosing the best one.
Learning how machine learning technologies interact with one another is a crucial component of your decision-making process. It will help you to stay ahead of your peers, but it is also important.