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Artificial Intelligence has existed for a long time and proven to be a disruptive force in the age of digital transformation. Due to a huge improvement in the field of AI, it along with its subfields, ML and DL, has become a buzzword in recent years.
AI is moving at an unprecedented rate. Isn’t it? However, with this velocity comes to some myths, especially in the mind of newcomers to this field, such as, the field is for hardcode programmers and not for the novice, or a person who is not good at math, can’t learn AI/ML. Well, such myths are really myths because, with the development of various toolkits and frameworks for contemporary AI workloads i.e. Machine Learning and Deep Learning, it became a friendlier IT field for newcomers too.
In this blog post, we will be looking at 5 top-quality toolkits along with their features, that are used for AI and the mantra to master them. Let’s dive in and explore the world of AI’s toolkits!
1. Scikit-Learn--“Machine Learning in Python”: If you are looking for a robust framework which can be used to bring ML into a production system then Scikit-Learn is the answer.
Pic Credit: https://scikit-learn.org
Initially released in 2007, Scikit-learn is an open source, powerful Python library developed for building ML models. The library is built upon other libraries such as NumPy, SciPy (Scientific Python), Matplotlib, etc., makes it very efficient for statistical modeling techniques such as Classification, Regression, Clustering, Dimensionality reduction, Model Selection as well as Preprocessing.
The deep focus of Scikit-learn library is on concerns such as ease of use, code quality, collaboration, and performance. Check it out. J.P. Morgan, Spotify, Inria, betaworks, Evernote and Huggin Face are some of the leading companies using Scikit-learn.
2. TensorFlow--“An end-to-end open source machine learning platform”: Once getting into the field of AI, most probably, one of the first toolkits, you'll hear about is, Google’s TensorFlow.
Initially released under the Apache 2.0 open-source license on November 9, 2015, TensorFlow was developed by the Google Brain team for internal Google use. TensorFlow is used for carrying out fast numerical computations using data flow graphs, where the nodes represent mathematical operations while the edges represent the multi-dimensional data array, called tensors, communicating between them. TensorFlow’s flexible architecture allows computation on any CPU or GPU, be it a desktop, a server, or even a mobile device.
Nominally available for Python programming language, TensorFlow is also available in C++, Haskell, Java, Go, Rust and most recently JavaScript. This toolkit is now used by several companies including Google, Intel, Coca-Cola, Uber, PayPal, eBay, Nvidia, SAP, and Twitter.
Amazing! Isn’t it?
3. Keras--“The Python Deep Learning toolkit”: Keras, a toolkit for building deep neural networks with Python, was initially developed as the as part of ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System) research project.
Pic Credit: https://keras.io/
Keras is known for its user-friendly API and built-in support for convolutional networks, recurrent networks as well as for the combination of both. One of the most important advantage of Keras is that it can be deployed on top of the other AI toolkit’s such as TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK), means any piece of code written in Keras can be run with any of these backends, without even changing a single line in the code. Very interesting! Even more interesting, via these backend tools, Keras can run seamlessly on CPUs as well as GPUs.
To get more insight, check the diagram below having deep-learning software and hardware stack.
Keras is now used by several companies including Apple, Cisco, Citi, and JPMorgan Chase.
4. PyTorch--“Simple yet powerful end-to-end deep learning framework”: Is there any python library that has the potential of changing the landscape in the field of deep learning? PyTorch is the answer!
Primarily developed by Facebook’s artificial-intelligence research group, PyTorch is an open-source Python-based library built to provide flexibility as a deep learning development platform. The workflow of PyTorch is very close to Python’s scientific computing library called NumPy.
Now, a question might arise in your mind, why would we use PyTorch to build deep learning models? I can list down some key features that might help answer that:
a) Imperative Programming: PyTorch uses imperative programming means we can perform computation as we typed the program. It also provides flexibility to the program as, for example, one can print out the values in the middle of the computation or can inject the loops into the computation flow itself.
b) Python Support: PyTorch is not just the Python binding on monolithic C++ framework rather it is built to be deeply integrated into Python. This is the reason; it can be used with important and popular Python libraries.
c) Dynamic computation graphs: PyTorch is ‘Define by Run’, means it provides us a framework to build computational graphs as we go. We can even change these computation graphs during runtime. It is quite valuable for the situations where we are not aware of the amount of memory it needed to create a neural network.
Simplified preprocessors, multi-GPU support, and custom data loaders are a few other useful advantages of PyTorch. Since its release in January 2016, many companies and universities including salesforce, Stanford University, and Udacity are using it.
5. Cafee--“The Python Deep Learning toolkit made with expression, speed, and modularity in mind”: Initially released under BSD 2-Clause license in 2017, the Cafee ( Convolutional Architecture for Fast Feature Embedding) project was initiated by Yangqing Jia as a part of his Ph.D. course at UC Berkeley and developed further by Berkeley AI research (BAIR) and community contributors. Having C++ as its core language, Cafee comes with a Python interface.
Pic Credit: https://dashbouquet.com
It’s expressive architecture, speed and community users make it perfect for research experiments as well as industry deployment. For example, Cafee can process over 60 million images per day with a single NVIDIA K40 GPU and we can download many successful models made by Cafee users from the Cafee Model Zoo. That is why, with Cafee, we can easily build a Convolutional Neural Network (CNN) for image classification.
For maximum performance, social media giants, Facebook and Pinterest also use Cafee.
So, you got an insight about top AI’s toolkits. But the question is still there, how to master AI’s toolkit? Just by downloading the toolkit on your machine can make you Master of It. No, certainly not!
We need to follow a basic strategy that comprises:
A. Learn theory and practical aspects both: You need to learn theory as well as practical aspect both to master AI’s toolkit. If you say I’ll learn only some APIs along with some programming and not the theory, you’ll be lacking for sure.
B. Gain good intuition on each concept: Not just theory, not just practical but it is also good to understand each concept intuitively. Moreover, it will help you to grasp the topic more easily.
C. Learning Python programming language: As we have seen most of the AI and ML frameworks are Python-based, hence learning Python programming language would an additional benefit. It is a general-purpose language and writing code in it is also quite easy as compared to other programming languages.
D.Practice, Practice, and Practice: The most important part of mastering anything is Practice. Isn’t it? Whatever the technique is, whether it is algebra, statistics, machine learning, or deep learning, apply and practice that technique in order to know where it works and where it not, is always good for learning.
Over above all, I believe AI and Machine learning is not at all difficult to learn if you are willing to put the efforts.
Pic Credit: https://www.beliefnet.com/
Without a doubt, the above strategy can lead you upfront in the field of AI. In addition to this, there are several tutorials, videos, and books to help you out. However, the random reading never stops, but it cannot provide you the required useful resources like Cloud & GPU-based labs, real-life capstone projects, and case-studies from industries for practicing, to master this cutting-edge technology. This is where the PG certificate courses like Artificial Intelligence & Machine Learning from Manipal Pro Learn helps. Integrated courses like these help you learn AI & ML - from fundamentals to deep dive and assist in building a strong portfolio that will help you get hired in any leading AI/ML company.
That’s all for now, readers! Hope this blog post proves to be insightful for you! We would love to hear your thoughts too. Don’t hesitate to leave your comments in the section below.
