A Simple Checklist to a lucrative path of becoming an AI Engineer ( Deep Learning)

Deep learning is a growing field of artificial intelligence (AI). It can provide opportunities for a challenging and lucrative career. Especially, if you are interested in computer vision challenges like image recognition, it can provide you the skills you need. The following tools will help you get started.

Basic Tools

Deep learning is a complex field that requires a solid background in mathematics and programming. You need a good understanding of Calculus, Probability, Linear Algebra, and Statistics.

In order to work with different frameworks, you also need programming skills and an understanding of computer algorithms. You will also need to learn Python, sk-learn

To get started, you also need to understand the core concepts of artificial intelligence, machine learning, and deep learning. Andrew Ng offers his popular course on Coursera

Frameworks and Libraries

There are a lot of frameworks and libraries available that can help you master the various aspects of deep learning. Below are some popular deep learning tools that can get you started:

TensorFlow

TensorFlow is an open source library that was developed by the Google Brain team. The library is built-on Python. It also works with programming languages like C++ and R. It has a flexible architecture that allows you to deploy computations across multiple CPUs and GPUs. You can achieve complex numerical computations using data flow graphs.

MXNet

MXNet is a deep learning library developed with the help of industry giants like Amazon Web Services (AWS), Baidu, Carnegie Mellon University, Dato, Intel, NVIDIA and more. It is built on C++ and CUDA. However, it supports Python, R, Julia, Go and JavaScript. MXNet is designed to accelerate numerical computations for neural networks. You can use its automated workflows to represent standard neural networks with few lines of code.

Theano

Theano is a Python-based deep learning library that is tightly integrated with the scientific computing Python package NumPy. Its fast data-intensive computation on the GPU is transparent. It is developed by academics and it is approachable in a classroom setting. So it has gained popularity at Universities as an introductory tool for deep learning.

Caffe

Berkeley AI Research (BAIR) is responsible for Cafee, a deep learning framework built on C++ libraries. It easily integrates with Python. Instead of hard-coding, Caffe allows models to be defined by configurations. You can also easily switch between CPUs and GPUs. It can process of 60M images in a day on a NVIDIA K40 GPU. It is one of the powerful frameworks for Convolution Neural Networks and image processing.

Torch

Torch is an easy-to-use scientific computing framework built on Lua[JIT] with a strong C/CUDA backend. Torch allows you create graph-based neural network representations and parallelize them over CPUs and GPUs efficiently. It also has a Python-based implementation called PyTorch.

DeepLearning4j

DeepLearning4j was developed for Java Virtual Machine (JVM) environments. It is a commercial-grade, open-source, distributed deep learning library that works with languages like Java, Scala, Clojure, Python, and Kotlin. It comes with integration for big data tools like Hadoop and Spark.

Final Thoughts

Mastering deep learning requires time and dedication. Hopefully, the above tools will help you get started. You can choose a tool you like, set up goals you want to achieve, and practice every day. Attainable goals and daily practice will help you master the skills you need to become a deep learning expert.

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