Introduction to Deep Learning Tools

Scope of Deep Learning in 2020

2020 is here and Artificial Intelligence is the word to go by if you start observing the current hiring trends. AI is not just a fancy word to be used in blogs, it’s the most important word in a resume nowadays. From Google to Facebook to upcoming start-ups, everyone wants a candidate with knowledge in AI who can create magic with tech. Every firm across the world is trying to sync its operations with AI in order to create value for its businesses. Before we jump into the world of Deep Learning Tool, we will explore the world of Deep Learning.

AI scope is unparalleled with plenty of benefits like improved efficiency, streamlining customer service, cost reduction, growth in terms of revenue, and more added advantages. Finding the right learning tool to develop the skills is an absolute necessity to achieve high career growth and get on the highway to your dream job.

So, the real question is this: What is Deep Learning?

The AI market will grow to a $190 billion industry by 2025, according to research firm Markets and Markets.

What is Deep Learning?

Deep Learning is an AI function essentially developed to copy the brain functioning in acts such as processing the data and creating patterns for its usage in making accurate decisions.

If you are starting to move deeper into the world of AI, you will find that Deep Learning is a subset of Machine Learning. Deep learning tools are used to train Deep Neural Networks for better use in those cases where machine learning fails.

Deep learning can be attributed as a collection of algorithms used by data scientists and everyone alike in Machine Learning, computes high-level abstractions in the data field with the usage of model architectures consisting of multiple nonlinear transformations.

Reasons for Learning Deep Learning in 2020-

• Continuous advancements in Algorithmic methodology are facilitating rapid developments in deep learning methods
• Evolution in Machine Learning approaches
• New classes of neural networks conceptualized and developed have led to the emergence of applications like text translation and image classification
• More data available for creating and developing neural network
• Advances in Computational advances of cloud computing and graphics processing units
  

Career opportunities in the field of Deep Learning

An outsider person not adept in deep learning will perceive it to be limited to the data scientists and computer researchers. But, its applications have been immense for businesses with many firms incorporating it into their processes.

With so many businesses on-board, it’s not surprising to see opportunities open for individuals to build their careers around deep learning.

Some of the career scopes in deep learning:

1. Software Engineer
2. Research Analyst
3. Data Analyst
4. Data Scientist
5. Data Engineer
6. Image Recognition
7. Software Developer
8. Research Scientist
9. Research Fellow
10. Instructor for Deep Learning
11. Applied Scientist
12. Natural Language Process Engineer

Deep Learning Applications

Speech recognition

Gaming tools like PSP, Xbox are employing deep learning tools for speech recognition. Siri, Google Now and Skype are among the other software tools to employ deep neural networks to streamline their services to offer the best consumer experience.

Image Recognition

Law enforcement agencies across the world are using face recognition software based on deep learning to hunt down criminals. Self-driven cars are employing it currently to find obstacles in the road and drive the car smoothly.

NLP- Natural Processing Language

Used primarily to find new patterns in customer reports, doctors reports and developing chatbots.

Salary

So, the real question for everyone who wants to make a stellar career in the deep learning industry is- how much they can earn? Sky’s the limit for deep learning experts. We will take a look at some of the salaries benchmark in the industry-

According to Indeed– a top recruitment online website,

As the above image highlights the growing demand for deep learning experts, it’s not surprising to see more and more people switching to the AI world.

We at Accredian, understand the world of deep learning and are helping people make a career in it effectively. 

I am going to help you get a further understanding of deep learning by talking about the best deep learning tools in 2020 important to be a part of the data scientist toolbox.

Top Deep Learning Tools in 2020

Tensorflow Flow2.0

TensorFlow2.0 was developed by the Google Brain team for internal Google use. It was released under the Apache License 2.0 on November 9, 2015. Offers js library which is much helpful for the machine learning students with its API helping in creating and training modules. Reusable libraries for common model components while providing ops that provide which wrap C++ kernels. One of the best deep learning open source tools available in the market.

Features:

1. Supports languages like Python, JavaScript, Swift, C, Go, Java, C#, Go, and more.
2. Supports 23 data types 
3. Different applications like NLP, image recognition, sentiment analysis are possible among others. 
4. Open-source software
5. Run-on GPUs and CPUs
6. Price- free

Amazon Machine Learning (AML)

Amazon SageMaker helps the developers in building, training and deploying machine learning models as per the scale they wish; or build custom models with enhanced support for all the common open-source frameworks. Developers can add image and video analysis to their applications to catalog assets, automate media workflows among others. 

Features:

1. Create machine learning models
2. Low cost and efficient
3. Supports image recognition, video recognition
4.  Deepest set of security & encryption capabilities
5. Turn text into lifelike speech
6. Add natural language search capabilities

Scikit

Scikits are Python-based scientific toolboxes built around the Python library named SciPy for advanced scientific computing. The best thing about it is that it’s an open-source project devoted to machine learning-

Has limited developer resources but has nice algorithms while using Cython for functions that need to be swift. It doesn’t support deep learning, reinforcement learning, sequence prediction and is developed only for Python language. It doesn’t have an API for other languages. 

Features:

1. Classification
2. Regression
3. Clustering
4. Dimensionality reduction
5. Model selection
6. Preprocessing
7. Easy to understand documentation
8. Open Source and free

Gensim

A free python library to help developers in semantic modeling. developers can analyze the plain text documents for semantic structure and you can retrieve text documents that are semantically similar. Free to use for both personal and commercial purposes. 

Features:

1. Highly scalable 
2. Platform Independent 
3. Robust 
4. Effective Implementation
5. Free to use

Keras.io

A cross-platform tool based on the python language. It has an API for neural networks and is free to use. focus on enabling fast experimentation. Keras is compatible with: Python 2.7-3.6.

Features:

1. Great user experience with GUI
2. Works with python
3. Works well with neural networks
4. Suited for advanced research

Pytorch

An open-source based machine learning framework to help the developers move from research prototyping to production deployment. Available for Linux, Mac OS, windows and is free to use. Based on the languages- Python, C++, CUDA. 

Features:

1. A rich ecosystem of tools and libraries
2. supported on major cloud platforms
3. Autograd Module

Convnetjs

Javascript based library for training deep learning models in the user browser itself. No software requirements, no compilers, no installations, no GPU makes it a much-vaunted tool in the deep learning industry. 

Features:

1. Reinforcement learning module
2. Train CNN for processing images
3. Common neural networks modules

Fastai

It’s built on top of PyTorch and provides a single API to the different deep learning applications and data types. One big advantage with Fastai is that the same API can be called for running various tasks and data types in machine learning including images, text, tabular data, etc. 

Features:

1. Support interactive computing and  traditional software development
2. The library is available on the Google Cloud Platform and is coming sooner to AWS.
3. Easy to use for deep learning applications.

Microsoft Cognitive Toolkit

Free-to-use and open-source that trains machine learning algorithms to learn like the complex human brain. Scalable, High Speed and accuracy with compatibility with programming languages make it a much-needed tool. 

Features

1. Train and host with Azure Cloud Services
2. Free
3. Commercial Grade Quality
4. highly optimized components 

So what do we learn?

The deep learning is going to become one of the most talked-about techs as it moves on its way to fuel the next growth in AI.  Experts are predicting the focus of companies will be on AI becoming the cornerstone for future advances in any industry be it the Tech, Healthcare, Sports, Entertainment. The career is sorted out for candidates with exceptional Knowledge in the field of deep learning.

One way is getting a proper certification and learning experiences while getting a good feel of the tools being used primarily in the deep learning industry.