The Best AI Development Tools, Frameworks for 2020

The Best AI Development Tools, Frameworks for 2020

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing (NLP), speech recognition and machine vision.

Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing (NLP), speech recognition and machine vision.

AI is becoming a bigger part of our lives, as the technology behind it becomes more and more advanced. Machines are improving their ability to 'learn' from mistakes and change how they approach a task the next time they try it.

Some researchers are even trying to teach robots about feelings and emotions.

In this article, we will talk about the best AI development tools,we have explained it’s top features which can help you to compare which is the best.

1. TensorFlow

This tool has many advantages that make it a good fit for those who need an efficient AI framework.

For example, the framework is developed by Google. This by itself is a big advantage. Why? Because you get extensive support and regular updates. This means that TensorFlow is keeping the pace with the modern industry of machine learning. You won’t have to worry about up to date features and new options, because they are coming out regularly.

It’s worth mentioning the support as well. Since the tool is backed up by Google, the support is also first grade. If any problem comes up, you can take a look in the huge community and you’ll surely find an answer.

Another pro that comes with using this framework is the flexibility. TensorFlow was built to be a modular system. This means that you can use parts of it on their own, but together as well. Portability is also an advantage of this tool. It works even on mobile systems if you don’t have a traditional desktop or laptop at hand.

Features of TensorFlow

  • Robust Production: TensorFlow serves a direct path for the production. Despite language or platform selection, with TensorFlow anyone can deploy and train its model smoothly.

  • Model building: With multiple abstractions with TensorFlow, you can choose the right one as per your requirements. It has high-level Keras API to build and train the models. This makes getting started with TensorFlow easy.

  • Supports powerful experiments: For research and development; TensorFlow supports the creation of complex technologies with its flexibility and control over the best features. Besides; It also supports experimentation with models and powerful add-on libraries.

  • Distributed training: TensorFlow has supported exploring of the two of the major distributed methods.

  • To reduce the training time; TensorFlow allows to distribute the training time of the neural network model over multiple kinds of servers.

  • To search for good hyperparameters; TensorFlow allows running multiple experiments parallelly over different servers.

  • Accessible syntax: TensorFlow provides syntax which is accessible and readable easily. This leads to easy ways to program any resource.

  • More network control: With higher control over the network; TensorFlow allows developers to experiment and understand how the implementations occur in the network for any operation.

2. Scikit-learn

A well known, free machine learning library is scikit-learn for the Python-based programming. It contains classification, regression, and clustering algorithms like support vector machines, random forests, gradient boosting, and k-means. This software is easily accessible. If you learn the primary use and syntax of Scikit-Learn for one kind of model, then switching to a new model or algorithm is very easy.

Features of Scikit-learn

  • Cross-Validation: Scikit-learn allows the developers to estimate the performance of supervised models for the unseen data.

  • Clustering: This feature is mainly for the unsupervised classification. Data like Kmeans which is unlabeled can be grouped.

  • Feature selection: To build supervised models, it is easy to identify meaningful attributes.

  • Feature extraction: To identify the text and image data attributes.

  • Datasets: to generate and test datasets that have a specific property. Later on, investigating their model behavior.

  • Dimensionality reduction: To reduce the number of required attributes in data for visualization, feature selection, and summarization.

  • Supervised model: a huge array which is not limited to discriminate analysis, lazy methods, generalized linear models, neural networks, support vector machines, and naive byes.

3.Keras

Keras is the best candidate if you’re looking for a framework that is simple and easy to learn. This is one of its greatest advantages. Getting to know a new framework is essential if you want to be proficient using it.

Because Keras is simple and minimalistic, comes with another pro: it is lightweight. Any framework, no matter what type it is, it’s best when it is lightweight. Why is this true? Because being such, will grant you a better performance. After all, there are fewer resources needed to get the job done. So yes, Keras is fast as well.

Features of Keras

  • Ease of extensibility: Developers can add new modules easily as new functions or classes. Also; the existing modules have ample examples for the support. The complete expressiveness which comes from the ease to build new modules; makes Keras most loved for advanced research.

  • User-friendliness: The best part of Keras is; it is designed for humans. Unlike others; which are designed for the machines. One of its goals is to make the user experience the best features by keeping it at the front and center.

  • Less cognitive load: Keras reduces the cognitive load with simple and consistent APIs. It provides actionable and clear feedback on any type of user error. Keras minimizes the number of actions by a user required for any common use case.

  • Modularity:

  • What is a model?

  • A model is a sequence of fully configurable and standalone modules that can work together with as few restrictions as possible. Hence; a developer can combine optimizer, neural layers, initialization schemes, cost functions, activation functions, and regularization schemes to build new models.

  • Blessing of Python: Models in Keras are written in Python code. Hence; it is easier to debug, is compact and provides ease of extensibility.

4. Theano

Theano is a robust Python library that enables you to simply define, optimize and analyze numerical expressions involving multi-dimensional arrays with a high level of accuracy. With the transparent use of GPU for carrying out the data computation, Theano ensures you high efficiency in its operations.

Features of Theano

  • It can be used for carrying out deep learning research.

  • It is completely optimized for CPU and GPU.

  • Highly efficient for complex numerical computational tasks.

  • It comes with extensive code testing capabilities.

5. CAFFE

This framework is a recent one, that was released in 2017. It was written in C++, so this makes it a good choice for many software engineers and programmers

Caffe has become one of the most popular software development frameworks in the field of Artificial Intelligence. This framework is designed with a special focus on expressiveness, speed and modularity. A large segment of software development companies chooses Caffe for AI development as it provides extensible code that assists in the active development process.

Apart, the massive community of researchers and developers around the world at Caffe user hub offers seamless support to multiple startups as well as leading enterprises in building complex AI projects.

Features of Caffe

  • Active development: With its extensible code; Caffe helps in the active development process. In its first year; developers showed an exceptional interest in Caffe which lead to adding other significant changes. With the contribution of researchers and developers, Caffe is serving state of the art models and code.

  • Speed: For scenarios that include industry deployment and research experiments, speed is an asset. Hence; Caffe becomes popular. Caffe can process more than 60M images per day. That means; 1 ms/image for interference and 4 ms/image for learning purposes. As per Berkeley’s vision; Caffe is one of the fastest known convent implementations present today.

  • Expressive architecture: Caffe supports innovation and application with the help of its expressive architecture. Without any need for hard coding; models and optimizations can be defined by configuration only.

  • Huge community: Caffe has a community of developers and researchers around the world on GitHub and Caffe users hub. The community has helped multiple startup prototypes, academic research projects, as well as large scale industrial processes.

Summary

Today, machine learning is an integral part of any software development task. Every device is built considering the possible integration with AI tools. Therefore, it becomes necessary to select the right framework and evaluate that for the optimum results.

Before initiating the machine learning application, the selection of one technology from many options is a difficult task. It is imperative to evaluate a few options before building the final decision.

Thanks for reading !

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Complete hands-on Machine Learning tutorial with Data Science, Tensorflow, Artificial Intelligence, and Neural Networks. Introducing Tensorflow, Using Tensorflow, Introducing Keras, Using Keras, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Learning Deep Learning, Machine Learning with Neural Networks, Deep Learning Tutorial with Python

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• The History of Artificial Neural Networks
• Deep Learning in the Tensorflow Playground
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• Introducing Tensorflow
• Using Tensorflow
• Introducing Keras
• Using Keras to Predict Political Parties
• Convolutional Neural Networks (CNNs)
• Using CNNs for Handwriting Recognition
• Recurrent Neural Networks (RNNs)
• Using a RNN for Sentiment Analysis
• The Ethics of Deep Learning
• Learning More about Deep Learning

At the end, you will have a final challenge to create your own deep learning / machine learning system to predict whether real mammogram results are benign or malignant, using your own artificial neural network you have learned to code from scratch with Python.

Separate the reality of modern AI from the hype – by learning about deep learning, well, deeply. You will need some familiarity with Python and linear algebra to follow along, but if you have that experience, you will find that neural networks are not as complicated as they sound. And how they actually work is quite elegant!

This is hands-on tutorial with real code you can download, study, and run yourself.

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Explore the full course on Udemy (special discount included in the link): http://learnstartup.net/p/BkS5nEmZg

In less than 3 hours, you can understand the theory behind modern artificial intelligence, and apply it with several hands-on examples. This is machine learning on steroids! Find out why everyone’s so excited about it and how it really works – and what modern AI can and cannot really do.

In this course, we will cover:

•    Deep Learning Pre-requistes (gradient descent, autodiff, softmax)

•    The History of Artificial Neural Networks

•    Deep Learning in the Tensorflow Playground

•    Deep Learning Details

•    Introducing Tensorflow

•    Using Tensorflow

•    Introducing Keras

•    Using Keras to Predict Political Parties

•    Convolutional Neural Networks (CNNs)

•    Using CNNs for Handwriting Recognition

•    Recurrent Neural Networks (RNNs)

•    Using a RNN for Sentiment Analysis

•    The Ethics of Deep Learning

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At the end, you will have a final challenge to create your own deep learning / machine learning system to predict whether real mammogram results are benign or malignant, using your own artificial neural network you have learned to code from scratch with Python.

Separate the reality of modern AI from the hype – by learning about deep learning, well, deeply. You will need some familiarity with Python and linear algebra to follow along, but if you have that experience, you will find that neural networks are not as complicated as they sound. And how they actually work is quite elegant!

This is hands-on tutorial with real code you can download, study, and run yourself.

Thanks for reading

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Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial

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  2. Why Machine Learning - 09:18

  3. What is Machine Learning - 13:25

  4. Types of Machine Learning - 18:32

  5. Supervised Learning - 18:44

  6. Reinforcement Learning - 21:06

  7. Supervised VS Unsupervised - 22:26

  8. Linear Regression - 23:38

  9. Introduction to Machine Learning - 25:08

  10. Application of Linear Regression - 26:40

  11. Understanding Linear Regression - 27:19

  12. Regression Equation - 28:00

  13. Multiple Linear Regression - 35:57

  14. Logistic Regression - 55:45

  15. What is Logistic Regression - 56:04

  16. What is Linear Regression - 59:35

  17. Comparing Linear & Logistic Regression - 01:05:28

  18. What is K-Means Clustering - 01:26:20

  19. How does K-Means Clustering work - 01:38:00

  20. What is Decision Tree - 02:15:15

  21. How does Decision Tree work - 02:25:15 

  22. Random Forest Tutorial - 02:39:56

  23. Why Random Forest - 02:41:52

  24. What is Random Forest - 02:43:21

  25. How does Decision Tree work- 02:52:02

  26. K-Nearest Neighbors Algorithm Tutorial - 03:22:02

  27. Why KNN - 03:24:11

  28. What is KNN - 03:24:24

  29. How do we choose 'K' - 03:25:38

  30. When do we use KNN - 03:27:37

  31. Applications of Support Vector Machine - 03:48:31

  32. Why Support Vector Machine - 03:48:55

  33. What Support Vector Machine - 03:50:34

  34. Advantages of Support Vector Machine - 03:54:54

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  36. Where is Naive Bayes used - 04:17:45

  37. Top 10 Application of Machine Learning - 04:54:48

  38. How to become a Machine Learning Engineer - 04:59:46

  39. Machine Learning Interview Questions - 05:09:03