Learn the basics of Machine Learning & PyTorch
PyTorch is a deep learning library that has gained a lot of popularity in recent months. It’s imperative programming paradigm and high level API make it ideal for learning and experimentation. This video covers the basics of PyTorch, taking Linear Regression and Feedforward Neural Network.
Originally published by Aakash N S at Udemy
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 PythonMachine Learning, Data Science and Deep Learning with Python
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
• 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.
This video will focus on the top Python libraries that you should know to master Data Science and Machine Learning. Here’s a list of topics that are covered in this session:
Thanks for reading ❤
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This article provides an overview of Python and its application to Data Science and Machine Learning and why it is important.
Originally published by Chris Kambala at dzone.com
Python is a general-purpose, high-level, object-oriented, and easy to learn programming language. It was created by Guido van Rossum who is known as the godfather of Python.
Python is a popular programming language because of its simplicity, ease of use, open source licensing, and accessibility — the foundation of its renowned community, which provides great support and help in creating tons of packages, tutorials, and sample programs.
Python can be used to develop a wide variety of applications — ranging from Web, Desktop GUI based programs/applications to science and mathematics programs, and Machine learning and other big data computing systems.
Let’s explore the use of Python in Machine Learning, Data Science, and Data Engineering.Machine Learning
Machine learning is a relatively new and evolving system development paradigm that has quickly become a mandatory requirement for companies and programmers to understand and use. See our previous article on Machine Learning for the background. Due to the complex, scientific computing nature of machine learning applications, Python is considered the most suitable programming language. This is because of its extensive and mature collection of mathematics and statistics libraries, extensibility, ease of use and wide adoption within the scientific community. As a result, Python has become the recommended programming language for machine learning systems development.Data Science
Data science combines cutting edge computer and storage technologies with data representation and transformation algorithms and scientific methodology to develop solutions for a variety of complex data analysis problems encompassing raw and structured data in any format. A Data Scientist possesses knowledge of solutions to various classes of data-oriented problems and expertise in applying the necessary algorithms, statistics, and mathematic models, to create the required solutions. Python is recognized among the most effective and popular tools for solving data science related problems.Data Engineering
Data Engineers build the foundations for Data Science and Machine Learning systems and solutions. Data Engineers are technology experts who start with the requirements identified by the data scientist. These requirements drive the development of data platforms that leverage complex data extraction, loading, and transformation to deliver structured datasets that allow the Data Scientist to focus on solving the business problem. Again, Python is an essential tool in the Data Engineer’s toolbox — one that is used every day to architect and operate the big data infrastructure that is leveraged by the data scientist.Use Cases for Python, Data Science, and Machine Learning
Here are some example Data Science and Machine Learning applications that leverage Python.
This article provided an overview of Python and its application to Data Science and Machine Learning and why it is important.
Originally published by Chris Kambala at dzone.com