If you follow my blog regularly, then you may be wondering why am I writing an article to tell people to learn Python? Didn't I ask you to prefer Java over Python a couple of years ago? Well, things have changed a lot since then. In 2016, Python replaced Java as the most popular language in colleges and Universities, and since then, it has never looked back. Python is growing and growing big time. If you read programming and technology news or blog post, then you might have noticed the rise of Python as many popular developer communities, including StackOverFlow and CodeAcademy has mentioned the rise of Python as a major programming language.

AI4ALL Open Learning is a new education program, facilitated by community members, that gives students the tools to solve problems they care about using AI. The free curriculum is designed for a high school audience and includes facilitator guides to help teachers and volunteers bring this knowledge into their classrooms and communities. The program builds problem-solving skills, technical skills, and helps students gain an understanding of how AI impacts everyday life. How will you change AI? Start here.

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Academics and startups from Imperial College London and the Massachusetts Institute of Technology presented their innovative ideas at a conference. Co-organised by Imperial Business Partners and MIT, the Innovation Journeys Conference brought together industry leaders, academics and startups from both universities to explore and share the latest technological innovations and research. Imperial Business Partners offers a unique approach to problem-solving for research-driven industries via accelerated access to Imperial's expertise, talent and facilities. Imperial's Director of Enterprise Simon Hepworth discussed the university's role in the entrepreneurial journey, saying it is present at all stages, whether that's for students or staff. The College's enterprising ecosystem is vast, ranging from academic ventures, entrepreneurship competitions and programmes such as WeInnovate and the Enterprise Lab, which supports student innovation.

Use Python for Data Science and Machine Learning Use Spark for Big Data Analysis Implement Machine Learning Algorithms Learn to use NumPy for Numerical Data Learn to use Pandas for Data Analysis Learn to use Matplotlib for Python Plotting Learn to use Seaborn for statistical plots Use Plotly for interactive dynamic visualizations Use SciKit-Learn for Machine Learning Tasks Random Forest and Decision Trees Natural Language Processing and Spam Filters Support Vector Machines Some programming experience Admin permissions to download files Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!

Nine months ago, as part of a joint reference architecture launch with Nvidia, Pure Storage published TensorFlow deep learning performance results. The goal of creating a joint architecture with Nvidia was to identify and solve performance bottlenecks present in an end-to-end deep learning environment -- especially at scale. During creation of our reference architecture, my team identified and improved performance issues across storage, networking, and compute. Our system is a physical entity, and everything from cabling configuration and MTU size to Tensorflow prefetch buffer size can impact performance. The software and hardware stack in our test environment.

Deep Learning is/has become the hottest skill in Data Science at the moment. There is a plethora of articles, courses, technologies, influencers and resources that we can leverage to gain the Deep Learning skills. Deep Learning is not just one thing though! There are so many applications that one cannot simply learn all in a short span of time (maybe some can but having learnt it is not the same as being skilled in it). This list will comprise of resources / courses that are the most famous right now.

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When you start learning a new skill, the first thing is to look at the big picture and where your would-be skills fit in the field. It gives you a context of what role you can play or are expected to play. And when the skill and field are evolving & overwhelmingly large, you are so engrossed in the details that most probably you tend to miss the purpose. In my view, to understand the big picture, ask yourself'why' more often and start with the end in your mind. The following analogy is not particularly about artificial intelligence but in general.

Developed by a team of MIT graduate students, TensorFire can run TensorFlow-style machine learning models on any GPU, without requiring the GPU-specific middleware typically needed by machine learning libraries such as Keras-js. TensorFire is another step towards making machine learning available to the broadest possible audience, using hardware and software people are already likely to possess, and via advances in how accurate model predictions can be served with a fraction of the resources previously needed. Business demands flexibility, but IT needs control. TensorFire works using the WebGL standard, a cross-platform system for rendering GPU-accelerated graphics in browsers. WebGL supports GLSL, a C-like language used to write shaders, which are short programs used to transform data directly on the GPU.