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PyTorch: Deep Learning and Artificial Intelligence

PyTorch: Deep Learning and Artificial Intelligence

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8.5/10 (Our Score)
Product is rated as #145 in category Machine Learning

Welcome to PyTorch: Deep Learning and Artificial Intelligence!

Although Google’s Deep Learning library Tensorflow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence.

Is it possible that Tensorflow is popular only because Google is popular and used effective marketing?

Why did Tensorflow change so significantly between version 1 and version 2? Was there something deeply flawed with it, and are there still potential problems?

It is less well–known that PyTorch is backed by another Internet giant, Facebook (specifically, the Facebook AI Research Lab – FAIR). So if you want a popular deep learning library backed by billion dollar companies and lots of community support, you can’t go wrong with PyTorch. And maybe it’s a bonus that the library won’t completely ruin all your old code when it advances to the next version. 😉

On the flip side, it is very well–known that all the top AI shops (ex. OpenAI, Apple, and JPMorgan Chase) use PyTorch. OpenAI just recently switched to PyTorch in 2020, a strong sign that PyTorch is picking up steam.

If you are a professional, you will quickly recognize that building and testing new ideas is extremely easy with PyTorch, while it can be pretty hard in other libraries that try to do everything for you. Oh, and it’s faster.

Instructor Details

Today, I spend most of my time as an artificial intelligence and machine learning engineer with a focus on deep learning, although I have also been known as a data scientist, big data engineer, and full stack software engineer. I received my masters degree in computer engineering with a specialization in machine learning and pattern recognition. Experience includes online advertising and digital media as both a data scientist (optimizing click and conversion rates) and big data engineer (building data processing pipelines). Some big data technologies I frequently use are Hadoop, Pig, Hive, MapReduce, and Spark. I've created deep learning models to predict click-through rate and user behavior, as well as for image and signal processing and modeling text. My work in recommendation systems has applied Reinforcement Learning and Collaborative Filtering, and we validated the results using A/B testing. I have taught undergraduate and graduate students in data science, statistics, machine learning, algorithms, calculus, computer graphics, and physics for students attending universities such as Columbia University, NYU, Hunter College, and The New School. Multiple businesses have benefitted from my web programming expertise. I do all the backend (server), frontend (HTML/JS/CSS), and operations/deployment work. Some of the technologies I've used are: Python, Ruby/Rails, PHP, Bootstrap, jQuery (Javascript), Backbone, and Angular. For storage/databases I've used MySQL, Postgres, Redis, MongoDB, and more.

Specification: PyTorch: Deep Learning and Artificial Intelligence

Duration

24 hours

Year

2022

Level

All

Certificate

Yes

Quizzes

No

4 reviews for PyTorch: Deep Learning and Artificial Intelligence

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  1. Dieter Jordens

    Nice, clear voice. I’m looking forward to the rest of the course.

    Helpful(0) Unhelpful(0)You have already voted this
  2. Marco Capuccini

    I have only gone through the code explanations since I am already familiar the DL theory. It’s top notch content IMO.

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  3. Frank Kurka

    The lazy programmer is one of the best. If he makes a course I buy it because it is him.

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  4. Richard Long

    Yes it seems to be a good match so far

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    PyTorch: Deep Learning and Artificial Intelligence
    PyTorch: Deep Learning and Artificial Intelligence

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