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Tensorflow 2.0: Deep Learning and Artificial Intelligence - Views Coupon

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Tensorflow is Google's library for deep learning and artificial intelligence. It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version. Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning. In other words, if you want to do deep learning, you gotta know Tensorflow.


PyTorch Ultimate: From Basics to Cutting-Edge - Views Coupon

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It is compact and to the point giving you practical "templates" on how to apply different classes of DL algorithms in PyTorch. PyTorch is a Python framework developed by Facebook to develop and deploy Deep Learning models. It is one of the most popular Deep Learning frameworks nowadays. You will learn everything that is needed for developing and applying deep learning models to your own data. All relevant and state of the art model architectures will be covered.


R Ultimate: Learn R for Data Science and Machine Learning - Views Coupon

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You want to be able to perform your own data analyses with R? You want to learn how to get business-critical insights out of your data? Or you want to get a job in this amazing field? In all of these cases, you found the right course! We will start with the very Basics of R, like data types and -structures, programming of loops and functions, data im- and export. Then we will dive deeper into data analysis: we will learn how to manipulate data by filtering, aggregating results, reshaping data, set operations, and joining datasets.


Ashish Patel on LinkedIn: #datascience #machinelearning #artificialintelligence

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Whether you're looking for information that will help you certify Google Cloud in machine learning, how to build deep learning model-based products, or the best data cleaning strategies and practices, you've come to the right place. First, examine the literature on industrial processes and their aftermath. The list may be helpful. You will be successful in achieving this objective. Key Features: --------------- Learn how to convert a deep learning model running on notebook environments into a production-ready application supporting various deployment environments.


Deep Learning With Keras and TensorFlow - Views Coupon

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The "Deep Learning with Keras and TensorFlow" course is an intermediate level course, curated exclusively for both beginners and professionals. The course covers the basics as well as the advanced level concepts. The course contains content based videos along with practical demonstrations, that performs and explains each step required to complete the task. If you're new to this technology, then don't worry - the course covers the topics from the basics. If you have done some programming before, you should pick it up quickly.


[100%OFF] NumPy For Data Science And Machine Learning In Python

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This is Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python. One question or concern I get a lot is that people want to learn deep learning and data science, so they take these courses, but they get left behind because they don't know enough about the Numpy stack in order to turn those concepts into code. Even if I write the code in full, if you don't know Numpy, then it's still very hard to read. This course is designed to remove that obstacle – to show you how to do things in the Numpy stack that are frequently needed in deep learning and data science. So what are those things?


Deep Dive into Artificial Intelligence - The Master Program

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The course covers the basics as well as the advanced level concepts. The course contains content based videos along with practical demonstrations, that performs and explains each step required to complete the task. There are separate sections for Artificial Intelligence, Data Science with Python, Machine Learning, and Deep Learning with Keras and TensorFlow which lets you scale up these techniques. Don't worry if you've never used Python before; the course covers the topics from the basics. You should be able to pick it up fast if you have experience with programming.


Autodidact's path to AI/Machine Learning (part 2)

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In the first part of the Autodidacts path to a MSc level in AI/Machine Learning, using UCL's MSc as a lighthouse to guide us through the rough waters of building a Machine Learning MSc curriculum, we had a look at some of the most established and helpful resources for a beginning ML engineer. Moving on to the second part of our attempt to build a curriculum for the autodidact enthusiast of Machine Learning, we will dive into one of the hot topics during the past decade. This is no other than Deep Learning. Although technically a sub-category of Machine Learning, Deep Learning has evolved into its own paradigm and has earned the title of'one of the pillars of ML' and for good reasons. The past decade has seen a huge number of successful applications and technological advancements that utilise Deep Learning.


Deep Learning With Keras and TensorFlow

#artificialintelligence

The "Deep Learning with Keras and TensorFlow" course is an intermediate level course, curated exclusively for both beginners and professionals. The course covers the basics as well as the advanced level concepts. The course contains content based videos along with practical demonstrations, that performs and explains each step required to complete the task. If you're new to this technology, then don't worry - the course covers the topics from the basics. If you have done some programming before, you should pick it up quickly.


Advanced AI: Deep Reinforcement Learning in Python

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Created by Lazy Programmer Team, Lazy Programmer Inc. This course is all about the application of deep learning and neural networks to reinforcement learning. If you've taken my first reinforcement learning class, then you know that reinforcement learning is on the bleeding edge of what we can do with AI. Specifically, the combination of deep learning with reinforcement learning has led to AlphaGo beating a world champion in the strategy game Go, it has led to self-driving cars, and it has led to machines that can play video games at a superhuman level. Reinforcement learning has been around since the 70s but none of this has been possible until now.