Welcome to Tensorflow 2.0!
What an thrilling time. It’s been practically 4 years since Tensorflow was launched, and the library has developed to its official second model.
Tensorflow is Google’s library for deep studying and synthetic intelligence.
Deep Learning has been chargeable for some wonderful achievements just lately, equivalent to:
- Producing stunning, photograph-reasonable photographs of individuals and issues that by no means existed (GANs)
- Beating world champions within the technique recreation Go, and complicated video video games like CS:GO and Dota 2 (Deep Reinforcement Learning)
- Self-driving vehicles (Pc Imaginative and prescient)
- Speech recognition (e.g. Siri) and machine translation (Pure Language Processing)
- Even creating movies of individuals doing and saying issues they by no means did (DeepFakes – a probably nefarious utility of deep studying)
Tensorflow is the world’s hottest library for deep studying, and it’s constructed by Google, whose mum or dad Alphabet just lately grew to become essentially the most money-wealthy firm on the planet (just some days earlier than I wrote this). It’s the library of alternative for a lot of corporations doing AI and machine studying.
In different phrases, if you wish to do deep studying, you gotta know Tensorflow.
This course is for newbie-degree college students all the way in which as much as skilled-degree college students. How can this be?
When you’ve simply taken my free Numpy prerequisite, then you understand every part you could leap proper in. We’ll begin with some very primary machine studying fashions and advance to cutting-edge ideas.
Alongside the way in which, you’ll study all the main deep studying architectures, equivalent to Deep Neural Networks, Convolutional Neural Networks (picture processing), and Recurrent Neural Networks (sequence knowledge).
Present initiatives embody:
- Pure Language Processing (NLP)
- Recommender Techniques
- Switch Learning for Pc Imaginative and prescient
- Generative Adversarial Networks (GANs)
- Deep Reinforcement Learning Inventory Buying and selling Bot
Even when you’ve taken all of my earlier programs already, you’ll nonetheless study easy methods to convert your earlier code in order that it makes use of Tensorflow 2.0, and there are all-new and by no means-earlier than-seen initiatives on this course equivalent to time sequence forecasting and easy methods to do inventory predictions.
This course is designed for college kids who wish to study quick, however there are additionally “in-depth” sections in case you wish to dig just a little deeper into the speculation (like what’s a loss operate, and what are the various kinds of gradient descent approaches).
Superior Tensorflow matters embody:
- Deploying a mannequin with Tensorflow Serving (Tensorflow within the cloud)
- Deploying a mannequin with Tensorflow Lite (cell and embedded purposes)
- Distributed Tensorflow coaching with Distribution Methods
- Writing your personal customized Tensorflow mannequin
- Changing Tensorflow 1.x code to Tensorflow 2.0
- Constants, Variables, and Tensors
- Keen execution
- Gradient tape
Non permanent VIP Part (be part of NOW and get it earlier than it’s gone!):
- DeepDream (3 workouts)
- Object Localization (7 workouts)
Teacher’s Be aware: Since Tensorflow 2.0 continues to be in beta, some options will not be but finalized. Keep tuned! 😉
Teacher’s Be aware 2: This course focuses on breadth relatively than depth, with much less concept in favor of constructing extra cool stuff. In case you are searching for a extra concept-dense course, this isn’t it. Typically, for every of those matters (recommender techniques, pure language processing, reinforcement studying, laptop imaginative and prescient, GANs, and so on.) I have already got programs singularly targeted on these matters.
Thanks for studying, and I’ll see you at school!
Who this course is for:
- Learners to superior college students who wish to study deep studying and AI in Tensorflow 2.0
Created by Lazy Programmer Staff, Lazy Programmer Inc.
Final up to date 7/2020
Measurement: 7.09 GB
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