Welcome to Tensorflow 2.0!
TensorFlow 2.0 has simply been launched, and it launched many options that simplify the mannequin improvement and upkeep processes. From the academic facet, it boosts folks’s understanding by simplifying many complicated ideas. From the business viewpoint, fashions are a lot simpler to know, keep, and develop.
Deep Studying is among the quickest rising areas of Synthetic Intelligence. Previously few years, we’ve got confirmed that Deep Studying fashions, even the only ones, can clear up very exhausting and complicated duties. Now, that the buzz-word interval of Deep Studying has, partially, handed, persons are releasing its energy and potential for his or her product enhancements.
The course is structured in a method to cowl all subjects from neural community modeling and coaching to place it in manufacturing.
In Half 1 of the course, you’ll study concerning the know-how stack that we are going to use all through the course (Part 1) and the TensorFlow 2.0 library fundamentals and syntax (Part 2).
In Half 2 of the course, we are going to dig into the thrilling world of deep studying. By way of this a part of the course, you’ll implement a number of varieties of neural networks (Absolutely Linked Neural Community (Part 3), Convolutional Neural Community (Part 4), Recurrent Neural Community (Part 5)). On the finish of this half, Part 6, you’ll study and construct their very own Switch Studying utility that achieves cutting-edge (SOTA) outcomes on the Canine vs. Cats dataset.
After passing the half 2 of the course and finally studying find out how to implement neural networks, in Half 3 of the course, you’ll discover ways to make your individual Inventory Market buying and selling bot using Reinforcement Studying, particularly Deep-Q Community.
Half 4 is all about TensorFlow Prolonged (TFX). On this a part of the course, you’ll discover ways to work with knowledge and create your individual knowledge pipelines for manufacturing. In Part 8 we are going to test if the dataset has any anomalies using the TensorFlow Knowledge Validation library and after discover ways to test a dataset for anomalies, in Part 9, we are going to make our personal knowledge preprocessing pipeline using the TensorFlow Remodel library.
In Part 10 of the course, you’ll study and create your individual Style API using the Flask Python library and a pre-trained mannequin. All through this part, you’ll get a greater image of find out how to ship a request to a mannequin over the web. Nevertheless, at this stage, the structure across the mannequin will not be scalable to thousands and thousands of request. Enter the Part 11. On this part of the course, you’ll discover ways to enhance resolution from the earlier part by using the TensorFlow Serving library. In a very simple method, you’ll study and create your individual Picture Classification API that may assist thousands and thousands of requests per day!
Today it’s changing into increasingly widespread to have a Deep Studying mannequin inside an Android or iOS utility, however neural networks require numerous energy and sources! That’s the place the TensorFlow Lite library comes into play. In Part 12 of the course, you’ll discover ways to optimize and convert any neural community to be appropriate for a cellular system.
To conclude with the training course of and the Half 5 of the course, in Part 13 you’ll discover ways to distribute the coaching of any Neural Community to a number of GPUs and even Servers using the TensorFlow 2.0 library.
Who this course is for:
- Deep Studying Engineers who need to study Tensorflow 2.0
- Synthetic Intelligence Engineers who need to develop their Deep Studying stack abilities
- Laptop Scientists who need to enter the thrilling space of Deep Studying and Synthetic Intelligence
- Knowledge Scientists who need to take their AI Expertise to the following degree
- AI consultants who need to develop on the sphere of functions
- Python Builders who need to enter the thrilling space of Deep Studying and Synthetic Intelligence
- Engineers who work in know-how and automation
- Businessmen and corporations who need to get forward of the sport
- College students in tech-related applications who need to pursue a profession in Knowledge Science, Machine Studying, or Synthetic Intelligence
- Anybody obsessed with Synthetic Intelligence
Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Crew, Luka Anicin
Final up to date 8/2020
Dimension: 5.12 GB
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