Construct Superb Purposes of Deep Studying and Synthetic Intelligence in TensorFlow 2.0
What you’ll be taught
- Find out how to use Tensorflow 2.0 in Knowledge Science
- Vital variations between Tensorflow 1.x and Tensorflow 2.0
- Find out how to implement Synthetic Neural Networks in Tensorflow 2.0
- Find out how to implement Convolutional Neural Networks in Tensorflow 2.0
- Find out how to implement Recurrent Neural Networks in Tensorflow 2.0
- Find out how to construct your individual Switch Studying utility in Tensorflow 2.0
- Find out how to construct a inventory market buying and selling bot using Reinforcement Studying (Deep-Q Community)
- Find out how to construct Machine Studying Pipeline in Tensorflow 2.0
- Find out how to conduct Knowledge Validation and Dataset Preprocessing using TensorFlow Knowledge Validation and TensorFlow Remodel.
- Placing a TensorFlow 2.0 mannequin into manufacturing
- Find out how to create a Trend API with Flask and TensorFlow 2.0
- Find out how to serve a TensorFlow mannequin with RESTful API
- Some maths fundamentals like understanding what’s a differentiation or a gradient
- Python fundamentals
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 aspect, it boosts individuals’s understanding by simplifying many complicated ideas. From the business standpoint, fashions are a lot simpler to know, keep, and develop.
Deep Studying is likely one of the quickest rising areas of Synthetic Intelligence. Prior to now few years, we’ve got confirmed that Deep Studying fashions, even the only ones, can remedy very exhausting and complicated duties. Now, that the excitement-phrase 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 strategy to cowl all matters from neural community modeling and coaching to place it in manufacturing.
In Half 1 of the course, you’ll be taught 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’ll dig into the thrilling world of deep studying. By this a part of the course, you’ll implement a number of forms of neural networks (Totally 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 be taught and construct their very own Switch Studying utility that achieves state-of-the-art (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 learn 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 learn to work with information and create your individual information pipelines for manufacturing. In Part 8 we’ll verify if the dataset has any anomalies using the TensorFlow Knowledge Validation library and after learn to verify a dataset for anomalies, in Part 9, we’ll make our personal information preprocessing pipeline using the TensorFlow Remodel library.
In Part 10 of the course, you’ll be taught and create your individual Trend API using the Flask Python library and a pre-educated 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 isn’t scalable to hundreds of thousands of request. Enter the Part 11. On this part of the course, you’ll learn to enhance answer from the earlier part by using the TensorFlow Serving library. In a very simple means, you’ll be taught and create your individual Picture Classification API that may help hundreds of thousands of requests per day!
Lately it’s turning into increasingly standard to have a Deep Studying mannequin inside an Android or iOS utility, however neural networks require a number of energy and assets! That’s the place the TensorFlow Lite library comes into play. In Part 12 of the course, you’ll learn to optimize and convert any neural community to be appropriate for a cell gadget.
To conclude with the training course of and the Half 5 of the course, in Part 13 you’ll learn 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 be taught Tensorflow 2.0
- Synthetic Intelligence Engineers who need to develop their Deep Studying stack expertise
- 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 stage
- AI specialists who need to develop on the sector 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 firms who need to get forward of the sport
- College students in tech-associated packages 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 Staff, Luka Anicin
Final up to date 8/2020
Dimension: 5.12 GB
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