what you will learn
- How to use the Tensor-flow 2.0 in the data science.
- There is a significant difference between Tensorflow 1.x and Tensorflow 2.0
- How to implement artificial neural networks in Tensorflow 2.0
- How to implement the convolutional neural networks in the Tensorflow 2.0.
- How to implement the recurrent neural networks in the Tensorflow 2.0.
- How to create a Transfer Learning application in Tensorflow 2.0
- How to create a stock trading system through strengthening training (Deep-Q Network)
- How to create a machine learning channel in Tensorflow 2.0.
- How to verify TensorFlow data and perform data validation and dataset preprocessing via TensorFlow conversion.
- Introducing the TensorFlow 2.0 model
- How to create a fashion API with Flask and Tensor Flow 2.0.
- How to view the TensorFlow model using the RESTful API.
Requirements
- Do some basic math like knowing what mathematics is or teaching.
- Python Basics
- He explained
- Tensorflow 2.0 ۾ Welcome!
TensorFlow 2.0 has just been launched and introduces some features that facilitate the process of form development and maintenance. Academically, it improves people's understanding by facilitating many complex concepts. From an industry perspective, models are easy to understand, maintain and develop.
Deep learning is the one of fastest growing areas of the artificial intelligence. Over the years, we have shown that even the simplest models of deep learning can solve very difficult and complex tasks. Now that the era of popular deep learning words is out, people are using their power and capabilities to improve their products.
The course is designed to cover a broad range of topics, from neural network modeling and training to production.
In the first part of the course, you will learn about the combination of techniques that we will use during the course (section 1) and the basics of the TensorFlow 2.0 library and syntax (section 2).
In the second part of the course, we will explore the fascinating world of deep learning. During this part of the course, you cover several types of neural networks (fully connected neural networks (section 3), complex neural networks (section 4) and recurrent neural networks (section 5)). At the end of this section, section 6, you will learn about their SOTA Application for Learning Transfer (SOTA) dogs. Cats
After passing Part 2 of the course and finally learning how to apply to neural networks, in Part 3 of the course you will learn how to build your own stocks using reinforcement learning, especially using Deep-Q networks. How To Build A Trade Boat Market
The fourth part is about TensorFlow Extended (TFX). In this part of cours - you will learn how to work with the data & create your own data pipelines for the production. In Section 8 we will examine the irregularities in the data set using the TensorFlow data validation library and once we know how to check for irregularities in the data set, in Section 9 we will check TensorFlow. Create a pre-processing pipeline using Transform Libraries.
In Section 10 of the course, you will learn and create your own Flask Python library and pre-trained models using the Fashion API. During this section you will get a better picture of how to apply on an online form. Currently, however, the architecture around the model is not accessible to millions of applications. Enter Section 11. In this section of the course, you will learn how to improve the solution from the previous section by using the TensorFlow Serving Library. The simplest way, you can learn and create your own image rating API that can help millions of requests in a day!
Nowadays, deep learning models are becoming more common within the Android or iOS app, but neural networks need more energy and resources! Here comes the TensorFlow Lite library. In Section 12 of the course, you will learn how to improve and modify any neural network according to your mobile device.
To summarize the learning process and in part 5 of this course, Section 13 will teach you how to deploy any neural network training in many GPUs or even servers using the TensorFlow 2.0 library.
This course is for anyone:
- Deep learning - engineers who want's to learn the Tensor-flow 2.0.
- AI engineers who want to improve their learning skills.
- Computer scientists who want to enter the exciting field of deep learning and artificial intelligence
- Data scientists who want to take their artificial intelligence skills to the next level
- AI experts who want to expand in the application field.
- Python developers who want to enter the exciting field of deep learning and artificial intelligence
- Engineer working in technology and automation
- Entrepreneurs and companies that want to grow in the game.
- Students in technology-related programs want to pursue careers in data science, machine learning, or artificial intelligence.
- Anyone who loves artificial intelligence
Here is the download links
https://www.udemy.com/course/tensorflow-2/
https://drive.google.com/file/d/1Ov3_VDnvmBDXLNdApWaDKU8_Xl6oigqb/view?usp=sharing
copy the link and past it in url
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