With the TensorFlow 2.0 Course, you will learn about an open-source, all-inclusive machine-learning that is contained in this TensorFlow 2.0 Course, You will learn about the adaptable ecosystem of TensorFlow’s tools, libraries, and community resources.
What you’ll discover
- Deep neural networks (DNNs) and artificial neural networks (ANNs) (DNNs)
- Calculate Stock Returns
- Computer vision with time series forecasting
- Building a Deep Reinforcement Learning Market Trading GAN bots (Generative Adversarial Networks)
- Recognizing Images using Recommender Systems
- Neural networks with convolutions (CNNs)
- Continuous Neural Networks (RNNs)
- Utilize a RESTful API with Tensorflow Serving to serve your model.
- To export your model for embedded and mobile (Android, iOS) devices, use Tensorflow Lite.
- To parallelize learning, use Tensorflow’s distribution strategies.
- Gradient tape, low-level Tensorflow, and creating your own unique models
- Deep Learning and Natural Language Processing (NLP)
- Use Code Transfer to illustrate Moore’s Law and learn how to design cutting-edge image classifiers.
Requirements
- Know how to code in Python and Numpy
- For the theoretical parts (optional), understand derivatives and probability
Description
Greetings from Tensorflow 2.0!
What a momentous time. Since Tensorflow’s initial release, which was about four years ago, the library has developed to its current official second version.
Google’s deep learning and artificial intelligence library are called Tensorflow. creating stunning, photorealistic representations of objects and people that have never been (GANs)
defeating global champions in the strategy games Go and CS: GO and Dota 2 (Deep Reinforcement Learning)
Automated translation and speech recognition (Natural Language Processing)
Even making videos of individuals acting and saying in ways they never would (DeepFakes – a potentially nefarious application of deep learning)
The most well-known deep learning library in the world, Tensorflow, was created by Google, whose parent firm Alphabet just overtook Apple as the richest corporation in the world in terms of cash (just a few days before I wrote this). It is the preferred library for many businesses engaged in AI and machine learning.
In other words, you must be familiar with Tensorflow if you wish to perform deep learning.
Beginner-level learners all the way up to experts should enroll in this TensorFlow 2.0 Course, How is this possible?
If you recently completed my free Numpy requirement, you are prepared to start using it right away. Beginning with some very fundamental machine learning models, we will work our way up to cutting-edge ideas.
All of the key deep learning architectures, including Deep Neural Networks, Convolutional Neural Networks (for image processing), and Recurrent Neural Networks, will be covered along the way (sequence data).
Current projects include:
- Natural Language Processing (NLP)
- Recommender Systems
- Transfer Learning for Computer Vision
- Generative Adversarial Networks (GANs)
- Deep Reinforcement Learning Stock Trading Bot
You’ll learn how to upgrade your old code to use Tensorflow 2.0 even if you’ve previously finished all of my other courses.
Additionally, this TensorFlow 2.0 Course, covers brand-new projects that have never been done before, such as time series forecasting and market prediction.
This TensorFlow 2.0 Course, is intended for individuals who want to learn quickly, but it also offers “in-depth” sections if you want to go a little deeper into the subject (like what is a loss function, and what are the different types of gradient descent approaches).
- Deploying a model with Tensorflow Serving (Tensorflow in the cloud)
- Deploying a model with Tensorflow Lite (mobile and embedded applications)
- Distributed Tensorflow training with Distribution Strategies
- Writing your own custom Tensorflow model
- Converting Tensorflow 1. x code to Tensorflow 2.0
- Constants, Variables, and Tensors
- Eager execution
- Gradient tape
Instructor’s Note: This TensorFlow 2.0 Course focuses on breadth rather than depth, with less theory in favor of building more cool stuff.
If you are looking for a more theory-dense TensorFlow 2.0 Course, this is not it. Generally, for each of these topics (recommender systems, natural language processing, reinforcement learning, computer vision, GANs, etc.) I already have courses singularly focused on those topics.
Thanks for reading, and I’ll see you in class!
WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:
- Check out the lecture “Machine Learning and AI Prerequisite Roadmap” (available in the FAQ of any of my courses, including the free Numpy course)
Who this course is for:
- Beginners to advanced students who want to learn about deep learning and AI in Tensorflow 2.0
