Tensorflow 2.0: Deep Learning and Artificial Intelligence
Tensorflow 2.0: Deep Learning and Artificial Intelligence
Machine Learning & Neural Networks for Computer Vision, Time Series Analysis, NLP, GANs, Reinforcement Learning, +More!
Created by Lazy Programmer Team, Lazy Programmer Inc. | 23 hours on-demand video course
Welcome to Tensorflow 2.0: Deep Learning and Artificial Intelligence Course. This course is for beginner-level students all the way up to expert-level students. It’s been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version. Tensorflow is Google’s library for deep learning and artificial intelligence.
Deep Learning has been responsible for some amazing achievements recently, such as:
- Generating beautiful, photo-realistic images of people and things that never existed (GANs)
- Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)
- Self-driving cars (Computer Vision)
- Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)
- Even creating videos of people doing and saying things they never did (DeepFakes – a potentially nefarious application of deep learning)
Tensorflow is the world’s most popular library for deep learning, and it’s built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.
Recommended Course by Lazy Programmers
Deep Learning: Convolutional Neural Networks in Python Best seller
Math 0-1: Matrix Calculus in Data Science & Machine Learning
Deep Learning Prerequisites: The Numpy Stack in Python (V2+) Best seller
What you’ll learn
- Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
- Predict Stock Returns
- Time Series Forecasting
- Computer Vision
- How to build a Deep Reinforcement Learning Stock Trading Bot
- GANs (Generative Adversarial Networks)
- Recommender Systems
- Image Recognition
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Use Tensorflow Serving to serve your model using a RESTful API
- Use Tensorflow Lite to export your model for mobile (Android, iOS) and embedded devices
- Use Tensorflow’s Distribution Strategies to parallelize learning
- Low-level Tensorflow, gradient tape, and how to build your own custom models
- Natural Language Processing (NLP) with Deep Learning
- Demonstrate Moore’s Law using Code
- Transfer Learning to create state-of-the-art image classifiers
- VIP Content: Build your own DeepDream Model
- VIP Content: Build your own Object Localization Model
