[2026] Tensorflow 2: Deep Learning & Artificial Intelligence

<div>Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.</div><div><br></div><div>Welcome to Tensorflow 2.0!</div><div><br></div><div>What an exciting time. It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version.</div><div><br></div><div>Tensorflow is Google's library for deep learning and artificial intelligence.</div><div><br></div><div>Deep Learning has been responsible for some amazing achievements recently, such as:</div><div><br></div><div><ul><li>Generating beautiful, photo-realistic images of people and things that never existed (GANs)</li><li><span style="font-size: 1rem;">Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)</span></li><li><span style="font-size: 1rem;">Self-driving cars (Computer Vision)</span></li><li><span style="font-size: 1rem;">Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)</span></li><li><span style="font-size: 1rem;">Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning)</span></li></ul></div><div><span style="font-size: 1rem;">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.</span></div><div><br></div><div>In other words, if you want to do deep learning, you gotta know Tensorflow.</div><div><br></div><div><span style="font-size: 1rem;">This course is for beginner-level students all the way up to expert-level students. How can this be?</span></div><div><br></div><div>If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.</div><div><br></div><div>Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data).</div><div><br></div><div>Current projects include:</div><div><br></div><div><ul><li>Natural Language Processing (NLP)</li><li><span style="font-size: 1rem;">Recommender Systems</span></li><li><span style="font-size: 1rem;">Transfer Learning for Computer Vision</span></li><li><span style="font-size: 1rem;">Generative Adversarial Networks (GANs)</span></li><li><span style="font-size: 1rem;">Deep Reinforcement Learning Stock Trading Bot</span></li></ul></div><div><br></div><div>Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions.</div><div><br></div><div>This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).</div><div><br></div><div>Advanced Tensorflow topics include:</div><div><br></div><div><ul><li>Deploying a model with Tensorflow Serving (Tensorflow in the cloud)</li><li><span style="font-size: 1rem;">Deploying a model with Tensorflow Lite (mobile and embedded applications)</span></li><li><span style="font-size: 1rem;">Distributed Tensorflow training with Distribution Strategies</span></li><li><span style="font-size: 1rem;">Writing your own custom Tensorflow model</span></li><li><span style="font-size: 1rem;">Converting Tensorflow 1.x code to Tensorflow 2.0</span></li><li><span style="font-size: 1rem;">Constants, Variables, and Tensors</span></li><li><span style="font-size: 1rem;">Eager execution</span></li><li><span style="font-size: 1rem;">Gradient tape</span></li></ul></div><div><br></div><div><span style="font-size: 1rem;">Instructor's Note: This 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 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.</span></div><div><br></div><div><span style="font-size: 1rem;">Thanks for reading, and I’ll see you in class!</span></div><div><br></div><div><span style="font-size: 1rem;">WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:</span></div><div><br></div><div>Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)</div><div><br></div><div>UNIQUE FEATURES</div><div><br></div><div><ul><li>Every line of code explained in detail - email me any time if you disagree</li><li><span style="font-size: 1rem;">No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch</span></li><li><span style="font-size: 1rem;">Not afraid of university-level math - get important details about algorithms that other courses leave out</span></li></ul></div>

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
• Earn the Tensorflow Developer Certificate
• Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion