This Course is a Deep Dive into Deep Learning Fundamentals:
It’s more than 5 hours of deep learning lectures and working code, aimed at those with some prior Python experience. It’s a deep dive into the fundamentals of neural networks and convolutional neural networks using Keras and TensorFlow.
It is not an introduction to Keras or TensorFlow. If you are already familiar with the basics of machine learning and neural networks, you’ll get the most value from this course by learning how to implement deep learning techniques from scratch, without using high-level libraries such as Keras or PyTorch.
This course focuses on building models for computer vision, text analysis, and forecasting using TensorFlow 2.0. You will also learn how to train your models super efficiently using Google Colab – a training environment based on the cloud that allows you to work with GPUs for free!
This Course is a Deep Dive into Deep Learning Fundamentals:
This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. We will explore various models and discuss pros and cons of each architecture. We will also look at practical aspects of deep learning including how to build, monitor, and iterate these models in a production environment.
We will cover the following topics in this course:
* Fully Connected Feedforward Neural Networks
* Convolutional Neural Networks (CNNs)
* Recurring Neural Networks (RNNs)
* Autoencoders & Generative Adversarial Networks (GANs)
* Transfer Learning & Multi-task Learning
* Model Building, Training & Monitoring
Deep learning is all the rage right now. It powers many of the services we use daily: Google Translate and Facebook’s style transfer app are just two examples of what’s possible when you give machines the ability to understand the world in ways similar to humans.
If you’ve ever been curious about deep learning but didn’t know where to start, this course is for you. We’ll begin with a broad overview of deep learning, then move on to hands-on tutorials in TensorFlow (Google’s open source machine learning library) that cover image classification, image similarity, sequence-to-sequence models for machine translation, generative adversarial networks, and reinforcement learning.
This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. back
This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. We will explore architectures like Inception and Xception and then apply them to image classification problems. We will also cover advanced topics like neural style transfer, generative adversarial networks, and reinforcement learning. By the end of the course you will have gained the practical knowledge needed to implement your own models in PyTorch.
The deep learning course CS 230 has launched! You can check out the syllabus, and there are a few blog posts about the class. This is a deep dive into the practical details of deep learning that fast.ai doesn’t cover, with very little math and theory. We’ll start with an introduction to machine learning and then move on to techniques such as transfer learning, generative models, neural style transfer, and reinforcement learning. The course will be taught by Andrew Ng, who has taught machine learning for over 20 years at Stanford, on Coursera and Baidu, and at Google; Kian Katanforoosh, who has TAed CS 231n several times and is currently a teaching assistant for CS 230; and you (the students) will also play a big role in mentoring each other. If you’re interested in taking the course after watching these videos, we hope you’ll sign up for the waitlist on our website.
These videos have been recorded by Chris Piech and his team at Stanford — Chris was one of my students when I taught CS 229 Deep Learning at Stanford last year.
With this course, you will learn the foundations of deep learning. When you finish this class, you will:
– Understand the major technology trends driving Deep Learning
– Be able to build, train and apply fully connected deep neural networks
– Know how to implement efficient (vectorized) neural networks
– Understand the key parameters in a neural network’s architecture
This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description. So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions.
Deep Learning is one of the hottest and most disruptive new technologies in recent memory. It is being adopted by major tech companies around the world, including Google, Facebook, Microsoft, IBM, and Apple.
It’s impact is also felt far beyond the tech sector. For example, deep learning is used to train self-driving cars and predict health care outcomes.
Every day new breakthroughs are announced that push deep learning forward. However, there are very few resources available to help beginners get started with deep learning.
This course aims to change that by providing students with a solid foundation in deep learning so they can better understand the latest research and apply deep learning at their own organization or startup.