Are you interested in learning about Deep Learning? Jovian and FreeCodeCamp are hosting a free 6-week live course on our YouTube channel, starting Saturday, November 20th at 9:30 AM PST.
Passively watching a video is often not enough to learn a software concept. You need to be able to ask questions and build real projects. That is exactly what you will be able to do in the course “Deep Learning with PyTorch: Zero to GANs”.
This is an online course intended to provide a coding-first introduction to deep learning using the PyTorch framework. The course takes a hands-on coding-focused approach and will be taught using live interactive Jupyter notebooks, allowing students to follow along and experiment.
This course is taught by Aakash N S. He is the co-founder and CEO of Jovian.ml, a project management and collaboration platform for machine learning.
Theoretical concepts will be explained in simple terms using code. Students will receive weekly assignments, work on a project with real-world datasets and participate in a private data science competition to test their skills. Upon successful completion of the course, students will receive a certificate of completion.
This is a beginner-friendly course, and no prior knowledge of data science, machine learning or deep learning is assumed. It is preferable to have some background in the following areas:
- Programming knowledge, preferably in Python
- Basics of linear algebra (vectors, matrices, dot products)
- Basics of calculus (differentiation, geometric interpretation of derivative)
Course duration: Nov 21, 2020 – Jan 2, 2021
“Deep Learning with PyTorch: Zero to GANs” is a beginner-friendly online course offering a practical and coding-focused introduction to deep learning using the PyTorch framework. Enroll now to start learning.
- Watch live hands-on tutorials on YouTube
- Train models on cloud Jupyter notebooks
- Build an end-to-end real-world course project
- Earn a verified certificate of accomplishment
Add the lecture schedule to your calendar (English, Hindi) and browse the Course Community Forum.
Lesson 1 – PyTorch Basics and Gradient Descent
- PyTorch basics: tensors, gradients, and autograd
- Linear regression & gradient descent from scratch
- Using PyTorch modules: nn.Linear & nn.functional
Assignment 1 – All About torch.Tensor
- Explore the PyTorch documentation website
- Demonstrate usage of some tensor operations
- Publish your Jupyter notebook & share your work
Lesson 2 – Working with Images and Logistic Regression
- Training-validation split on the MNIST dataset
- Logistic regression, softmax & cross-entropy
- Model training, evaluation & sample predictions
Assignment 2 – Train Your First Model
- Download and explore a real-world dataset
- Create a linear regression model using PyTorch
- Train multiple models and make predictions
Lesson 3: Training Deep Neural Networks on a GPU
- Multilayer neural networks using nn.Module
- Activation functions, non-linearity & backprop
- Training models faster using cloud GPUs
Assignment 3 – Feed Forward Neural Networks
- Explore the CIFAR10 image dataset
- Create a pipeline for training on GPUs
- Hyperparameter tuning & optimization
Lesson 4: Image Classification with Convolutional Neural Networks
- Working with 3-channel RGB images
- Convolutions, kernels & features maps
- Training curve, underfitting & overfitting
Lesson 5: Data Augmentation, Regularization, and ResNets
- Adding residual layers with batchnorm to CNNs
- Learning rate annealing, weight decay & more
- Training a state-of-the-art model in 5 minutes
Lesson 6: Image Generation using Generative Adversarial Networks (GANs)
- Generative modeling and applications of GANs
- Training generator and discriminator networks
- Generating fake digits & anime faces with GANs
Course Project
- Discover & explore a large real-world dataset
- Train a convolutional neural network from scratch
- Document, present, and publish your work online
Certificate of Accomplishment
Earn a verified certificate of accomplishment (sample) for FREE by completing all weekly assignments and the course project. The certificate can be added to your LinkedIn profile, linked from your Resume, and downloaded as a PDF.
Course Prerequisites
- Programming basics (functions & loops)
- Linear algebra basics (vectors & matrices)
- Calculus basics (derivatives & slopes)
- No prior knowledge of deep learning required
Instructor – Aakash N S
Aakash N S is the co-founder and CEO of Jovian. Previously, Aakash has worked as a software engineer (APIs & Data Platforms) at Twitter in Ireland & San Francisco and graduated from the Indian Institute of Technology, Bombay. He’s also an avid blogger, open-source contributor, and online educator.
Jovian Mentorship Program
Get access to a private Slack group with the course team, attend weekly office hours on Zoom, and get 1-on-1 guidance for your project by joining the Jovian Data Science Mentorship Program. This is a limited and paid program designed to help you get the most out of this course. Apply here: https://jovian.ai/mentorship .
