Master PyTorch with this learning path, designed for those experienced in Python and machine learning. From tensor basics to advanced modeling, it includes practical exercises focused on real-world datasets, such as the wine dataset, enhancing your deep learning skills through PyTorch.
This learning path includes:
4 courses with bite-sized lessons and practices
18 engaging lessons in text and video formats
89 hands-on practices in our state-of-the art IDE
One-on-one guidance from Cosmo, our AI tutor
Step into the world of PyTorch, a leading library for deep learning and neural network development. This beginner-oriented course introduces the foundational building blocks of PyTorch, emphasizing tensors and their pivotal role in constructing neural networks. Through practical examples and exercises, you'll develop the skills to start building and experimenting with tensors in PyTorch.
Embark on a journey to understand and build simple neural networks using PyTorch. This course explores neural networks, including essential concepts like layers, neurons, activation functions, and training a model. You’ll grasp these elements through progressive, interlocking code examples, culminating in the construction and evaluation of a simple neural network model for binary classification.
Learn to model the Wine dataset with PyTorch in this detailed course. Start by preprocessing the data for PyTorch, then construct and train a multi-class classification model. Explore model evaluation with various metrics and plots to identify strengths and improvements. The course concludes with methods to save and deploy your model, maximizing PyTorch's features for practical application.
Explore advanced PyTorch techniques to boost model performance. Learn about regularization, dropout to avoid overfitting, batch normalization for stable and quick training, and efficient training through learning rate scheduling. Also, discover how to save the best model with checkpointing. Each concise module offers practical skills to improve your machine learning projects.
Our built-in AI guide and tutor, Cosmo, prompts you with challenges that are built just for you and unblocks you when you get stuck.
Embark on a comprehensive journey into the world of machine learning with this carefully curated learning path. Designed with python programmers and data scientists in mind who don't know much Machine Learning yet, this path starts with the fundamentals of Machine Learning using Sklearn and then progresses to advanced concepts in deep learning through Tensorflow. By the end of this course, you'll have a solid foundation in machine learning, along with the skills needed to build and optimize neural networks using Tensorflow.
Dive deep into the intricate universe of Artificial Intelligence with this in-depth learning path. This path is perfectly suited for those who wish to not only understand the theoretical aspects of Machine Learning algorithms but also wish to learn how to code these algorithms from scratch, without relying on common libraries such as Sklearn. You'll start with grasping the essence of Machine Learning, dissect the underlying principles, and then move on to implementing some of the most fundamental and crucial algorithms in ML all by yourself.
Dive into the world of unsupervised learning with this specialized path focusing on Clustering, an essential Machine Learning technique. Understand everything about Clustering from scratch, starting with data preprocessing, moving on to different clustering algorithms like K-means, DBSCAN, Agglomerative Hierarchical Clustering, and finally, mastering validation techniques to evaluate the performance of your models.