Ementorhub

What you'll learn

Build neural networks from scratch and apply them to real-world datasets like MNIST.

Apply back-propagation for optimizing neural network models and understand computational graphs.

Utilize L1, L2, drop-out regularization, and decision tree pruning to reduce model overfitting.

Implement convolutional neural networks (CNNs) and tensors using PyTorch for image and audio processing.

Skills you'll gain

Deep Learning

Computer Vision

PyTorch (Machine Learning Library)

Machine Learning

Decision Tree Learning

Machine Learning Algorithms

Image Analysis

Artificial Neural Networks

Supervised Learning

There are 5 modules in this course

What makes this course unique is its emphasis on building neural networks from scratch, allowing learners to grasp the intricate details of model design and training. Additionally, the course covers computational graphs, activation and loss functions, and how to efficiently utilize GPUs for faster computation. Learners will also delve into CNNs for image and audio processing, gaining insights into cutting-edge applications in these fields. By completing this course, learners will develop advanced skills in neural network design, model regularization, and the use of PyTorch for deep learning tasks—empowering them to tackle complex machine learning challenges with confidence.

Multilayer Artificial Neural Networks

Model Regularization

PyTorch

Convolutional Neural Networks