Top Deep Learning Courses To Try In 2024

Top Deep Learning Courses To Try In 2024

Deep Learning Specialization

The Deep Learning Specialization equips you with the skills to build and optimize neural networks using Python and TensorFlow. It covers architectures like CNNs, RNNs, LSTMs, and Transformers, allowing learners to apply these skills to real-world AI cases.

TensorFlow Developer Professional Certificate

This course teaches how to build and train neural networks using TensorFlow through a hands-on program. It helps you gain skills to create AI-powered applications, prepare for the Google TensorFlow Certificate exam, and apply your knowledge to real-world projects.

Introduction to Deep Learning & Neural Networks with Keras

This course introduces deep learning and compares it to artificial neural networks. It covers various models, teaching unsupervised models like autoencoders and restricted Boltzmann machines and supervised models like CNNs and recurrent networks.

TensorFlow 2 for Deep Learning Specialization

This specialization allows machine learning researchers and practitioners to develop practical TensorFlow skills. It covers building, training, and evaluating models, customizing workflows with TensorFlow’s lower-level APIs, and developing probabilistic models using the TensorFlow Probability library.

NYU Deep Learning

This course covers deep learning history, neural networks, gradient descent, and backpropagation. It includes practical implementations using PyTorch, covering ConvNets, RNNs, autoencoders, GANs, transformers, and graph neural networks.

Intro to Deep Learning with PyTorch

This course teaches deep learning basics and how to build neural networks using PyTorch. Learners work on practical projects like image classification, style transfer, and text generation. The syllabus includes neural networks, CNNs, RNNs, and deploying models.

Practical Deep Learning For Coders

This course covers how to set up a GPU server and create deep learning models for computer vision, NLP, and recommendation systems. It includes CNNs, RNNs, and their practical applications.

Probabilistic Deep Learning with TensorFlow 2

This course delves into the probabilistic aspect of deep learning using TensorFlow. It focuses on handling uncertainty in real-world datasets and teaches how to develop probabilistic models with TensorFlow Probability.

Machine Learning with Python: From Linear Models to Deep Learning

This course teaches machine learning principles and algorithms for making predictions from training data. It covers topics like representation, over-fitting, regularization, clustering, classification, reinforcement learning, SVMs, and neural networks.

Deep Learning Applications for Computer Vision

This course teaches Computer Vision, starting with classic approaches and then applying Deep Learning methods to the same problems. It explores modern machine learning tools covering topics like image classification, object detection, segmentation, facial recognition, and pose estimation.

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