Certificate in Deep Learning: Essentials
-- ViewingNowThe Certificate in Deep Learning: Essentials is a comprehensive course designed to equip learners with the fundamental concepts and practical skills required to excel in the rapidly growing field of deep learning. This program covers key topics including neural networks, convolutional neural networks, recurrent neural networks, and deep reinforcement learning.
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⢠Introduction to Deep Learning: Understanding the basics of deep learning, its applications, and benefits.
⢠Neural Networks: Diving into the fundamentals of artificial neural networks, including perceptrons, activation functions, and backpropagation.
⢠Convolutional Neural Networks (CNNs): Learning about the architecture and use cases of Convolutional Neural Networks, primarily in computer vision.
⢠Recurrent Neural Networks (RNNs): Exploring Recurrent Neural Networks, their variations, and how they're applied for sequential data analysis.
⢠Long Short-Term Memory (LSTM) Networks: Delving into Long Short-Term Memory networks, focusing on handling long-range dependencies in sequences and use cases.
⢠Deep Learning Frameworks: Hands-on experience with popular deep learning frameworks such as TensorFlow, Keras, and PyTorch.
⢠Training and Optimizing Deep Learning Models: Understanding strategies for training deep learning models, including optimization techniques and hyperparameter tuning.
⢠Transfer Learning and Fine-Tuning: Learning how to leverage pre-trained models and fine-tuning them to solve specific tasks with limited data.
⢠Generative Adversarial Networks (GANs): Familiarizing with the concept of Generative Adversarial Networks, their applications, and challenges.
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