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A comprehensive guide to Deep Learning covering ANNs, CNNs, RNNs, GANs, and Auto-encoders. Aligned with the Vidyasagar University 7th Semester syllabus, enhanced with practical foundations and real-world projects.
Essential Mathematics, Machine Learning basics, and environment setup required before diving into Deep Learning concepts.
A quick and practical review of the essential Linear Algebra (vectors, matrices, tensors) and Calculus (derivatives, gradients) concepts required to master Deep Learning.
Before building complex Neural Networks, you must understand the foundational concepts of Machine Learning, including Supervised vs. Unsupervised learning, data splitting, and the critical Bias-Variance tradeoff.
Get your hands dirty by setting up a Python Deep Learning environment. Learn how to import TensorFlow and manipulate Tensors, the foundational data structures of neural networks.
Evolution of Deep Learning, Artificial Neural Networks (ANN), Perceptrons, Feed-Forward Networks, and Back-Propagation.
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CNN architecture, Activation functions, Pooling, Training in TensorFlow, and popular architectures like VGG and ResNet.
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Sequential data analysis, Back Propagation Through Time (BPTT), LSTMs, GRUs, and Bidirectional Networks.
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Generative vs Discriminative models, GAN architecture (Generator & Discriminator), and real-world applications.
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Encoder-Decoder architecture, data compression, reconstruction techniques, and Encoder-Decoder GAN hybrid models.
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Applying deep learning concepts to the real world through Transfer Learning and building an End-to-End Capstone Project.
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