मुफ्त ऑनलाइन कोर्स Deep Learning Complete Course: ANN, CNN, RNN and Transformers

कोर्स का विवरण

Deep Learning Complete Course: ANN, CNN, RNN and Transformers is a free online program in Information Technology, designed for learners who want a clear, end-to-end pathway through modern neural networks and the ideas that power today’s AI systems. It starts by building strong intuition around what deep learning is, how it differs from machine learning, and where it is used in real products and research.

You will progress from core building blocks such as perceptrons and multilayer perceptrons to practical training concepts like forward propagation, backpropagation, gradient descent variants, and common loss functions. Along the way, you will learn how to improve model performance with techniques such as feature scaling, early stopping, dropout, regularization, weight initialization, batch normalization, and hyperparameter tuning using Keras tooling.

The course then moves into specialized architectures. For computer vision, it explains convolution, padding, pooling, classic CNN designs, and training details including CNN backpropagation. You will also explore transfer learning, data augmentation, and working with pretrained models, with project-style practice such as image classification.

For sequence learning, it covers why recurrent networks are needed, how RNNs work, and how to handle real-world challenges like long-term dependencies, leading into LSTMs, GRUs, bidirectional and stacked variants, and sequence-to-sequence encoder–decoder designs. Finally, it ties the evolution of language models to the transformer architecture, breaking down attention, self-attention, scaled dot-product attention, multi-head attention, positional encoding, layer normalization, masking, cross-attention, and inference.

With a strong balance of theory, intuition, and hands-on implementation using Keras and TensorFlow, this course is a practical choice for anyone aiming to build, tune, and deploy deep learning models across vision and NLP.