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Pages

• Introduction To Deep Learning
• Difference Between Ml And Dl
• Neural Networks Basics
• Perceptron And Activation Functions
• Feedforward Neural Networks
• Backpropagation Algorithm
• Loss Functions In Deep Learning
• Gradient Descent And Optimization
• Overfitting In Deep Learning
• Regularization Techniques
• Introduction To Tensorflow
• Introduction To Keras
• Building A Neural Network With Keras
• Convolutional Neural Networks Cnns
• Pooling Layers In Cnns
• Building Cnn With Keras
• Recurrent Neural Networks Rnns
• Lstm And Gru Networks
• Natural Language Processing Basics
• Word Embeddings And Word2vec
• Text Classification With Deep Learning
• Autoencoders Introduction
• Generative Adversarial Networks Gans
• Transfer Learning
• Using Pretrained Models
• Image Classification Project
• Object Detection Basics
• Deploying Deep Learning Models
• Deep Learning In Real World Applications
• Best Practices In Deep Learning