Gain a clear understanding of the intuition behind key Deep Learning architectures, including Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Self-Organizing Maps, Boltzmann Machines, and Autoencoders, and learn how to apply each of them effectively in practice.
₹7,000.00
Deep Learning & AI
Learn to build Deep Learning models in Python with guidance from experienced Machine Learning and Data Science experts, complete with...
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Course content
Curriculum
- 6 Sections
- 42 Lessons
- 20 Weeks
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- Introduction to Python0
- Introduction to Logistic Regression0
- Introduction to Artificial Neural Network11
- 3.1History of Neural networks and Deep Learning.
- 3.2How Biological Neurons work?
- 3.3Growth of biological neural networks.
- 3.4Diagrammatic representation: Logistic Regression and Perceptron.
- 3.5Multi-Layered Perceptron (MLP).
- 3.6Notation.
- 3.7Training a single-neuron model.
- 3.8Backpropagation.
- 3.9Activation functions.
- 3.10Vanishing Gradient problem.
- 3.11Bias-Variance tradeoff.
- Deep Multi-layer perceptrons11
- 4.1Deep Multi-layer perceptrons:1980s to 2010s
- 4.2Dropout layers & Regularization.
- 4.3Rectified Linear Units (ReLU).
- 4.4Weight initialization.
- 4.5Batch Normalization.
- 4.6Optimizers:Hill descent in 3D and contours.
- 4.7Adam
- 4.8Which algorithm to choose when?
- 4.9Gradient Checking and clipping
- 4.10Softmax and Cross-entropy for multi-class classification.
- 4.11How to train a Deep MLP?
- Convolutional Neural Network13
- 5.1Biological inspiration: Visual Cortex
- 5.2Convolution:Edge Detection on images.
- 5.3Convolution:Padding and strides
- 5.4Convolution over RGB images.
- 5.5Convolutional layer.
- 5.6Max-pooling.
- 5.7CNN Training: Optimization
- 5.8Receptive Fields and Effective Receptive Fields
- 5.9ImageNet dataset.
- 5.10Data Augmentation.
- 5.11Convolution Layers in Keras
- 5.12AlexNet
- 5.13VGGNet
- Recurrent Neural Network7
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₹7,000.00
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