Moby Learn

Advanced Machine Learning

Elevate your skills with expert-level training on deep learning architectures, optimization techniques, and cutting-edge AI models.

🚀 Access Advanced Training

Why Advanced ML?

This course builds on fundamental concepts to show you how to design and optimize complex machine learning systems. You'll learn to implement state-of-the-art models like transformers and GANs while mastering critical topics like regularization and hyperparameter optimization.

80+

Advanced Topics

50+

Code Labs

import torch

from torchvision import datasets

model = Transformer( ... )

optimizer = Adam( ... )

Advanced Course Topics

Neural Network Architectures

Master the design and implementation of deep neural networks, including convolutional, recurrent, and hybrid architectures. Learn how to optimize model depth, width, and regularization for maximum performance.

  • • Convolutional Neural Networks (CNNs)
  • • Recurrent Neural Networks (RNNs)
  • • Transformer Architectures
  • • Autoencoders
  • • Generative Adversarial Networks
  • • Attention Mechanisms
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Advanced Optimization Techniques

Learn to implement advanced optimization algorithms like AdamW and lookback optimization. Understand learning rate scheduling strategies and second-order methods for faster model convergence.

  • • AdamW Optimizer
  • • Lookahead
  • • Learning Rate Schedulers
  • • Second-Order Methods
  • • Weight Decay
  • • Gradient Clipping
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🧠 Real-World Applications

Production-Grade

Image Recognition

Build state-of-the-art image recognition systems using advanced CNN architectures. Learn transfer learning strategies and performance optimization for real-world deployment.

Includes hands-on labs with ResNet, EfficientNet, and YOLO implementations
Model Compression

Transformer Optimization

Optimize massive language models using techniques like knowledge distillation, pruning, and quantization. Learn how to deploy compact model versions for mobile and edge computing.

Includes implementation of model distillation and pruning techniques
Research Grade

Generative AI

Develop advanced generative models including VAEs and GANs. Learn to create high-quality image generation systems and text generation models using current state-of-the-art techniques.

Includes Stable Diffusion and GPT-style architecture implementations

🛠 Interactive Learning Labs

Neural Network Implementation

Build and train neural networks using PyTorch

import torch import torch.nn
Python 3
1
class NeuralNet(torch.nn.Module):
2
def __init__(self):
super(NeuralNet, self).__init__()
self.flatten = torch.nn.Flatten()
self.layers = torch.nn.Sequential(
torch.nn.Linear(784, 1024),
torch.nn.ReLU(),
torch.nn.Linear(1024, 512),
torch.nn.ReLU(),
torch.nn.Linear(512, 10)
)
3
def forward(self, x):
x = self.flatten(x)
return self.layers(x)
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🔍 Advanced Optimization Techniques

AdamW Optimizer

Master the improved Adam optimizer version with decoupled weight decay. Learn how to implement it for better generalization in deep learning models.

// Sample implementation\noptimizer = optim.AdamW(model.parameters(), lr=0.1, weight_decay=1e-4)

Cosine Annealing

Learn to implement advanced learning rate scheduling techniques that dynamically adjust learning rates during training for improved model convergence.

# Example scheduler\nscheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=50, eta_min=1e-4)

Gradient Clipping

Implement techniques to prevent exploding gradients in RNN and deep architectures, ensuring stable model training and convergence.

torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=2)

K-FAC Optimization

Explore second-order optimization methods for faster convergence in neural network training using the Kronecker-Factored Approximate Curvature algorithm.

# K-FAC implementation requires additional dependencies\nimport kfac\n...\n

Your AI Career Toolkit

Master the most advanced machine learning techniques and prove your expertise with our industry-recognized certification program.

🎓 Certification

Show your mastery of advanced ML with an industry-recognized certification in your digital profile

🚀 Career Support

Access exclusive job listings and tech interviews with top ML employers using our platform

🤝 Community

Join elite study groups and collaborate on research with our most advanced learners