??WWWH: Transfer learning, fine-tuning, Multi-tasking learning, Federated learning, and Meta learning??

Greetings to all of you! I am back, and this time I discussed a broad review of various machine learning and deep learning methodologies. Knowing the methodologies is crucial for any developer or engineer.

First, we will short discuss about major real time approaches of machine learning and deep learning?

Thus, the core problem in the graphic above is beyond the capabilities of any developer or engineer to address. However, we shall talk about the dilemma that follows the selection. then carefully consider what kind of instructional technique or methods work best for this particular issue. In regard to this, I will go into great detail about each strategy and its advantages and disadvantages.

WHAT, WHY, WHERE, HOW?

1. Transfer Learning

What: Transfer learning involves leveraging knowledge from solving one problem and applying it to a related but different problem.

Why: It helps improve model performance on a target task, especially when limited data is available, by transferring knowledge from a pre-trained model. How: Start with a pre-trained model and fine-tune it on the target task using a smaller dataset.

Where: Transfer learning is widely used in computer vision, natural language processing, and other domains where pre-trained models are available.

Key points:

• No Gradient flow
• Neurons from pre-trained model
• No Appended neuron

2. Fine-tuning

What: Fine-tuning is a specific application of transfer learning where a pre-trained model is adjusted to perform a new task.

Why: It allows for efficient adaptation of pre-trained models to new tasks by updating their parameters.

How: Initialize the model with pre-trained weights, then continue training on the new task-specific data with small adjustments.

Where: Fine-tuning is commonly used in deep learning for tasks like image classification, object detection, and natural language understanding.

Key points:

• Gradient flow
• Full pre-trained network
• Appended network

3. Multitask Learning

What: Multitask learning involves training a single model to perform multiple tasks simultaneously.

Why: It allows the model to leverage shared representations across tasks, improving overall performance.

How: Train the model on multiple tasks jointly, with shared parameters across tasks.

Where: Multitask learning is applied in various domains, including natural language processing, computer vision, and speech recognition.

Key points:

• Gradient flow
• Shared network
• Task-specific branches

4. Federated Learning

What: Federated learning is a decentralized approach where multiple devices collaboratively train a global model while keeping their data local.

Why: It addresses privacy concerns by allowing model training without sharing raw data.

How: Devices compute model updates locally, which are then aggregated to update the global model.

Where: Federated learning is used in scenarios where data privacy is crucial, such as healthcare, finance, and IoT.

Key points:

Types:

• Centralized federating learning
• Decentralized federating learning
• Heterogeneous federating learning

Steps:

1. Initializing
2. Client selection
3. Local training
4. Aggregation
5. Global model adapt
6. local model fine-tune
7. Repeat Repeat…….

Algorithm: FedSGD, FedAvg, FedDyn

5. Meta-Learning

What: Meta-learning, or learning to learn, involves training models on a variety of tasks so that they can quickly adapt to new tasks.

Why: It enables fast adaptation to new tasks with minimal additional training.

How: Train the model on diverse tasks to learn a learning algorithm that generalizes well.

Where: Meta learning is applied in few-shot learning scenarios and other situations where rapid adaptation to new tasks is required.

Key points:

• Learning to adapt
• Few shot (or) Zero shot learn
• Task Similarity

Three approaches:

• Model-based
• Metric based
• optimization-based approaches

Two levels of learning data:

• Meta Training data
• Meta Test data

Steps:

• Meta train (Task generation, Task sampling, Training)
• Meta testing (Task adaptation, Fine-tuning, Inference)
• Evaluation
• Meta-learning objective
• Optimization
• Hyper-parameter tuning
• Regularization
• Model Architecture
• Dataset preparation
• Iterative process

Algorithm: Torch-meta, Learn2learn, Meta-datasets

Federating learning and meta-learning are popular methodologies in the industry. production-based methods in ML and DL initiatives

Comparison of All techniques

• Data Requirement: Transfer learning and fine-tuning require less data for the target task compared to training from scratch. Multitask learning can benefit from shared representations across tasks, reducing the need for extensive labeled data for each task. Federated learning allows training on decentralized data sources without centralizing data, thus addressing privacy concerns. Meta learning aims to learn from diverse tasks to generalize better to new tasks with limited data.
• Model Adaptation: Transfer learning and fine-tuning involve adapting a pre-trained model to a new task, while multitask learning trains a single model on multiple tasks simultaneously. Federated learning distributes model training across multiple devices while keeping data local. Meta learning focuses on learning a learning algorithm to quickly adapt to new tasks.
• Computational Efficiency: Transfer learning and fine-tuning are computationally efficient since they leverage pre-trained models. Multitask learning can improve efficiency by jointly learning multiple tasks. Federated learning distributes computation across devices, reducing the need for centralized servers. Meta learning requires training on diverse tasks but can be efficient in adapting to new tasks.
• Privacy Concerns: Transfer learning and fine-tuning may involve sharing pre-trained models but not necessarily data. Multitask learning may involve sharing data across tasks within a single model. Federated learning allows training on decentralized data sources without sharing raw data. Meta learning learns from diverse tasks but may require sharing task data during training.

According to my experience and understanding of deep learning and machine learning. First step in determining the right problem and developing a solution for it. Not every step makes advantage of deep learning. When a problem is straightforward, we just build a solution in line with it. For example, in machine learning, linear regression suffices; elaborate models are not needed. Select the approaches by the situation and the needs of the client. The process is made simple by one of the greatest engineers or developers. And among the greatest learning strategies are federating learning and meta-learning from a major industry and privacy standpoint. small or medium-sized business, we’ll attempt to apply our standard techniques?—?transfer learning, fine-tuning. Additionally, we will use multitasking techniques to improve training and achieve high efficiency and accuracy.

Nowadays, everyone believes that generative AI is superior to everything. However, in a real-time way, we want to achieve 60 to 70 percent efficiency in the output obtained from the firm. However, techniques are equally crucial for solution-based approaches and product creation. Therefore, don’t worry in GenAi; instead, keep yourself informed and go more into conventional methods as needed. If GenAi is used to address problems other than this one, it is more crucial to identify the issue and rely on the answer and approach. Grow in line with that. Discover your lot and take appropriate advantage of it.

In my upcoming piece, I’ll attempt to experiment with perspective.

I appreciate you coming, people. Please leave a comment if this article contains any questions or errors. or let’s connect via kaggle Conversation or LinkedIn.

Social network?: linkedin, Kaggle, Github