Contrastive Learning of General-Purpose Audio Representations.

This post is a short summary and steps to implement the following paper:
Learning of General-Purpose Audio Representations
The objective of this paper is to learn self-supervised general-purpose audio representations using Discriminative Pre-Training. The authors train a 2D CNN EfficientNet-B0 to transform Mel-spectrograms into 1D-512 vectors. Those representations are then transferred to other tasks like Speaker Identification or Bird Song detection.

#deep-learning #machine-learning #audio #pytorch #unsupervised-learning

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Contrastive Learning of General-Purpose Audio Representations.

This post is a short summary and steps to implement the following paper:
Learning of General-Purpose Audio Representations
The objective of this paper is to learn self-supervised general-purpose audio representations using Discriminative Pre-Training. The authors train a 2D CNN EfficientNet-B0 to transform Mel-spectrograms into 1D-512 vectors. Those representations are then transferred to other tasks like Speaker Identification or Bird Song detection.

#deep-learning #machine-learning #audio #pytorch #unsupervised-learning

Google Reveals "What is being Transferred” in Transfer Learning

Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community — What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question.

The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others.

#developers corner #learn transfer learning #machine learning #transfer learning #transfer learning methods #transfer learning resources

Deep Reinforcement Learning and Representation Learning

One major problem of current state-of-the-art Reinforcement Learning (RL) algorithms is still the need for millions of training examples to learn a good or near-optimal policy to solve the given task. This plays especially a critical role for real-world applications in the industry be it for robotics or other complex optimization problems for decision making or optimal control.
Due to these problems, engineers and researchers are looking for ways to improve this sample-inefficiency to increase the speed of learning and the need for gathering millions of expensive training examples.

#reinforcement-learning #artificial-intelligence #representation-learning #machine-learning

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

Visit Blog- https://www.xplace.com/article/8743

#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert

Intro to Reinforcement Learning: Temporal Difference Learning, SARSA Vs. Q-learning

Reinforcement learning (RL) is surely a rising field, with the huge influence from the performance of AlphaZero (the best chess engine as of now). RL is a subfield of machine learning that teaches agents to perform in an environment to maximize rewards overtime.

Among RL’s model-free methods is temporal difference (TD) learning, with SARSA and Q-learning (QL) being two of the most used algorithms. I chose to explore SARSA and QL to highlight a subtle difference between on-policy learning and off-learning, which we will discuss later in the post.

This post assumes you have basic knowledge of the agent, environment, action, and rewards within RL’s scope. A brief introduction can be found here.

The outline of this post include:

• Temporal difference learning (TD learning)
• Parameters
• QL & SARSA
• Comparison
• Implementation
• Conclusion

We will compare these two algorithms via the CartPole game implementation. This post’s code can be found here :QL code ,SARSA code , and the fully functioning code . (the fully-functioning code has both algorithms implemented and trained on cart pole game)

The TD learning will be a bit mathematical, but feel free to skim through and jump directly to QL and SARSA.

#reinforcement-learning #artificial-intelligence #machine-learning #deep-learning #learning