Tensor2Tensor to accelerate training of complex machine learning models
This article mainly focuses on the Tensor2Tensor library and to understand the dynamic abilities to handle and process complex models.
This article mainly focuses on the Tensor2Tensor library and to understand the dynamic abilities to handle and process complex models.
Gradient Descent is primarily used in Neural Networks for unsupervised learning
Hyperband is a framework for tuning hyperparameters
This article explains the concept of cosine similarity and how it is used as a metric for evaluation of data points in various applications.
The log loss function measures the cross entropy of the error between two probability distributions
Deconvolution simply reverses the process of convolution
This article has covered the steps to create a machine learning model using Big Query ML.
This article is about the gradient descent algorithm and the different alternatives that can be used instead of the gradient descent algorithm.
SARSA is one of the reinforcement learning algorithm which learns from the current set os states and actions and learns from the same target policy.
NP-hard is a set of sequentially decision problems which are hard to solve in a time frame.
In this article, we’ll explore different libraries and frameworks for reinforcement learning using JAX.
Embedding Q-Learning with Policy network would generate recommendation
This article is about the limitations of tree based machine learning models and the conditions that forbid the use of tree based models in machine learning.
Gradient ascent maximizes the loss function of the algorithm
Debiased ML combines bias correction and sample splitting to compute scalar summaries.
How to develop deep learning models in edge devices? Here is the answer
The 21-month online programme includes 11 hands-on projects.
Using pre-trained machine learning models to evaluate parameters
This article is about the transfer learning technique and how to use it in time series forecasting problems.
This article briefs on the various ways a machine learning model learns from the data with uncertainty and why is probabilistic machine learning the best.
The need for testing and validating data and machine learning models
Deploy and demonstrate the capabilities of DL models with Streamlit
Logistic regression is a simple classification algorithm used to model the probability of a discrete outcome from a given set of input variables.
Databricks offers a Unified Data Analytics Platform.
DataRobot, powered with the recent open-source algorithms and loaded with on-premise AI services, offers features to build and deploy ML models with ease.
The workshop covered various initiatives and projects launched by Intel®, alongside deep-diving into Intel® Optimisation for TensorFlow to enhance the performance on Intel platforms and more.
Intel® Extension for PyTorch* optimises for both imperative mode and graph mode.
The data science/analytics jobs figure for April 2022 witnessed a 30.1% increase in open jobs compared to last year (April 2021).
The summit will feature talks, workshops, paper presentations, exhibitions and hackathons.
Curriculum learning is also a type of machine learning that trains the model in such a way that humans get trained using their education system
Join the forefront of data innovation at the Data Engineering Summit 2024, where industry leaders redefine technology’s future.
© Analytics India Magazine Pvt Ltd & AIM Media House LLC 2024
The Belamy, our weekly Newsletter is a rage. Just enter your email below.