⭕️ Top 12 R packages for ML in 2020

🔹do not miss out this nice article!
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📌Via: @cedeeplearning

https://analyticsindiamag.com/top-12-r-packages-for-machine-learning-in-2020/

#machinelearning #AI
#r #R_language #math
#neuralnetworks #skill
#deeplearning #datascience

🔹 Fundamentals of Data Analytics
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📌Other social media: https://linktr.ee/cedeeplearning

#datasicence #analytics #machinelearning #math #skills #resume #datamining #course

📈 Data Analytics is rapidly becoming one of the most critical drivers for any decision-making, at an individual or a business level. At the heart of Data analytics, lies the fundamentals of statistics. This course will help you learn basic statistical concepts with practical problem solving and interpretation through application of the theoretical learnings.

🔹 You will learn fundamental statistical concepts, that are widely applicable in data analytics through course lessons and solving business cases.

🔹 You will then apply the knowledge gained to solve business problems through simulations using real data, validate your knowledge by answering quiz questions under each module and finally test your understanding by solving real problems under the Solve section.

🔹 At the end of this course, you should be able to understand data type and their representation, apply denoscriptive statistical measures to interpret data and make statistical inferences based on the data distribution and use of appropriate statistical tests.

⭕️ Prerequisite: Basic understanding of mathematics, especially algebra.

Sign up today! Link: https://bit.ly/2UUo62z

Answers to FAQs:
🔘 Due to high traffic, you might experience a little delay, but the system is working perfectly fine.
🔘 The field of 'referral code' is optional. You can successfully sign up without it.
🔘 The course is selfpaced.
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📌 Via: @cedeeplearning

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 17 Broadcasting in Python

Neural Networks and Deep Learning
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📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning
#broadcasting #python

⭕️ How to Avoid Data Leakage When Performing Data Preparation

🔹A naive approach to preparing data applies the transform on the entire dataset before evaluating the performance of the model. This results in a problem referred to as data leakage, where knowledge of the hold-out test set leaks into the dataset used to train the model. This can result in an incorrect estimate of model performance when making predictions on new data.
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📌 Via: @cedeeplearnig

https://machinelearningmastery.com/data-preparation-without-data-leakage/

#machinelearning #AI
#neuralnetworks #deeplearning
#datascience #preprocessing
#datamining

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 18 A Note on Python Numpy Vectors

Neural Networks and Deep Learning
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📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning
#numpy #python

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 19 Quick Tour of Jupyter iPython Notebooks

Neural Networks and Deep Learning
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #supervised_learning
#jupyter #ipython

🔹The 5 Basic Statistics Concepts Data Scientists Need to Know

Statistics can be a powerful tool when performing the art of Data Science (DS). From a high-level view, statistics is the use of mathematics to perform technical analysis of data. A basic visualisation such as a bar chart might give you some high-level information, but with statistics we get to operate on the data in a much more information-driven and targeted way. The math involved helps us form concrete conclusions about our data rather than just guesstimating.
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📌Via: @cedeeplearning

link: https://towardsdatascience.com/the-5-basic-statistics-concepts-data-scientists-need-to-know-2c96740377ae

#statistics #datascience
#machinelearning
#tutorial #AI #python
#deeplearning

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 20 Explanation of Logistic Regression's Cost Function

Neural Networks and Deep Learning
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📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #NeuralNeworks
#machinelearning #AI #coursera
#free #python #machinelearning #cost_function

🔹 Reinforcement Learning

Acme: A research framework for reinforcement learning

Github: https://github.com/deepmind/acme

Paper: https://arxiv.org/abs/2006.00979
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📌Via: @cedeeplearning

#deeplearning #machinelearning
#neuralnetworks #python #math
#statistics #reinforcement #Acme

⭕️ Fine-tuning ResNet with Keras, TensorFlow, and Deep Learning

In this tutorial, you will learn how to fine-tune #ResNet using #Keras, #TensorFlow, and Deep Learning.

https://www.pyimagesearch.com/2020/04/27/fine-tuning-resnet-with-keras-tensorflow-and-deep-learning/
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📌Via: @cedeeplearning

#machinelearning #AI
#deeplearning #neuralnetworks #math
#tutorial #free

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 21 Neural Network Overview

Neural Networks and Deep Learning
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #machinelearning #AI #coursera #free #python #machinelearning #neuralnetworks

⭕️ Loss functions for image segmentation

🔹 Segmentation Loss Odyssey

Github: https://github.com/JunMa11/SegLoss

Paper: https://arxiv.org/abs/2005.13449v1
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📌 Via: @cedeeplearning
📌 Other social media: https://linktr.ee/cedeeplearning

📕 deep learning in computer vision
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📌Via: @cedeeplearning

#deeplearning #math #AI
#computer_vision #neuralnetworks
#machinelearning #datascience

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 22 Neural Network Representations

Neural Networks and Deep Learning
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#DeepLearning #machinelearning #AI #coursera #free #python #machinelearning #neuralnetworks

⭕️ BentoML

🔹BentoML is an open-source platform for high-performance ML model serving.

https://github.com/bentoml/BentoML
bentoml/BentoML

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📌Via: @cedeeplearning

#deeplearning #machinelearning
#neuralnetworks #artificial_intelligence

⚪️ Basics of Neural Network Programming

✒️ by prof. Andrew Ng
🔹Source: Coursera

🔖 Lecture 23 Computing Neural Network Output

Neural Networks and Deep Learning
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📌Other social media: https://linktr.ee/cedeeplearning

#DeepLearning #machinelearning #AI #coursera #free #python #machinelearning #neuralnetworks

⭕️ What are tensors?

Learn from these amazing blogs:

🔹 A Gentle Introduction to Tensors for Machine Learning with NumPy by: Jason Brownlee. https://machinelearningmastery.com/introduction-to-tensors-for-machine-learning/

🔹 WTF is a Tensor?!? by: Matthew Mayo.
https://www.kdnuggets.com/2018/05/wtf-tensor.html

🔹 Quick ML Concepts: Tensors by: Chi Nok Enoch Kan.
https://towardsdatascience.com/quick-ml-concepts-tensors-eb1330d7760f

🔹 Our Instagram post covering this topic: https://www.instagram.com/p/CCSnIO9AVfd/?igshid=a2bgrgoip8zx
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📌Via: @cedeeplearning

#tensor #tensorflow #machinelearning
#neuralnetworks #deeplearninig #tutorial