🔥 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐃𝐞𝐯𝐎𝐩𝐬 𝐜𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎): https://www.edureka.co/masters....-program/devops-engi
This Edureka "Top 10 DevOps Tools to Learn" gives you an introduction to the most trending DevOps tools in the industry that you must learn. To have knowledge of these DevOps tools is very important for a successful DevOps engineer. The DevOps tools included in this video are:

1. Git
2. Jenkins
3. Selenium
4. Docker
5. Puppet
6. Chef
7. Ansible
8. Splunk
9. ELK
10. Nagios

📝 Feel free to comment your doubts in the comment section below, and we will be happy to answer📝

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬

🔵 DevOps Online Training:https://bit.ly/3r7xtvQ
🌕 AWS Online Training: https://bit.ly/3r6sawS
🔵 Azure DevOps Online Training:https://bit.ly/3r8shaX
🌕 Tableau Online Training: https://bit.ly/3LMOLGE
🔵 Power BI Online Training: https://bit.ly/3J9uOrP
🌕 Selenium Online Training: https://bit.ly/3jeSvEx
🔵 PMP Online Training: https://bit.ly/3DNgUKX
🌕 Salesforce Online Training: https://bit.ly/3j8VyxW
🔵 Cybersecurity Online Training: https://bit.ly/3LJBoGV

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬

🔵 DevOps Engineer Masters Program: https://bit.ly/37p4goY
🌕 Cloud Architect Masters Program: https://bit.ly/35LP0SV
🔵 Data Scientist Masters Program: https://bit.ly/3NULA1q
🌕 Big Data Architect Masters Program:https://bit.ly/38qZTud
🔵 Machine Learning Engineer Masters Program:https://bit.ly/3ueP9rm
🌕 Business Intelligence Masters Program: https://bit.ly/3x9qpT5
🔵 Cyber Security Masters Program: https://bit.ly/3uo98UN
🌕 Full Stack Developer Masters Program : https://bit.ly/3NUlVGb
🔵 Automation Testing Engineer Masters Program : https://bit.ly/3E0rFcZ
🌕 Python Developer Masters Program : https://bit.ly/3j8YYkg
🔵 Azure Cloud Engineer Masters Program: https://bit.ly/3NQb9Ax

🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬

🌕 Professional Certificate Program in DevOps with Purdue University: https://bit.ly/3Ov52lT

📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 2023 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩ NEW Top 10 Technologies To Learn In 2023 - https://youtu.be/udD_GQVDt5g

📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates
📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain
📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka
📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: https://www.instagram.com/edureka_learning/
📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/
📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN
📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN
📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/
📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/

About Course
What will you learn as a part of this DevOps course?
This DevOps training course is designed keeping in mind the latest trends in the industry. The course focuses on creating a strong base for various DevOps tools including Git, Jenkins, Docker, Ansible, Kubernetes, Prometheus and Grafana, and Terraform. The training is completely hands-on oriented and designed in a way that will help you in becoming a certified practitioner by providing you an intensified training for the best practices about Continuous Development, Continuous Testing, Configuration Management, including Continuous Integration and Continuous Deployment and finally Continuous Monitoring of the software throughout its development life cycle.

What are the skills that you will be learning with our DevOps course?
Upon completion of the DevOps training course, you will be able to:

Understand the DevOps Process and Lifecycle
Manage and keep a track of different versions of the source code using GIT
Use Jenkins and maven to build the application and integrate the CI/CD Pipeline
Manage your infrastructure using Ansible
Build and Deploy containers using Docker
Orchestrate your containerized environment with Kubernetes

For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).

#shorts #machinelearning #deeplearning

In this video we show a quick example of how to obtain a clean progress bar that contains information about the current epoch and the progression with regards to loss and accuracy.

Written blogpost if you prefer to read:
https://aladdinpersson.medium.....com/how-to-get-a-pro

People often ask what courses are great for getting into ML/DL and the two I started with is ML and DL specialization both by Andrew Ng. Below you'll find both affiliate and non-affiliate links if you want to check it out. The pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
ML Course (affiliate): https://bit.ly/3qq20Sx
DL Specialization (affiliate): https://bit.ly/30npNrw
ML Course (no affiliate): https://bit.ly/3t8JqA9
DL Specialization (no affiliate): https://bit.ly/3t8JqA9

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https://github.com/aladdinpers....son/Machine-Learning

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PyTorch Playlist:
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A good model follows the “Goldilocks” principle in terms of data fitting. Models that underfit data will have poor accuracy, while models that overfit data will fail to generalize. A model that is “just right” will avoid these important problems.

Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv

Suppose you are trying to classify big cats based on features such as claw size, sex, body dimensions, bite strength, color, speed, and the presence of a mane. Due to various deficiencies in the training process and data set, the resultant model may fail to fully differentiate the various types of cats. For example, a rule-based model may predict that any cat with a mane that roars is a lion, while ignoring that if such a cat is female, it is technically referred to as a lioness. Since this model does not take all necessary features into account while performing classification, it is said to underfit the data. The problem of underfitting is solved by adding more detail to the model to ensure that it properly captures the differences between classes.

On the other hand, a model may also consider every possible detail and develop very specific, complex rules for classification. For example, if one data point represents a lion that is 3.5 feet tall, weighs 305 pounds, and has 2.9-inch claws, the model may develop a rule that classifies every 3.5 foot tall, 305 pound cat with 2.9-inch claws as a lion. Such rules will accurately classify the training data, but will poorly generalize to new data samples. A model that develops these kinds of rules is said to overfit the data. In other words, the model has failed to identify the true patterns that differentiate the classes. As a separate example, if the data only contained tigers that grew up in a zoo, the model may have difficulty classifying tigers that grew up in the wild. So while improving the process of data collection is helpful to prevent this problem, the model must be designed to identify the most important patterns that identify a class, so that new samples can be properly classified.

With regards to neural networks, overfitting typically stems from too many input features, or the use of an overly-complicated network configuration. If the input count is too large, the training process may start to assign weights to features that either aren't needed or add unnecessary complexity to the model. An overly-complicated configuration may lead to the development of specific rules that improperly relate many different features, resulting in poor generalization.

Overfitting is a common problem in data science. One popular method to reduce overfitting is the use of a cross-validation data set along with parameter averaging. For neural networks, a common method is regularization. There are different types such as L1 and L2, but each of these follows the same principle – penalize the model for letting weights and biases become too large. Another method is Max Norm constraints, which directly adds a size limit to the weights and biases. A different approach is dropout, which randomly switches off certain neurons in the network, preventing the model from becoming too dependent on a set of neurons and the associated weights and biases. While these methods are broadly applied across the model rather than used for systematically searching for problem patterns, they have been proven to reduce and sometimes prevent the problem of overfitting.

Credits
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelan....cers/~0147b8991909b2
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal