TensorFlow Tutorial Part 1 | Deep Learning Using TensorFlow | TensorFlow Tutorial Python | CloudxLab

This CloudxLab TensorFlow tutorial helps you to understand TensorFlow in detail. Below are the topics covered in this tutorial:

1) Why TensorFlow?
2) What are Tensors?
3) What is TensorFlow?
4) Creating your First Graph
5) Linear Regression with TensorFlow
6) Implementing Gradient Descent using TensorFlow
7) Implementing Gradient Descent Using autodiff

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#DeepLearning #Datasciencecourse #DataScience #CloudxLabMachineLearning #DeepLearningCourse #TensorFlow

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How does it work?

1. This is a 100+ hour online instructor-led course
2. With the course, you get access to real-time distributed production cluster so that you can learn by doing hands-on
3. Each topic consists of videos, assessments, questions and case studies to make sure you master the topic
4. We have a 24×7 support and discussion forum to answer all your queries throughout your learning journey
5. At the end of the training, you will work on real-life projects on which we will provide you a grade and a verifiable certificate!
6. Optionally, subscribe to 1:1 mentoring sessions and get guidance from industry leaders and professional

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About the Course

CloudxLab's Machine Learning & Deep Learning Specialization Training is designed to help you become a top Machine Learning Engineer. This specialization is designed for those who want to gain hands-on experience in solving real-life problems using machine learning and deep learning. After finishing this specialization, you will find creative ways to apply your learnings to your work. During this course, our expert will help you in

1. Python Foundations for Machine Learning
2. Foundations of Statistics & Linear Algebra
3. Machine Learning Classification Algorithms
4. Linear Regression, Logistic Regression and Polynomial Regression
5. Support Vector Machines
6. Decision Trees
7. Ensemble Learning, Random Forests
8. Dimensionality Reduction
9. Getting Started with TensorFlow
10. Introduction to Artificial Neural Networks
11. Convolutional Neural Networks
12. Recurrent Neural Networks
13. Autoencoders
14. Reinforcement Learning
15. Real-life Projects so that you can apply the skills learnt during the course

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Who should go for this course?

This course is for anyone who wants to become expert in Machine Learning, Deep Learning, Data Science and progress in the career. Ideally, this course will help professionals in the following groups

1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or Artificial Intelligence
4. Recent graduates passionate about building a successful career in Data Science and Machine Learning

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Why Learn Machine Learning and Deep Learning?

In the recent times, it has been proven that Machine Learning and Deep Learning approach to solving a problem gives far better accuracy than other approaches. Every domain of computing such as data analysis, software engineering, and artificial intelligence is going to be impacted by Machine Learning. Therefore, every engineer, researcher, manager or scientist would be expected to know Machine Learning.

There is massive growth in the machine learning and deep learning, and opportunities are skyrocketing, making this the perfect time to launch your career in this space.

Customer Review:

Ross Burns, Machine Learning Engineer - I have thoroughly enjoyed both the ML and DL courses from CloudXLab and will look forward to reviewing the videos/material at a later time. I’ve been to many meetups and paid sessions on ML /DL and this course beats most of them on the depth of topics and certainly breadth of topics. I’ve not taken any online courses (Andrew Ng, for example) to their conclusion, so I won’t draw a conclusion there. For an instructor-led, interactive course, I would expect to pay many times more for a class (ML and DL) such as this in the US. The instructor is easy to understand, has extensive experience, and truly cares about the student knowing the material.
This course is very much influenced and inspired by the book "Hands-On Machine Learning with Scikit–Learn and TensorFlow" by Aurelien Geron. We strongly recommend that you buy the book too. Some of the images on the slides are from this book.

[kiddyboy]

What does best_theta = theta.eval() do? I'm trying to understand what does ".eval()" do.. Is that statement even needed? What does it do different as compared to 'x' because both seem t return the same values..
Thanks!