MP4 | h264, 1280x720 | Lang: English | Audio: aac, 44.1 KHz | 5h 47m | 3.60 GB

What you'll learn
Basics of TensorFlow 2.0
Decision Trees and Linear Regression in TensorFlow
Keras
Foundational algorithms

Description
Undoubtedly, TensorFlow is one of the most popular & widely used open-source libraries for machine learning applications. Apart from it, TensorFlow is also heavily used for dataflow and differentiable programming across a range of tasks. Because of this and a lot of other promises, hundreds of individuals are keen on exploring TensorFlow for AI & ML, Data Science, text-based application, video detection & others.

In order to cater to all our student's needs for learning TensorFlow, we have curated this exclusive practical guide. It will teach you Practical TensorFlow with more from a training perspective rather than just the theoretical knowledge.

What makes this course so unique?

It will help you in understanding both basics and the advanced concepts of TensorFlow along with the codes in a practical manner! Upon completing this course, you will be able to learn various essential aspects of this famous library. It will unfold with the basic introduction covering graphs, Keras, supervised learning and others.

In the later sections, you will learn more about AI & ML models like decision trees, linear regression & logistic regression along with evaluating models, gradient descent & digit classification. Concepts of CNN are also covered along with its architectures, layers, K-means algorithm, K-means implementation, facial recognition & others.

This course includes:

Section 1- TensorFlow 2.0, Graphs, Automatic Differentiation, Keras and TensorFlow, Intro to Machine Learning, Types of Supervised Learning.

Section 2- Decision Trees, Linear Regression, Logistic Regression, Model Evaluation.

Section 3- Gates and Forward Propagation, Complex Decision Boundaries, Backpropagation, Gradient Descent Type and Softmax, Digit Classification.

Section 4- CNN, Layers of CNN, Famous CNN Architectures.

Section 5- K-Means Algorithm, Centroid Initialization, K-Means ++, Number of Clusters, K-Means Implementation, Principal Component Analysis, Facial Recognition using PCA.

Searching for the online course that will teach you TensorFlow practically? Search no more!! Begin with this course today to get your hands dirty with TensorFlow!!

Who this course is for:
Students who want to learn practical implementation of algorithms in TensorFlow

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