Learn how to build a neural network and how to train, evaluate and optimize it with TensorFlow.
TensorFlow is the second machine learning framework that Google created and used to design, build, and train deep learning models. You can use the TensorFlow library do to numerical computations, which in itself doesn`t seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or tensors, that are communicated between these edges.
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This course is part of the upcoming Machine Learning in Tensorflow Specialization and will teach you best practices for using TensorFlow, a popular open-source framework for machine learning.
The Machine Learning course and Deep Learning Specialization teaches the most important and foundational principles of Machine Learning and Deep Learning.
This new deeplearning.
ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems.
To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
-You have hands on exp with ML / Hadoop technologies with a genuine interest in DL.
-You need to know basic python such as lists, dictionaries, loops, functions and classes
- You need to know basic differentiation
- You need to know basic algebra
Installation and Setup
What is Machine Learning?
Crash Course Overview
Introduction to Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Reinforcement Learning with OpenAI Gym
GAN - Generative Adversarial Networks