Neural Networks with TensorFlow - A Complete Guide!: 3-in-1

Description

Tensorflow is Google’s popular offering for machine learning and deep learning. It has become a popular choice of tool for performing fast, efficient, and accurate Deep Learning. TensorFlow is one of the newest and most comprehensive libraries for implementing Deep Learning and building CNNs. Neural Networks are at the forefront of almost all recent major technology breakthroughs. The intersection of big data, parallel programming, and AI generated a new wave of Neural Network research.

Are you looking forward to getting hands-on and use Deep Learning to build CNNs and train efficient Neural Networks? If yes, then this is the course perfect for you!

This comprehensive 3-in-1 course takes a solution-based approach where every topic is explicated with the help of a real-world example. Use Tensorflow to implement different kinds of Neural Networks – from simple feedforward Neural Networks to multi layered perceptrons, CNNs, RNNs and more! Moreover, Implement multi layered perceptrons, CNN, and more using Tensorflow!

By the end of the course, you’ll not just be able to build powerful Deep Learning models, but also accelerate the training of your models and scale them as required.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, Learning Neural Networks with Tensorflow, covers Neural Networks by solving real real-world datasets using Tensorflow. In this course, you’ll start by building a simple flower recognition program, making you feel comfortable with Tensorflow, and it will teach you several important concepts in Neural Networks. Next, you’ll start working with high-dimensional uses to predict one output: 1275 molecular features you can use to predict the atomization energy of an atom. The next program we’ll create is a handwritten number recognition system trained on the famous MNIST dataset. In the final program, estimate what a celebrity looks like, checking for new pictures to see whether a celebrity is attractive, wears a hat, has lipstick on, and many more properties that are difficult to estimate with "traditional" computer vision techniques.

After the course, you’ll not only be able to build a Neural Network for your own dataset, you’ll also be able to reason which techniques will improve your Neural Network.

The second course, Advanced Neural Networks with Tensorflow, covers getting hands-on to understand Advanced Neural Networks with TensorFlow. You'll explore Deep Reinforcement Learning algorithms such as Generative Networks and Deep Q Learning. You will learn to implement some more complex types of neural networks such as Deep Q Learning with OpenAI Gym, autoencoders, and Siamese neural networks. During the course of the video, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn Autoencoder applications.

By the end of this course, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle.

The third course, TensorFlow for Neural Network Solutions, covers exploring high-level concepts such as neural networks, CNN and RNN using TensorFlow. This course covers important high-level concepts such as neural networks, CNN, RNN, and NLP. Once you are familiar and comfortable with the TensorFlow ecosystem, the last section will show you how to take it to production. Once you are familiar and comfortable with the TensorFlow ecosystem, the last section will show you how to take it to production.

By the end of the course, you’ll not just be able to build powerful Deep Learning models, but also accelerate the training of your models and scale them as required.

About the Authors

●        Roland Meertens is currently developing computer vision algorithms for self-driving cars. Previously he has worked as a research engineer at a translation department. Examples of things he has made are a Neural Machine Translation implementation, a post-editor, and a tool that estimates the quality of a translated sentence. Last year, he worked at the Micro Aerial Vehicle Laboratory at the university of Delft, on indoor localization (SLAM) and obstacle avoidance behaviors for a drone that delivers food inside a restaurant. Another thing he worked on was detecting and following people using onboard computer vision algorithms on a stereo camera. For his Master's thesis, he did an internship at a company called SpirOps, where he worked on the development of a dialogue manager for project Romeo. In his Artificial Intelligence study, he specialized in cognitive artificial intelligence and brain-computer interfacing. His research interests lie in machine learning techniques, human-robot interaction, brain-computer interfaces, and human-computer interaction.

●        Nick McClure is currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow Group and Caesars Entertainment Corporation. He got his degrees in Applied Mathematics from The University of Montana and the College of Saint Benedict and Saint John's University. He has a passion for learning and advocating for analytics, machine learning, and artificial intelligence.

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