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Neural Networks with TensorFlow and PyTorch

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Neural Networks with TensorFlow and PyTorch, available at $39.99, has an average rating of 3.45, with 101 lectures, 4 quizzes, based on 12 reviews, and has 139 subscribers.

You will learn about Get hands-on and understand Neural Networks with TensorFlow and PyTorch Understand how and when to apply autoencoders Develop an autonomous agent in an Atari environment with OpenAI Gym Apply NLP and sentiment analysis to your data Develop a multilayer perceptron neural network to predict fraud and hospital patient readmission Build convolutional neural network classifier to automatically identify a photograph Learn how to build a recurrent neural network to forecast time series and stock market data Know how to build Long Short Term Memory Model (LSTM) model to classify movie reviews as positive or negative using Natural Language Processing (NLP) Get familiar with PyTorch fundamentals and code a deep neural network Perform image captioning and grammar parsing using Natural Language Processing This course is ideal for individuals who are This course is for machine learning developers, engineers, and data science professionals who want to work with neural networks and deep learning using powerful Python libraries, TensorFlow and PyTorch. It is particularly useful for This course is for machine learning developers, engineers, and data science professionals who want to work with neural networks and deep learning using powerful Python libraries, TensorFlow and PyTorch.

Enroll now: Neural Networks with TensorFlow and PyTorch

Summary

Title: Neural Networks with TensorFlow and PyTorch

Price: $39.99

Average Rating: 3.45

Number of Lectures: 101

Number of Quizzes: 4

Number of Published Lectures: 101

Number of Published Quizzes: 4

Number of Curriculum Items: 105

Number of Published Curriculum Objects: 105

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • Get hands-on and understand Neural Networks with TensorFlow and PyTorch
  • Understand how and when to apply autoencoders
  • Develop an autonomous agent in an Atari environment with OpenAI Gym
  • Apply NLP and sentiment analysis to your data
  • Develop a multilayer perceptron neural network to predict fraud and hospital patient readmission
  • Build convolutional neural network classifier to automatically identify a photograph
  • Learn how to build a recurrent neural network to forecast time series and stock market data
  • Know how to build Long Short Term Memory Model (LSTM) model to classify movie reviews as positive or negative using Natural Language Processing (NLP)
  • Get familiar with PyTorch fundamentals and code a deep neural network
  • Perform image captioning and grammar parsing using Natural Language Processing

Who Should Attend

  • This course is for machine learning developers, engineers, and data science professionals who want to work with neural networks and deep learning using powerful Python libraries, TensorFlow and PyTorch.

Target Audiences

  • This course is for machine learning developers, engineers, and data science professionals who want to work with neural networks and deep learning using powerful Python libraries, TensorFlow and PyTorch.

TensorFlow is quickly becoming the technology of choice for deep learning and machine learning, because of its ease to develop powerful neural networks and intelligent machine learning applications. Like TensorFlow, PyTorch has a clean and simple API, which makes building neural networks faster and easier. It’s also modular, and that makes debugging your code a breeze. If you’re someone who wants to get hands-on with Deep Learning by building and training Neural Networks, then go for this course.

This course takes a step-by-step approach where every topic is explicated with the help of a real-world examples. You will begin with learning some of the Deep Learning algorithms with TensorFlow such as Convolutional Neural Networks and Deep Reinforcement Learning algorithms such as Deep Q Networks and Asynchronous Advantage Actor-Critic. You will then explore Deep Reinforcement Learning algorithms in-depth with real-world datasets to get a hands-on understanding of neural network programming and Autoencoder applications. You will also predict business decisions with NLP wherein you will learn how to program a machine to identify a human face, predict stock market prices, and process text as part of Natural Language Processing (NLP). Next, you will explore the imperative side of PyTorch for dynamic neural network programming. Finally, you will build two mini-projects, first focusing on applying dynamic neural networks to image recognition and second NLP-oriented problems (grammar parsing).

By the end of this course, you will have a complete understanding of the essential ML libraries TensorFlow and PyTorch for developing and training neural networks of varying complexities, without any hassle.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:

  • 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.

  • Harveen Singh Chadha is an experienced researcher in Deep Learning and is currently working as a Self Driving Car Engineer. He is currently focused on creating an ADAS (Advanced Driver Assistance Systems) platform. His passion is to help people who currently want to enter into the Data Science Universe.

  • Anastasia Yanina is a Senior Data Scientist with around 5 years of experience. She is an expert in Deep Learning and Natural Language processing and constantly develops her skills as far as possible. She is passionate about human-to-machine interactions. She believes that bridging the gap may become possible with deep neural network architectures.

Course Curriculum

Chapter 1: Learning Neural Networks with Tensorflow

Lecture 1: The Course Overview

Lecture 2: Solving Public Datasets

Lecture 3: Why We Use Docker and Installation Instructions

Lecture 4: Our Code, in a Jupyter Notebook

Lecture 5: Understanding TensorFlow

Lecture 6: The Iris Dataset

Lecture 7: The Human Brain and How to Formalize It

Lecture 8: Backpropagation

Lecture 9: Overfitting — Why We Split Our Train and Test Data

Lecture 10: Ground State Energies of 16,242 Molecules

Lecture 11: First Approach – Easy Layer Building

Lecture 12: Preprocessing Data

Lecture 13: Understanding the Activation Function

Lecture 14: The Importance of Hyperparameters

Lecture 15: Images of Written Digits

Lecture 16: Dense Layer Approach

Lecture 17: Convolution and Pooling Layers

Lecture 18: Convolution and Pooling Layers (Continued)

Lecture 19: From Activations to Probabilities – the Softmax Function

Lecture 20: Optimization and Loss Functions

Lecture 21: Large-Scale CelebFaces Attributes (CelebA) Dataset

Lecture 22: Building an Input Pipeline in TensorFlow

Lecture 23: Building a Convolutional Neural Network

Lecture 24: Batch Normalization

Lecture 25: Understanding What Your Network Learned –Visualizing Activations

Chapter 2: Advanced Neural Networks with Tensorflow

Lecture 1: The Course Overview

Lecture 2: The Approach of This Course

Lecture 3: Installing Docker and Downloading the Source Code for This Course

Lecture 4: Understanding Jupyter Notebooks and TensorFlow

Lecture 5: Visualizing Your Graph

Lecture 6: Adding Summaries

Lecture 7: Plotting the Weights in a Histogram

Lecture 8: Inspecting Input and Output

Lecture 9: Encoding MNIST Characters

Lecture 10: Practical Application –Denoising

Lecture 11: The Dropout Layer

Lecture 12: Variational Autoencoders

Lecture 13: The Omniglot Dataset

Lecture 14: What Is a Siamese Neural Network?

Lecture 15: Training and Testing a Siamese Neural Network

Lecture 16: Alternative Loss Functions

Lecture 17: Speed of Your Network

Lecture 18: Getting Started with the OpenAI Gym

Lecture 19: Random Search

Lecture 20: Reinforcement Learning Explained

Lecture 21: Reinforcement Learning Explained (Continued)

Lecture 22: Reinforcement Learning Tricks

Lecture 23: Playing Atari Games

Lecture 24: Defining Our Network

Lecture 25: Starting and Training a Session

Chapter 3: Hands-On Neural Network Programming with TensorFlow

Lecture 1: The Course Overview

Lecture 2: Introduction To Neural Networks

Lecture 3: Setting Up Environment

Lecture 4: Introduction To TensorFlow

Lecture 5: TensorFlow Installation

Lecture 6: Multilayer Perceptron Neural Network

Lecture 7: Forward Propagation & Loss Functions

Lecture 8: Backpropagation

Lecture 9: Creating First Neural Network to Predict Fraud

Lecture 10: Testing Neural Network to Predict Fraud

Lecture 11: Introduction To Convolutional Neural Networks

Lecture 12: Training a Convolution Neural Network

Lecture 13: Testing a Convolution Neural Network

Lecture 14: Introduction To Recurrent Neural Networks

Lecture 15: Training a Recurrent Neural Network

Lecture 16: Testing a Recurrent Neural Network

Lecture 17: Introduction To Long Short-Term Memory Network

Lecture 18: Training an LSTM Network

Lecture 19: Testing a Long Short-Term Memory Network

Lecture 20: Introduction To Generative models

Lecture 21: Neural Style Transfer: Basics

Lecture 22: Results: Neural Style Transfer

Lecture 23: Introduction To Autoencoders

Lecture 24: Autoencoder in TensorFlow

Lecture 25: Training & Testing a Autoencoder

Chapter 4: Dynamic Neural Network Programming with PyTorch

Lecture 1: The Course Overview

Lecture 2: Installation Checklist

Lecture 3: Tensors, Autograd, and Backprop

Lecture 4: Backprop, Loss Functions, and Neural Networks

Lecture 5: PyTorch on GPU: First Steps

Lecture 6: Imperative Programming Architectures

Lecture 7: Static Graphs versus Dynamic Graphs

Lecture 8: Neural Network Debugging: Why Imperative Philosophy Helps

Lecture 9: Feedforward and Recurrent Neural Networks

Lecture 10: Convolutional Neural Networks

Lecture 11: Autoencoders

Lecture 12: Extensions with Numpy – Part 1

Lecture 13: Extensions with Numpy – Part 2

Lecture 14: Custom C++ and CUDA Extensions: Motivation

Lecture 15: Custom C++ and CUDA Extensions: Setuptools

Lecture 16: Custom C++ and CUDA Extensions: Binding to Python

Lecture 17: Custom C++ and CUDA Extensions: JIT Compilation

Lecture 18: Image Captioning: First Steps

Instructors

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  • 4 stars: 4 votes
  • 5 stars: 3 votes

Frequently Asked Questions


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