Neural Networks With Tensorflow And Pytorch

Unleash the power of TensorFlow and PyTorch to build and train Neural Networks effectively

What you'll 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

Requirements

Basic knowledge of Python is required. Familiarity with TensorFlow and PyTorch will be beneficial.

Description

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.

Overview

Section 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

Section 2: Advanced Neural Networks with Tensorflow

Lecture 26 The Course Overview

Lecture 27 The Approach of This Course

Lecture 28 Installing Docker and Downloading the Source Code for This Course

Lecture 29 Understanding Jupyter Notebooks and TensorFlow

Lecture 30 Visualizing Your Graph

Lecture 31 Adding Summaries

Lecture 32 Plotting the Weights in a Histogram

Lecture 33 Inspecting Input and Output

Lecture 34 Encoding MNIST Characters

Lecture 35 Practical Application –Denoising

Lecture 36 The Dropout Layer

Lecture 37 Variational Autoencoders

Lecture 38 The Omniglot Dataset

Lecture 39 What Is a Siamese Neural Network?

Lecture 40 Training and Testing a Siamese Neural Network

Lecture 41 Alternative Loss Functions

Lecture 42 Speed of Your Network

Lecture 43 Getting Started with the OpenAI Gym

Lecture 44 Random Search

Lecture 45 Reinforcement Learning Explained

Lecture 46 Reinforcement Learning Explained (Continued)

Lecture 47 Reinforcement Learning Tricks

Lecture 48 Playing Atari Games

Lecture 49 Defining Our Network

Lecture 50 Starting and Training a Session

Section 3: Hands-On Neural Network Programming with TensorFlow

Lecture 51 The Course Overview

Lecture 52 Introduction To Neural Networks

Lecture 53 Setting Up Environment

Lecture 54 Introduction To TensorFlow

Lecture 55 TensorFlow Installation

Lecture 56 Multilayer Perceptron Neural Network

Lecture 57 Forward Propagation & Loss Functions

Lecture 58 Backpropagation

Lecture 59 Creating First Neural Network to Predict Fraud

Lecture 60 Testing Neural Network to Predict Fraud

Lecture 61 Introduction To Convolutional Neural Networks

Lecture 62 Training a Convolution Neural Network

Lecture 63 Testing a Convolution Neural Network

Lecture 64 Introduction To Recurrent Neural Networks

Lecture 65 Training a Recurrent Neural Network

Lecture 66 Testing a Recurrent Neural Network

Lecture 67 Introduction To Long Short-Term Memory Network

Lecture 68 Training an LSTM Network

Lecture 69 Testing a Long Short-Term Memory Network

Lecture 70 Introduction To Generative models

Lecture 71 Neural Style Transfer: Basics

Lecture 72 Results: Neural Style Transfer

Lecture 73 Introduction To Autoencoders

Lecture 74 Autoencoder in TensorFlow

Lecture 75 Training & Testing a Autoencoder

Section 4: Dynamic Neural Network Programming with PyTorch

Lecture 76 The Course Overview

Lecture 77 Installation Checklist

Lecture 78 Tensors, Autograd, and Backprop

Lecture 79 Backprop, Loss Functions, and Neural Networks

Lecture 80 PyTorch on GPU: First Steps

Lecture 81 Imperative Programming Architectures

Lecture 82 Static Graphs versus Dynamic Graphs

Lecture 83 Neural Network Debugging: Why Imperative Philosophy Helps

Lecture 84 Feedforward and Recurrent Neural Networks

Lecture 85 Convolutional Neural Networks

Lecture 86 Autoencoders

Lecture 87 Extensions with Numpy – Part 1

Lecture 88 Extensions with Numpy – Part 2

Lecture 89 Custom C++ and CUDA Extensions: Motivation

Lecture 90 Custom C++ and CUDA Extensions: Setuptools

Lecture 91 Custom C++ and CUDA Extensions: Binding to Python

Lecture 92 Custom C++ and CUDA Extensions: JIT Compilation

Lecture 93 Image Captioning: First Steps

Lecture 94 PyTorch DataLoaders

Lecture 95 Image Captioning: Theory

Lecture 96 Image Captioning: Practice

Lecture 97 Honor Track: Image Captioning Datasets

Lecture 98 Motivation and Section Overview

Lecture 99 Word Embeddings

Lecture 100 Sentiment Analysis with PyTorch

Lecture 101 Char-Level RNN for Text Generation

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.