Published 2/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 9.16 GB | Duration: 20h 36m
Master and Deploy Sentiment analysis and machine translation solutions with Tensorflow and Hugggingface Transformers
What you’ll learn
The Basics of Tensors and Variables with Tensorflow
Linear Regression, Logistic Regression and Neural Networks built from scratch.
Basics of Tensorflow and training neural networks with TensorFlow 2.
Model deployment
Conversion from tensorflow to Onnx Model
Quantization Aware training
Building API with Fastapi
Deploying API to the Cloud
Sentiment Analysis with Recurrent neural networks, Attention Models and Transformers from scratch
Neural Machine Translation with Recurrent neural networks, Attention Models and Transformers from scratch
Neural Machine Translation with T5 in Huggingface transformers
Attention Networks
Transformers from scratch
Requirements
Basic Math
Access to an internet connection, as we shall be using Google Colab (free version)
Basic Knowledge of Python
Description
Sentiment analysis and machine translation models are used by millions of people every single day. These deep learning models (most notably transformers) power different industries today. With the creation of much more efficient deep learning models, from the early 2010s, we have seen a great improvement in the state of the art in the domains of sentiment analysis and machine translation.In this course, we shall take you on an amazing journey in which you’ll master different concepts with a step-by-step approach. We shall start by understanding how to process text in the context of natural language processing, then we would dive into building our own models and deploying them to the cloud while observing best practices. We are going to be using Tensorflow 2 (the world’s most popular library for deep learning, built by Google) and HuggingfaceYou will learn:The Basics of Tensorflow (Tensors, Model building, training, and evaluation).Deep Learning algorithms like Recurrent Neural Networks, Attention Models, Transformers, and Convolutional neural networks.Sentiment analysis with RNNs, Transformers, and Huggingface Transformers (Deberta)Transfer learning with Word2vec and modern Transformers (GPT, Bert, ULmfit, Deberta, T5…)Machine translation with RNNs, attention, transformers, and Huggingface Transformers (T5)Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals!This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much emphasis on feedback. Your reviews and questions in the forum will help us better this course. Feel free to ask as many questions as possible on the forum. We do our very best to reply in the shortest possible time.Enjoy!!!
Overview
Section 1: Introduction
Lecture 1 Welcome
Lecture 2 General intro
Section 2: Tensors and variables
Lecture 3 Basics
Lecture 4 Initialization and Casting
Lecture 5 Indexing
Lecture 6 Maths Operations
Lecture 7 Linear algebra operations
Lecture 8 Common methods
Lecture 9 Ragged tensors
Lecture 10 Sparse tensors
Lecture 11 String tensors
Lecture 12 Variables
Section 3:[PRE-REQUISCITE] Building neural networks with tensorflow
Lecture 13 Task understanding
Lecture 14 Data preparation
Lecture 15 Linear regression model
Lecture 16 Error sanctioning
Lecture 17 Training and optimization
Lecture 18 Performance measurement
Lecture 19 Validation and testing
Lecture 20 Corrective measures
Section 4: Text Preprocessing for Sentiment Analysis
Lecture 21 Understanding Sentiment Analysis
Lecture 22 Text Standardization
Lecture 23 Tokenization
Lecture 24 One-hot encoding and Bag of Words
Lecture 25 Term frequency – Inverse Document frequency (TF-IDF)
Lecture 26 Embeddings
Section 5: Sentiment Analysis with Recurrent neural networks
Lecture 27 How Recurrent neural networks work
Lecture 28 Data preparation
Lecture 29 Building and training RNNs
Lecture 30 Advanced RNNs (LSTM and GRU)
Lecture 31 1D Convolutional Neural Network
Section 6: Sentiment Analysis with transfer learning
Lecture 32 Understanding Word2vec
Lecture 33 Integrating pretrained Word2vec embeddings
Lecture 34 Testing
Lecture 35 Visualizing embeddings
Section 7: Neural Machine Translation with Recurrent Neural Networks
Lecture 36 Understanding Machine Translation
Lecture 37 Data Preparation
Lecture 38 Building, training and testing Model
Lecture 39 Understanding BLEU score
Lecture 40 Coding BLEU score from scratch
Section 8: Neural Machine Translation with Attention
Lecture 41 Understanding Bahdanau Attention
Lecture 42 Building, training and testing Bahdanau Attention
Section 9: Neural Machine Translation with Transformers
Lecture 43 Understanding Transformer Networks
Lecture 44 Building, training and testing Transformers
Lecture 45 Building Transformers with Custom Attention Layer
Lecture 46 Visualizing Attention scores
Section 10: Sentiment Analysis with Transformers
Lecture 47 Sentiment analysis with Transformer encoder
Lecture 48 Sentiment analysis with LSH Attention
Section 11: Transfer Learning and Generalized Language Models
Lecture 49 Understanding Transfer Learning
Lecture 50 Ulmfit
Lecture 51 Gpt
Lecture 52 Bert
Lecture 53 Albert
Lecture 54 Gpt2
Lecture 55 Roberta
Lecture 56 T5
Section 12: Sentiment Analysis with Deberta in Huggingface transformers
Lecture 57 Data Preparation
Lecture 58 Building,training and testing model
Section 13: Neural Machine Translation with T5 in Huggingface transformers
Lecture 59 Dataset Preparation
Lecture 60 Building,training and testing model
Beginner Python Developers curious about Applying Deep Learning for Natural Language Processing in the domains of sentiment analysis and machine translation,Deep Learning for NLP Practitioners who want gain a mastery of how things work under the hood,NLP practitioners who want to learn how state of art sentiment analysis and machine translation models are built and trained using deep learning.,Anyone wanting to deploy ML Models,Learners who want a practical approach to Deep learning for Sentiment analysis and Machine Translation
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