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+--- Thread: Deep Learning Zero To Hero - Hands-On With Python (/Thread-Deep-Learning-Zero-To-Hero-Hands-On-With-Python)
Deep Learning Zero To Hero - Hands-On With Python - BaDshaH - 01-14-2024
![[Image: 0c4c3c577a05d08098546f067dd7ec92.jpg]](https://i122.fastpic.org/big/2024/0114/92/0c4c3c577a05d08098546f067dd7ec92.jpg)
Published 1/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.05 GB | Duration: 10h 56m
Learn Deep learning practically from scratch using Python
[b]What you'll learn[/b]
How to build artificial neural networks
Architectures of feedforward and convolutional networks
The calculus and code of gradient descent
Learn Python from scratch (no prior coding experience necessary)
[b]Requirements[/b]
Basic Machine learning concepts and Python.
[b]Description[/b]
Deep Learning is a new part of Machine Learning, which has been introduced with the objective of moving Machine Learning closer to Artificial Intelligence. Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. Through this training we are going to learn and apply concepts of deep learning with live projects.The course includes the following;•Prediction in Structured/Tabular Data•Recommendation•Image Classification•Image Segmentation•Object Detection•Style Transfer•Super Resolution•Sentiment Analysis•Text Generation•Time Series (Sequence) Prediction•Machine Translation•Speech Recognition•Question Answering•Text Similarity•Image Captioning•Image Generation•Image to Image TranslationWe will be learning the followings:The theory and math underlying deep learningHow to build artificial neural networksArchitectures of feedforward and convolutional networksBuilding models in PyTorchThe calculus and code of gradient descentFine-tuning deep network modelsLearn Python from scratch (no prior coding experience necessary)How and why autoencoders workHow to use transfer learningImproving model performance using regularization
Overview
Section 1: Deep Learning ZERO To HERO - Hands-On With Python
Lecture 1 Introduction to Hands on Deeplearning
Lecture 2 What is Machine Learning
Lecture 3 Popular ML Methods
Lecture 4 What is Deep Learning
Lecture 5 Applications of Deeplearning
Lecture 6 Recommendations
Lecture 7 Basic Concept of Deeplearning
Lecture 8 Perception
Lecture 9 Neural Network
Lecture 10 Universal Approximations Theorem
Lecture 11 Deep Neural Network
Lecture 12 Deep Neural Network Continue
Lecture 13 Getting Started
Lecture 14 Where to write Code
Lecture 15 Jupiter Notebook
Lecture 16 Google Colab
Lecture 17 Pytorch
Lecture 18 Tensors
Lecture 19 Tensors Continue
Lecture 20 Gradients
Lecture 21 MNIST Example
Lecture 22 Check Sample
Lecture 23 Hidden Layer
Lecture 24 Interface on a Digit
Lecture 25 Transfer-Learning-Overview
Lecture 26 What is Transfer Learning
Lecture 27 CS231n Convolutional Neural Networks
Lecture 28 Download Dataset
Lecture 29 Transform the Data
Lecture 30 Visualize the Data
Lecture 31 Define the Model
Lecture 32 Add a Few Final Layers
Lecture 33 Train the Model
Lecture 34 Test the Model
Lecture 35 What About CIFAR
Lecture 36 Image Classifier on Cifar 10 Dataset
Lecture 37 Download and Load Our Dataset
Lecture 38 Train and Test Dataset
Lecture 39 Define Our Neural Network
Lecture 40 Working on Image
Lecture 41 Input and Output
Lecture 42 Define Our Loss Function
Lecture 43 Train Data in Enumerate
Lecture 44 Train Data in Enumerate Continue
Lecture 45 Test the Neural Network on the Test Image
Lecture 46 Intro to Text Classifier
Lecture 47 Text Classification Using CNN
Lecture 48 Prepare the Data
Lecture 49 Build the Model
Lecture 50 Build the Model Coninue
Lecture 51 More on Build the Model
Lecture 52 Define a Loss Function
Lecture 53 Define a Loss Function Continue
Lecture 54 More on Define a Loss Function
Lecture 55 Evaluate or Test the Model
Lecture 56 Intro to Text Generation
Lecture 57 Text Generation-Transformers
Lecture 58 Text Generation-Transformers Continue
Lecture 59 Transformers-Architectures
Lecture 60 Transformers-Architectures Cintinue
Lecture 61 Word-Generation
Lecture 62 Word-Generation Continue
Lecture 63 Text-Generation
Lecture 64 Intro to Text Translation
Lecture 65 Loading-Data
Lecture 66 Preparing-Data
Lecture 67 Encoder-Attention Part 1
Lecture 68 Encoder-Attention Part 2
Lecture 69 Encoder-Attention Part 3
Lecture 70 Decoder
Lecture 71 Train-Eval-Functions
Lecture 72 Train-Eval-Functions Continue
Lecture 73 Training-Fixes
Lecture 74 Training-Evaluation
Lecture 75 Prediction-Tabular-Data Part 1
Lecture 76 Prediction-Tabular-Data Part 2
Lecture 77 Prediction-Tabular-Data Part 3
Lecture 78 Prediction-Tabular-Data Part 4
Lecture 79 Collaborative Filtering
Lecture 80 Collaborative Filtering Continue
Lecture 81 Other Recommendation Approaches
Aspiring Data Scientists and AI/Machine Learning/Deep Learning Engineers
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