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Why Deep Learning Is Becoming So Popular?🔥🔥🔥🔥🔥🔥

Complete Road Map To Prepare For Deep Learning🔥🔥🔥🔥

Introduction To Machine Learning And Deep Learning For Starters

Tutorial 1- Introduction to Neural Network and Deep Learning

Tutorial 2- How does Neural Network Work

Tutorial 3-Activation Functions Part-1

Tutorial 4: How to train Neural Network with BackPropogation

Tutorial 5- How to train MultiLayer Neural Network and Gradient Descent

Tutorial 6-Chain Rule of Differentiation with BackPropagation

Tutorial 7- Vanishing Gradient Problem

Tutorial 8- Exploding Gradient Problem in Neural Network

Tutorial 9- Drop Out Layers in Multi Neural Network

Tutorial 10- Activation Functions Rectified Linear Unit(relu) and Leaky Relu Part 2

Deep Learning-Activation Functions-Elu, PRelu,Softmax,Swish And Softplus

Tutorial 11- Various Weight Initialization Techniques in Neural Network

Tutorial 12- Stochastic Gradient Descent vs Gradient Descent

Tutorial 13- Global Minima and Local Minima in Depth Understanding

Tutorial 14- Stochastic Gradient Descent with Momentum

Tutorial 15- Adagrad Optimizers in Neural Network

Tutorial 16- AdaDelta and RMSprop optimizer

Deep Learning-All Optimizers In One Video-SGD with Momentum,Adagrad,Adadelta,RMSprop,Adam Optimizers

Session On Different Types Of Loss Function In Deep Learning

Tutorial 17- Create Artificial Neural Network using Weight Initialization Tricks

Keras Tuner Hyperparameter Tuning-How To Select Hidden Layers And Number of Hidden Neurons In ANN

Tutorial 18- Hyper parameter Tuning To Decide Number of Hidden Layers in Neural Network

Tutorial 19- Training Artificial Neural Network using Google Colab GPU

Tutorial 20- Convolution Neural Network vs Human Brain

Tutorial 21- What is Convolution operation in CNN?

Tutorial 22- Padding in Convolutional Neural Network

Tutorial 23- Operation Of CNN(CNN vs ANN)

Tutorial 24- Max Pooling Layer In CNN

Tutorial 25- Data Augmentation In CNN-Deep Learning

Tutorial 26- Create Image Dataset using Data Augmentation using Keras-Deep Learning-Data Science

Tutorial 27- Create CNN Model and Optimize using Keras Tuner- Deep Learning

Tutorial 28- Create CNN Model Using Transfer Learning using Vgg 16, Resnet

Tutorial 29- Why Use Recurrent Neural Network and Its Application

Tutorial 30- Recurrent Neural Network Forward Propogation With Time

Tutorial 31- Back Propagation In Recurrent Neural Network

Tutorial 32- Problems In Simple Recurrent Neural Network

Tutorial 33- Installing Cuda Toolkit And cuDNN For Deep Learning

Tutorial 34- LSTM Recurrent Neural Network In Depth Intuition

Word Embedding - Natural Language Processing| Deep Learning

Implementing Word Embedding Using Keras- NLP | Deep Learning

Develop your Neural Network Like A Google Deep Learning Developer

Kaggle Faker News Classifier Using LSTM- Deep LEarning| Natural Language Processing

Stock Price Prediction And Forecasting Using Stacked LSTM- Deep Learning

Bidirectional RNN Indepth Intuition- Deep Learning Tutorial

Implement Kaggle Fake News Classifier Using Bidirectional LSTM RNN

Sequence To Sequence Learning With Neural Networks| Encoder And Decoder In-depth Intuition

Develop Your First Deep Learning End To End Project As A Beginner In Data Science in 30 minutes

Encoder And Decoder- Neural Machine Learning Language Translation Tutorial With Keras- Deep Learning

Problems With Encoders And Decoders- Indepth Intuition

Live Session- Understanding Attention Models Architecture And Maths Intuition- Deep Learning

Live Session- Encoder Decoder,Attention Models, Transformers, Bert Part 1

Live- Attention Models, Transformers In depth Intuition Deep Learning- Part 2

Live -Transformers Indepth Architecture Understanding- Attention Is All You Need

How To Train Deep Learning Models In Google Colab- Must For Everyone

Alexnet Architecture In-depth-Discussion Along With Code-Deep Learning Advanced CNN

VGGNET Architecture In-depth Discussion Along With Code -Deep Learning Advanced CNN

Hummingbird-Run Traditional Machine Learning model on Deep Neural Network frameworks-Data Science

Lets Implement LSTM RNN Models For Univariate Time Series Forecasting- Deep Learning

TensorDash- How To Monitor Your Deep Learning Model Metrics, Loss, Accuracy Using Mobile App

Handling Imbalanced Dataset Using Cost Sensitive Neural Networks- Credit Card Fraud Detection

500+ Machine Learning And Deep Learning Projects All At One Place

Google Colab Pro Vs Colab Free- Benefits Of Using Colab Pro- How To Access From India

How To Implement Image Classification Using SVM In Convolution Neural Network

Object Localization Vs Object Detection Deep Learning

3000+ Research Datasets For Machine Learning Researchers By Papers With Code

PerceptiLabs-The Best Machine Learning Visual Modeling Tool-Train Deep Learning Neural Network

Face Recognition Attendance Based Project In Machine Learning

Colab Pro Now Available In India, Brazil, France, Thailand,Japan,UK- BOON FOR Data Science Aspirants

Part 1-EDA-Audio Classification Project Using Deep Learning

Part 2-Data Preprocessing-Audio Classification Project Using Deep Learning

Part 3-Model Creation-Audio Classification Project Using Deep Learning

Part 4-Testing ANN Model-Audio Classification Project Using Deep Learning

Gradio Library-Interfaces for your Machine Learning Models

Comparing Transfer Learning Models Using Gradio

TFOD 2.0 Custom Object Detection Step By Step Tutorial

Text Generation with Transformers (GPT-2) In 10 Lines Of Code

Image Segmentation And Object Detection Using 5 Lines Of Code Using PixelLib

Real Time Image Segmentation And Object Detection From Live Video Stream Using PixelLib

Face Detection, Face Mesh, OpenPose, Holisitic, Hand Detection Using MediaPipe On Live Stream Video

GauGAN AI Art Tool By Nvidia- Convert Imagination Into Real Picture- Application Of GAN

Implementation Of Perceptron In Deep Learning Using Python From Scratch- Part 1- Ft: Sunny

Complete Implementation Of Perceptron In Deep Learning Using Python From Scratch

How to Install Ubuntu in Windows 10 with WSL2-Windows Subsystem for Linux

Tutorial on Automated Machine Learning using MLBox

Monte Carlo DropOut Layers In Deep Learning

Description:

Embark on a comprehensive deep learning journey with this extensive 30-hour course. Explore the fundamentals of neural networks, advanced architectures, and practical applications. Learn about popular deep learning concepts, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and attention models. Master essential techniques such as backpropagation, gradient descent, and hyperparameter tuning. Dive into transfer learning, natural language processing, and computer vision tasks. Gain hands-on experience with tools like Keras, TensorFlow, and Google Colab. Develop real-world projects in image classification, object detection, time series forecasting, and audio processing. Understand cutting-edge models like transformers and GPT-2. By the end of this course, acquire the skills to build and optimize deep learning models for various applications in data science and artificial intelligence.

Complete Deep Learning

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#Computer Science #Deep Learning #Machine Learning #Artificial Intelligence #Neural Networks #Convolutional Neural Networks (CNN) #Recurrent Neural Networks (RNN) #Gradient Descent #Hyperparameter Tuning #Activation Functions #Loss Functions