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.
Overview
Syllabus
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.
Taught by
Krish Naik
