AI Sciences

Promo & Highlights

Introduction: Introduction to Instructor and Aisciences

Basics of Deep Learning: Problem to Solve Part 1

Basics of Deep Learning: Problem to Solve Part 2

Basics of Deep Learning: Problem to Solve Part 3

Basics of Deep Learning: Linear Equation

Basics of Deep Learning: Linear Equation Vectorized

Basics of Deep Learning: 3D Feature Space

Basics of Deep Learning: N Dimensional Space

Basics of Deep Learning: Theory of Perceptron

Basics of Deep Learning: Implementing Basic Perceptron

Basics of Deep Learning: Logical Gates for Perceptrons

Basics of Deep Learning: Perceptron Training Part 1

Basics of Deep Learning: Perceptron Training Part 2

Basics of Deep Learning: Learning Rate

Basics of Deep Learning: Perceptron Training Part 3

Basics of Deep Learning: Perceptron Algorithm

Basics of Deep Learning: Coading Perceptron Algo (Data Reading & Visualization)

Basics of Deep Learning: Coading Perceptron Algo (Perceptron Step)

Basics of Deep Learning: Coading Perceptron Algo (Training Perceptron)

Basics of Deep Learning: Coading Perceptron Algo (Visualizing the Results)

Basics of Deep Learning: Problem with Linear Solutions

Basics of Deep Learning: Solution to Problem

Basics of Deep Learning: Error Functions

Basics of Deep Learning: Discrete vs Continuous Error Function

Basics of Deep Learning: Sigmoid Function

Basics of Deep Learning: Multi-Class Problem

Basics of Deep Learning: Problem of Negative Scores

Basics of Deep Learning: Need of Softmax

Basics of Deep Learning: Coding Softmax

Basics of Deep Learning: One Hot Encoding

Basics of Deep Learning: Maximum Likelihood Part 1

Basics of Deep Learning: Maximum Likelihood Part 2

Basics of Deep Learning: Cross Entropy

Basics of Deep Learning: Cross Entropy Formulation

Basics of Deep Learning: Multi Class Cross Entropy

Basics of Deep Learning: Cross Entropy Implementation

Basics of Deep Learning: Sigmoid Function Implementation

Basics of Deep Learning: Output Function Implementation

Deep Learning: Introduction to Gradient Decent

Deep Learning: Convex Functions

Deep Learning: Use of Derivatives

Deep Learning: How Gradient Decent Works

Deep Learning: Gradient Step

Deep Learning: Logistic Regression Algorithm

Deep Learning: Data Visualization and Reading

Deep Learning: Updating Weights in Python

Deep Learning: Implementing Logistic Regression

Deep Learning: Visualization and Results

Deep Learning: Gradient Decent vs Perceptron

Deep Learning: Linear to Non Linear Boundaries

Deep Learning: Combining Probabilities

Deep Learning: Weighted Sums

Deep Learning: Neural Network Architecture

Deep Learning: Layers and DEEP Networks

Deep Learning: Multi Class Classification

Deep Learning: Basics of Feed Forward

Deep Learning: Feed Forward for DEEP Net

Deep Learning: Deep Learning Algo Overview

Deep Learning: Basics of Back Propagation

Deep Learning: Updating Weights

Deep Learning: Chain Rule for BackPropagation

Deep Learning: Sigma Prime

Deep Learning: Data Analysis NN Implementation

Deep Learning: One Hot Encoding (NN Implementation)

Deep Learning: Scaling the Data (NN Implementation)

Deep Learning: Splitting the Data (NN Implementation)

Deep Learning: Helper Functions (NN Implementation)

Deep Learning: Training (NN Implementation)

Deep Learning: Testing (NN Implementation)

Optimizations: Underfitting vs Overfitting

Optimizations: Early Stopping

Optimizations: Quiz

Optimizations: Solution & Regularization

Optimizations: L1 & L2 Regularization

Optimizations: Dropout

Optimizations: Local Minima Problem

Optimizations: Random Restart Solution

Optimizations: Vanishing Gradient Problem

Optimizations: Other Activation Functions

Final Project: Final Project Part 1

Final Project: Final Project Part 2

Final Project: Final Project Part 3

Final Project: Final Project Part 4

Final Project: Final Project Part 5

Introduction: Course Content

Introduction: Benefits of Framework

Introduction: Installations and Setups

Tensor: Introduction to Tensor

Tensor: List vs Array vs Tensor

Tensor: Arithmetic Operations

Tensor: Tensor Operations

Tensor: Auto-Gradiants

Tensor: Activity Solution

Tensor: Detaching Gradients

Tensor: Loading GPU

NN with Tensor: Introduction to Module

NN with Tensor: Basic NN part 1

NN with Tensor: Basic NN part 2

NN with Tensor: Loss Functions

NN with Tensor: Activation Functions & Hidden Layers

NN with Tensor: Optimizers

NN with Tensor: Data Loader & Dataset

NN with Tensor: Activity

NN with Tensor: Activity Solution

NN with Tensor: Formating the Output

NN with Tensor: Graph for Loss

CNN: Introduction to Module

CNN: CNN vs NN

CNN: Introduction to Convolution

CNN: Convolution Animations

CNN: Convolution using Pytorch

CNN: Introduction to Pooling

CNN: Pooling using Numpy

CNN: Pooling in Pytorch

CNN: Introduction to Project

CNN: Project (Data Loading)

CNN: Project (Transforms)

CNN: Project (DataLoaders)

CNN: Project (CNN Architect)

CNN: Project (Forward Propagation)

CNN: Project (Training CNN)

CNN: Project (Analyzing Model Output)

CNN: Project (Making Predictions)

Introduction to TensorFlow: Module Introduction

Introduction to TensorFlow: TensorFlow Definition and Properties

Introduction to TensorFlow: Tensor Types and Tesnor Board

Introduction to TensorFlow: How to use TensorFlow

Introduction to TensorFlow: Google Colab

Introduction to TensorFlow: Exercise

Introduction to TensorFlow: Exercise Solution

Introduction to TensorFlow: Quiz

Introduction to TensorFlow: Quiz Solution

Building your first deep learning Project: Module Introduction

Building your first deep learning Project: ANNs

Building your first deep learning Project: TensorFlow Playground

Building your first deep learning Project: Load TF and Data

Building your first deep learning Project: Model Training and Evaluation

Building your first deep learning Project: Project

Building your first deep learning Project: Project Implementation

Building your first deep learning Project: Quiz

Building your first deep learning Project: Quiz Solution

Multi-layer Deep Learning Project: Module Introduction

Multi-layer Deep Learning Project: Training and Epochs

Multi-layer Deep Learning Project: Gradient Decent and Back Propagation

Multi-layer Deep Learning Project: Bias Variance Trade-Off

Multi-layer Deep Learning Project: Performance Metrics

Multi-layer Deep Learning Project: Project-Sales Predition

Multi-layer Deep Learning Project: Quiz

Multi-layer Deep Learning Project: Quiz Solution