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## Table of contents
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### [1. Introduction to Deep Learning](#)
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### [1. Introduction to Deep Learning](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/tree/master/01.%20Introduction%20to%20Deep%20Learning)
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*[1.1. What is Deep Learning?](#)
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*[1.1. What is Deep Learning?](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/01.%20Introduction%20to%20Deep%20Learning/1.01%20What%20is%20Deep%20Learning%3F.ipynb)
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* 1.2. Biological and Artifical Neurons
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* 1.3. ANN and its Layers
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* 1.4. Exploring Activation Functions
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* 1.5. Forward Propagation in ANN
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* 1.6. How does ANN learn?
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* 1.7. Debugging Gradient Descent with Gradient Checking
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* 1.8. Putting it all together
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*[1.9. Building Neural Network from Scratch](#)
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*[1.9. Building Neural Network from Scratch](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/01.%20Introduction%20to%20Deep%20Learning/1.09%20Building%20Neural%20Network%20from%20scratch.ipynb)
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### [2. Getting to Know TensorFlow](#)
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### [2. Getting to Know TensorFlow](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/tree/master/02.%20Getting%20to%20Know%20TensorFlow)
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* 2.1. What is TensorFlow?
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* 2.2. Understanding Computational Graphs and Sessions
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* 2.3. Variables, Constants, and Placeholders
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* 2.4. Introducing TensorBoard
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*[2.5. Handwritten digits classification using Tensorflow ](#)
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*[2.5. Handwritten digits classification using Tensorflow ](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/02.%20Getting%20to%20Know%20TensorFlow/2.05%20Handwritten%20digits%20classification%20using%20TensorFlow.ipynb)
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* 2.6. Visualizing Computational graph in TensorBord
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* 2.7. Introducing Eager execution
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*[2.8. Math operations in TensorFlow](#)
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*[2.9. Tensorflow 2.0 and Keras](#)
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* 2.10. MNIST digits classification in Tensorflow 2.0
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* 2.11. Should we use Keras or TensorFlow?
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*[2.8. Math operations in TensorFlow](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/02.%20Getting%20to%20Know%20TensorFlow/2.08%20Math%20operations%20in%20TensorFlow.ipynb)
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* 2.9. Tensorflow 2.0 and Keras
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*[2.10. MNIST digits classification in Tensorflow 2.0](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/02.%20Getting%20to%20Know%20TensorFlow/2.10%20MNIST%20digits%20classification%20in%20TensorFlow%202.0.ipynb)
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*[2.11. Should we use Keras or TensorFlow?](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/02.%20Getting%20to%20Know%20TensorFlow/2.11%20Should%20we%20use%20Keras%20or%20TensorFlow%3F.ipynb)
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### [3. Gradient Descent and its variants](#)
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### [3. Gradient Descent and its variants](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/tree/master/03.%20Gradient%20Descent%20and%20its%20variants)
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*[3.1. Demystifying Gradient Descent](#)
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*[3.2. Performing Gradient Descent in Regression](#)
*[3.2. Performing Gradient Descent in Regression](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/03.%20Gradient%20Descent%20and%20its%20variants/3.02%20Performing%20Gradient%20Descent%20in%20Regression.ipynb)
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* 3.3. Gradient Descent vs Stochastic Gradient Descent
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* 3.4. Momentum based Gradient Descent
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* 3.5. Adaptive methods of Gradient Descent
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*[ 3.6. Implementing Various Gradient descent methods from Scratch](#)
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*[ 3.6. Implementing Various Gradient descent methods from Scratch](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/03.%20Gradient%20Descent%20and%20its%20variants/3.06%20%20Implementing%20Several%20Variants%20of%20Gradient%20Descent%20from%20Scratch.ipynb)
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### [4. Generating Song lyrics with RNN](#)
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### [4. Generating Song lyrics with RNN](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/tree/master/04.%20Generating%20Song%20Lyrics%20Using%20RNN)
*[4.6. Generating song lyrics using RNN](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/04.%20Generating%20Song%20Lyrics%20Using%20RNN/4.06%20Generating%20Song%20Lyrics%20Using%20RNN.ipynb)
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* 4.7. Different types of RNN architectures
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### [5. Improvements to the RNN](#)
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### [5. Improvements to the RNN](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/tree/master/05.%20Improvements%20to%20the%20RNN)
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*[5.1. LSTM to the Rescue](#)
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* 5.1. LSTM to the Rescue
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* 5.2. Understanding the LSTM cell
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* 5.3. Forward propagation in LSTM
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* 5.4. Backpropagation in LSTM
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* 5.5. Deriving backpropagation of LSTM Step by step
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*[5.6. Predicting Bitcoins price using LSTM](#)
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*[5.6. Predicting Bitcoins price using LSTM](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/05.%20Improvements%20to%20the%20RNN/5.06%20Predicting%20Bitcoins%20price%20using%20LSTM%20RNN.ipynb)
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* 5.7. Gated Recurrent Units
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* 5.8. Understanding GRU cell
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* 5.9. Forward propagation in GRU cell
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*[ 6.4. Implementing CNN in tensorflow](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/06.%20Demystifying%20Convolutional%20Networks/6.04%20Implementing%20CNN%20in%20TensorFlow.ipynb)
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* 6.5. Different types of CNN architectures
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* 6.6. Capsule networks
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*[6.7. Building capsule networks in Tensorflow](#)
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*[6.7. Building capsule networks in Tensorflow](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/06.%20Demystifying%20Convolutional%20Networks/6.07%20Building%20Capsule%20Networks%20in%20TensorFlow.ipynb)
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### [7. Learning Text Representations](#)
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### [7. Learning Text Representations](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/tree/master/07.%20Learning%20Text%20Representations)
*[7.8. Visualizing word embeddings in TensorBoard](#)
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*[ 7.7. Building word2vec model using Gensim](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/07.%20Learning%20Text%20Representations/7.07%20Building%20word2vec%20model%20using%20Gensim.ipynb)
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*[7.8. Visualizing word embeddings in TensorBoard](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/07.%20Learning%20Text%20Representations/7.08%20Visualizing%20Word%20Embeddings%20in%20TensorBoard.ipynb)
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* 7.9. Converting documents to vectors using doc2vec
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*[7.10. Finding similar documents using Doc2vec](#)
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*[7.10. Finding similar documents using Doc2vec](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/07.%20Learning%20Text%20Representations/7.10%20Finding%20similar%20documents%20using%20Doc2Vec.ipynb)
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* 7.11. Understanding skip thoughts algorithm
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* 7.12 Quick thoughts for sentence embeddings
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### [8. Generating Images using GANs](#)
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### [8. Generating Images using GANs](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/tree/master/08.%20Generating%20Images%20using%20GANs)
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* 8.1. Distinguishing generative and discriminative models
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*[8.1. Distinguishing generative and discriminative models](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/08.%20Generating%20Images%20using%20GANs/8.04%20Demystifying%20GAN%20Loss%20Function.ipynb)
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* 8.2. Say hello to GANs
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* 8.3. Architecture of GANs
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* 8.4. Demystifying GAN loss function
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*[8.5. Generating images using GAN in TensorFlow](#)
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*[8.5. Generating images using GAN in TensorFlow](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/08.%20Generating%20Images%20using%20GANs/8.05%20Generating%20images%20using%20GAN%20in%20TensorFlow.ipynb)
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* 8.6. DCGAN - Adding convolution to the GAN
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*[8.7. Implementing DCGAN to generate CIFAR images](#)
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*[8.7. Implementing DCGAN to generate CIFAR images](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/08.%20Generating%20Images%20using%20GANs/8.07%20Implementing%20DCGAN%20to%20Generate%20CIFAR%20Images.ipynb)
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* 8.8. Least Squares GAN
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*[8.9. Building LSGAN in tensorflow](#)
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*[8.9. Building LSGAN in tensorflow](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/08.%20Generating%20Images%20using%20GANs/8.09%20Building%20LSGAN%20in%20TensorFlow.ipynb)
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* 8.10. WGAN - GANs with Wasserstein distance
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### [9. Learning more about GANs](#)
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### [9. Learning more about GANs](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/tree/master/09.%20Learning%20more%20about%20GANs)
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* 9.1. Conditional GAN
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*[9.2. Generating specific digits using CGAN](#)
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*[9.2. Generating specific digits using CGAN](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/09.%20Learning%20more%20about%20GANs/9.02%20Generating%20Specific%20Handwritten%20Digit%20Using%20CGAN.ipynb)
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* 9.3. Understanding InfoGAN
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* 9.4. Architecture of InfoGAN
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*[9.5. Constructing InfoGAN in tensorflow](#)
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*[9.5. Constructing InfoGAN in tensorflow](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/09.%20Learning%20more%20about%20GANs/9.05%20Constructing%20InfoGan%20in%20Tensorflow.ipynb)
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* 9.6. Translating images using CycleGAN
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*[9.7. Converting photos to paintings using CycleGAN](#)
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*[9.7. Converting photos to paintings using CycleGAN](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/09.%20Learning%20more%20about%20GANs/9.07%20Converting%20photos%20to%20paintings%20using%20CycleGAN.ipynb)
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* 9.8. Text to image synthesis using Stack GAN
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### [10. Reconstructing inputs using Autoencoders](#)
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### [10. Reconstructing inputs using Autoencoders](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/tree/master/10.%20Reconsturcting%20Inputs%20using%20Autoencoders)
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* 10.1. What is Autoencoder?
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* 10.2. Understanding the architecture of autoencoders
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*[10.3. Reconstructing MNIST images using autoencoders](#)
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*[10.3. Reconstructing MNIST images using autoencoders](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/10.%20Reconsturcting%20Inputs%20using%20Autoencoders/10.03%20Reconstructing%20MNIST%20images%20using%20Autoencoder.ipynb)
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* 10.4. Autoencoders with convolution
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*[10.5. Building convolution autoencoder](#)
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*[10.5. Building convolution autoencoder](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/10.%20Reconsturcting%20Inputs%20using%20Autoencoders/10.05%20Building%20Convolutional%20Autoencoder.ipynb)
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* 10.6. Exploring denoising autoencoder
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*[10.7. Denoising images using DAE](#)
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*[10.7. Denoising images using DAE](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/10.%20Reconsturcting%20Inputs%20using%20Autoencoders/10.07%20Denoising%20images%20using%20Denoising%20Autoencoder.ipynb)
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* 10.8. Understanding sparse autoencoders
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*[10.9. Building sparse autoencoders](#)
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*[10.9. Building sparse autoencoders](#https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/10.%20Reconsturcting%20Inputs%20using%20Autoencoders/10.09%20Building%20the%20Sparse%20Autoencoder.ipynb
*[10.13. Generating images using VAE](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/10.%20Reconsturcting%20Inputs%20using%20Autoencoders/10.13%20Generating%20images%20using%20VAE.ipynb)
*[11.1. What is few-shot learning?](https://github.com/sudharsan13296/Hands-On-Deep-Learning-Algorithms-with-Python/blob/master/11.%20Exploring%20Few%20Shot%20Learning%20Algorithms/11.01%20What%20is%20few-shot%20learning%3F.ipynb)
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