Neural Networks

Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide

Build better PyTorch models with TensorBoard visualization

Learn the implementation of both ANN & DNN using the H2O package Of R programming language

Increase the performance of various neural network architectures using NEAT, HyperNEAT, ES-HyperNEAT, Novelty Search, SAFE, and Deep Neuroevolution.

A comprehensive guide to developing neural network-based solutions using TensorFlow 2.0

Build and train Deep Learning neural networks with TensorFlow 2.0

Design and create neural networks with deep learning and artificial intelligence principles using OpenAI Gym, TensorFlow, and Keras

Develop generative models for a variety of real-world use-cases and deploy them to production

Implement neural network architectures by building them from scratch for multiple real-world applications.

Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python

Create and unleash the power of neural networks using TensorFlow and deep learning concepts in a hands-on style

Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network architectures for deep learning with Python's most popular TensorFlow framework.

Build and train neural network models to solve complex problems

Tackle all CNN-related queries with this fast-paced guide

Explore high-level concepts such as neural networks, CNN and RNN using TensorFlow.